artificial intelligence education ethical problems and solutions

Artificial Intelligence Education Ethical Problems and Solutions

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Artificial Intelligence Education Ethical Problems and Solutions

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... [10] Li Sijing, Wang Lan, Artificial Intelligence Education Ethical Problems and Solutions, Proceedings ...

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The provided paper does not specifically mention the ethical considerations of using artificial intelligence in science education.

The challenges of artificial intelligence in evaluating students include algorithmic irrationality, incomplete data, and inaccurate content.

The problems in student's academic performance using artificial intelligence include algorithmic irrationality, incomplete data, and inaccurate content.

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Artificial Intelligence (AI) in Education

• Background Information

AI and Ethics in Education

Chatgpt and free labor, assignments and privacy policies.

• Academic integrity, syllabi statements, & AI
• Class assignments and AI
• How to cite AI

The ethical and societal implications of AI, especially in education, are numerous and complex. Below are some issues to consider:

Accessibility and Equity : On the one hand, AI can help make education more accessible and personalized, enabling students to learn at their own pace and providing teachers with tools to identify areas where students are struggling. It could also create opportunities for students in remote areas or those who cannot attend school due to health issues or disabilities. However, on the other hand, not all students and schools have equal access to the technology and infrastructure needed for AI-based education. This digital divide can exacerbate educational inequalities.

Data Privacy and Security : AI systems in education often rely on collecting and analyzing large amounts of data about students. This raises questions about how this data is stored, who has access to it, and how it is used. There are risks of breaches of privacy and potential misuse of data.

Bias and Fairness : Like all AI systems, educational AI can be subject to bias, depending on how it's trained and what data it's trained on. For instance, an AI tutoring system could potentially favor certain types of students over others, based on the data it was trained with. This could perpetuate existing biases and inequalities.

Teacher-Student Relationship : While AI can automate some tasks, it cannot replace the human interaction and emotional support provided by teachers. There are concerns that over-reliance on AI could erode the teacher-student relationship and the social skills students develop in the classroom.

Skill Development : As AI and automation become increasingly integrated into the workforce, there is a need to ensure education systems are adequately preparing students with the skills they will need for the future. This includes not just technical skills for working with AI, but also soft skills like critical thinking and creativity that AI is currently not able to replicate.

Transparency and Understanding : It's important for students, parents, and educators to understand how AI tools make decisions. Unfortunately, many AI systems are "black boxes" where the decision-making process is opaque. This can make it hard to trust the system or to challenge its decisions.

Regulation and Policy : Given all these issues, there is a clear need for regulation and policies to guide the use of AI in education. These regulations need to address issues like data privacy, transparency, and equity. However, policy-making in this area is complex and needs to strike a balance between protecting students and enabling innovation.

All these issues underline the need for an interdisciplinary approach to AI in education, incorporating not just technological expertise, but also input from educators, psychologists, sociologists, ethicists, and legal experts.

Asking students to use ChatGPT provides free labor to OpenAI. ChatGPT is in its infancy and has been released as a free research preview (OpenAI, 2022). It will continue to become a more intelligent form of artificial intelligence… with the help of users who provide feedback to the responses it generates. 

Consider: Do you really want to ask your students to help train an AI tool as part of their education? 

A  blog post from Autumm Caines  (2022), Instructional Designer at the University of Michigan – Dearborn, outlines a few tips to mitigate this free labor, including:

• Not asking students to create ChatGPT accounts and instead doing instructor demos;
• Encouraging students to use burner email accounts (to reduce personal data collection) if they choose to use the tool;
• Using one shared class login.

Caines includes some interesting thoughts on students working themselves out of future jobs by using ChatGPT. We currently cannot find research to support this.

This information is from " ChatGPT & Education " by  Torrey Trust , Ph.D., and is licensed under  CC BY NC 4.0 .

Before assigning students to work on projects involving AI chatbots, make sure to review the privacy policy of the tool(s) you've selected. Also consider what benefit you may be providing the developer by requiring your students to conduct free labor to improve the tool's algorithm.

OpenAI (the company that designed ChatGPT) collects a lot of data from ChatGPT users. 

The  privacy policy  states that this data can be shared with third-party vendors, law enforcement, affiliates, and other users.

Do NOT provide a student’s full name and associated class grade to ChatGPT to write emails, this is a potential FERPA violation (in the United States) for sharing a student’s educational record (with OpenAI) without their permission. See more about FERPA at JMU.

This tool should not be used by children under 13 (data collection from children under 13 violates the  Children’s Online Privacy Protection Rule - COPPA ).

While you can request to have your  ChatGPT account deleted , the prompts that you input into ChatGPT cannot be deleted. If you, or your students, were to ask ChatGPT about sensitive or controversial topics, this data cannot be removed. 

TIP: Before asking your students to use ChatGPT (if you plan to do so), please read over the  privacy policy  with them and allow them to opt out if they do not feel comfortable having their data collected and shared as outlined in the policy.

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• Last Updated: Jan 29, 2024 8:08 AM
• URL: https://guides.lib.jmu.edu/AI-in-education

Addressing Ethical Problems and Finding Solutions in Artificial Intelligence Education

• Post author By aqua
• Post date 03.12.2023
• No Comments on Addressing Ethical Problems and Finding Solutions in Artificial Intelligence Education

In recent years, the rapid advancement of artificial intelligence (AI) has brought about significant changes in various sectors, including education. AI has the potential to revolutionize the way we learn, making education more accessible and personalized. However, along with the benefits, there are also ethical concerns that need to be addressed.

One of the main ethical problems in AI education is the issue of privacy. With the use of AI in the classroom, there is a vast amount of data being collected on students, including their personal information, learning patterns, and even emotions. This raises concerns about how this data is being used and shared, and whether it is being adequately protected. It is crucial to establish clear guidelines and regulations to ensure that students’ privacy rights are respected.

Another ethical concern is the potential for bias in AI algorithms. AI systems are trained on large datasets, which can contain biases and prejudices from the real world. If these biases are not addressed, they can perpetuate discrimination and inequality in education. It is essential for educators and developers to carefully review and test AI systems to identify and eliminate any biases that may be present.

Moreover, there are ethical questions surrounding the automation of tasks traditionally performed by educators. While AI can assist in grading assignments or providing personalized feedback, there is a concern that relying too heavily on AI may diminish the role of human teachers. The ethical implications of this shift need to be considered, ensuring that AI is used as a tool to enhance education and not replace human interaction and empathy.

In conclusion, as AI continues to reshape the field of education, it is crucial to address the ethical concerns that arise. Privacy protection, bias prevention, and the appropriate use of AI as a tool are key considerations in ensuring that AI education is fair, inclusive, and beneficial for all learners.

Ethical Concerns in Artificial Intelligence Education

As artificial intelligence (AI) becomes more prevalent in education, it is important to address the ethical concerns that arise from its integration into learning environments. AI technologies offer numerous opportunities to enhance educational experiences, but they also pose potential problems that need to be carefully considered and mitigated.

1. Transparency and Bias

One major ethical concern in AI education is the lack of transparency in how AI algorithms make decisions and recommendations. This opacity can lead to biased outcomes that perpetuate existing inequalities and reinforce stereotypes. It is crucial for AI systems used in education to be transparent, explainable, and free from bias, ensuring that all students are treated equitably and fairly.

2. Data Privacy and Security

Artificial intelligence relies heavily on data collection and analysis to understand student behavior and personalize learning experiences. However, this raises significant concerns about data privacy and security. Educational institutions must ensure that student data is protected, and that AI systems adhere to strict privacy regulations. Additionally, data should be used solely for educational purposes and should not be shared with third parties without explicit consent.

In order to address these ethical concerns in AI education, several solutions can be implemented. First, educational institutions should prioritize the use of transparent and explainable AI algorithms, allowing students and educators to understand how decisions are made. This transparency can help identify and correct any biases that may arise.

Second, strict data privacy and security protocols must be established and adhered to. This includes implementing secure data storage practices, obtaining informed consent from students and parents, and regularly auditing AI systems to ensure compliance with privacy regulations.

Ultimately, the integration of AI into education has immense potential to improve learning experiences and outcomes. However, it is crucial to address the ethical concerns associated with its use to ensure that AI technologies are used responsibly and ethically, promoting inclusivity, fairness, and privacy in education.

Privacy Implications

The advancement in artificial intelligence has brought about numerous benefits and possibilities for various industries and sectors. However, this progress also brings along ethical concerns and privacy implications that need to be addressed.

As artificial intelligence increasingly collects and analyzes vast amounts of data, there are potential privacy issues that arise. The intelligent systems can inadvertently or intentionally access and process personal information without the individuals’ consent or knowledge. This can result in a violation of privacy and raise concerns about unauthorized access and use of personal data.

• Data Breaches: With the increased reliance on artificial intelligence systems, the risk of data breaches and cyber-attacks becomes more significant. These breaches could lead to the exposure of sensitive personal information and result in financial losses, identity theft, and other harmful consequences.
• Algorithmic Bias: Artificial intelligence systems often rely on algorithms trained on historical data, which can perpetuate and amplify existing biases. This can result in discriminatory outcomes and unfairly impact individuals or groups based on race, gender, or other attributes.
• Lack of Transparency: Another concern is the lack of transparency regarding how artificial intelligence systems collect, store, and use personal data. It can be difficult for individuals to understand what information is being collected about them, how it is being used, and who has access to it.
• Privacy by Design: Implementing privacy principles and practices from the beginning can help mitigate potential privacy issues. Artificial intelligence systems should be designed with privacy as a core consideration, ensuring that data collection and processing are done in a transparent and secure manner.
• Data Minimization: Collecting only the necessary data to perform the intended tasks can help minimize privacy risks. It is essential to assess the data requirements and ensure that any data collected is relevant, limited, and properly protected.
• Privacy Policies and User Consent: Clear and comprehensive privacy policies should be provided to users, outlining how their data will be collected, stored, and used. Obtaining informed consent from users before collecting their data is crucial to ensure transparency and respect for their privacy rights.

Addressing the privacy implications of artificial intelligence is an ethical imperative. By implementing privacy-focused practices and solutions, we can ensure that the benefits of artificial intelligence are realized without compromising individuals’ privacy rights.

Bias and Discrimination

One of the ethical concerns surrounding artificial intelligence (AI) education is the issue of bias and discrimination. As AI algorithms are developed and trained, there is a risk of biases being introduced into the system. These biases can arise from the datasets used to train the AI models, which may contain biased or discriminatory information.

Discrimination in AI systems can have serious consequences, as it can perpetuate existing societal inequalities and reinforce unfair biases. For example, if an AI system used in education is biased against certain groups, it could result in unequal opportunities for students, further exacerbating social disparities.

To address these problems, it is crucial to develop AI education solutions that are aware of and actively mitigate bias and discrimination. This can be done through various approaches, such as:

1. Diverse and Representative Datasets:

Ensuring that the datasets used to train AI models are diverse and representative of the population is essential to mitigate bias. By including data from various demographics, cultures, and backgrounds, AI systems can better understand and respond to the needs of different individuals and communities. This helps to prevent discriminatory outcomes by reducing the likelihood of biased training data.

2. Transparent Algorithms:

Transparency in AI algorithms is key to addressing biases and discrimination. Making the decision-making processes of the AI system transparent allows for scrutiny and identification of potential biases. This transparency also enables stakeholders, such as educators and policymakers, to hold AI systems accountable for any discriminatory outcomes and take action to rectify them.

Overall, addressing bias and discrimination in AI education is an ethical imperative. By incorporating solutions that mitigate bias, such as diverse datasets and transparent algorithms, we can ensure that AI education promotes fairness, equality, and inclusivity for all learners.

Data Security Risks

Data security is a critical aspect to consider when it comes to artificial intelligence (AI) education. As AI becomes more integrated into educational systems, there are potential problems and risks associated with the management and protection of data.

One of the major concerns is the unauthorized access to personal and sensitive information. Educational institutions gather and store vast amounts of data, including student records, grades, and attendance. If this data falls into the wrong hands, it can lead to identity theft, fraud, and other malicious activities.

Education Challenges

Another challenge is the lack of awareness and education on data security among educators. Many educators may not be well-informed about the best practices and protocols for data protection. There is a need for training and professional development programs to educate educators about the importance of data security and how to implement preventive measures.

Furthermore, there is also a need for standardized policies and guidelines regarding data security in AI education. Educational institutions should have clear protocols and procedures in place to ensure the safe collection, storage, and use of data. This includes implementing encryption, access controls, and regular data backups.

To address these data security risks in AI education, collaboration and partnerships are essential. Educational institutions should work closely with cybersecurity experts and professionals to develop robust data security plans. This can involve conducting regular audits and vulnerability assessments to identify and address any potential security gaps.

Additionally, incorporating data security as part of the AI education curriculum can help raise awareness among students. Teaching students about the importance of data protection and ethical practices can empower them to be responsible users and developers of AI technology.

In conclusion, data security risks are a critical concern in AI education. By prioritizing data protection, implementing preventive measures, and raising awareness among educators and students, we can ensure a safer and more secure AI education environment.

Accountability and Responsibility

As artificial intelligence continues to advance and become more prevalent in our society, it is crucial to address the ethical concerns that arise with this technology. One of the key concerns is accountability and responsibility for the actions and decisions made by AI systems.

Artificial intelligence is designed to replicate human intelligence and make decisions on its own. While this can lead to innovative and efficient solutions to complex problems, it also raises ethical issues. If an AI system makes a mistake or causes harm, who should be held accountable?

There is no easy answer to this question, as it requires a careful balancing of various factors. On one hand, the developers and programmers who create the AI system should take responsibility for its behavior. They are the ones who design and train the system, and they have the power to shape its decision-making process.

On the other hand, there is also a need for accountability and responsibility from the users of AI systems. If users are aware of the potential risks and ethical problems associated with AI, they should be held responsible for how they use and deploy these systems. It is important for users to understand the limitations and biases of AI algorithms, and to use them in a responsible and ethical manner.

In addition, there is a role for regulators and policymakers in ensuring accountability and responsibility in AI. They can establish guidelines and regulations that govern the development and use of AI systems, and hold both developers and users accountable for any harm caused by these systems.

Overall, accountability and responsibility are crucial when it comes to artificial intelligence. It is important for all stakeholders – developers, users, and regulators – to work together to address the ethical problems and ensure that AI is used in a responsible and ethical manner.

Transparency and Trust

One of the biggest ethical concerns surrounding artificial intelligence in education is the issue of transparency. Many educational institutions are now using AI technologies to automate various tasks and processes, such as grading exams or personalizing learning experiences for students. However, this reliance on AI raises questions about how transparent these systems are.

Transparency refers to the ability to understand how and why AI systems are making certain decisions. In the context of education, this means ensuring that algorithms used to assess student performance or make recommendations are fair, unbiased, and based on accurate and reliable data. Without transparency, there is a risk of perpetuating inequalities, reinforcing biases, or making incorrect judgments.

Building trust in AI systems requires transparency. Students, teachers, and parents need to have confidence that the AI tools used in education are reliable, unbiased, and accountable. This can be achieved through clear communication and disclosure of how AI systems work, what data they use, and how decisions are made. Additionally, making the inner workings of AI algorithms accessible and understandable to stakeholders helps foster trust and ensures that the use of AI in education is perceived as ethical and beneficial.

Ensuring transparency and trust in AI education requires collaborative efforts from various stakeholders. Educational institutions, AI developers, policymakers, and researchers need to work together to establish best practices, guidelines, and regulations for the use of AI in education. This includes addressing issues such as privacy, data security, algorithmic fairness, and accountability.

By prioritizing transparency and trust, we can address ethical concerns and ensure that AI technologies in education are used in responsible and ethical ways. Developing transparent AI solutions and fostering trust among stakeholders is crucial for the successful integration of artificial intelligence into the education system.

Intellectual Property Rights

As artificial intelligence technology becomes more prevalent in education, it is crucial to consider the intellectual property rights involved. The development and use of AI solutions in education often require the creation of new algorithms, software, and content. These intellectual creations must be protected to ensure that the creators are credited and rewarded for their work.

Protecting AI Solutions

When implementing AI in education, it is important to establish clear ownership and protection mechanisms for the solutions developed. This includes patenting algorithms and software, copyrighting educational content, and trademarking brand names associated with AI-based products and services.

By protecting AI solutions, creators and developers are incentivized to continue innovating and contributing to the field. Intellectual property rights create a framework for fair competition and encourage investment in research and development, leading to improved educational outcomes and advancements in artificial intelligence technology.

Ethical Considerations

While intellectual property rights are important for fostering innovation, it is also crucial to consider the ethical implications of these rights. Access to education should be equitable and affordable for all, and strict intellectual property rights can sometimes hinder this goal.

One possible approach is to balance intellectual property rights with open-source initiatives in AI education. By sharing algorithms, software, and educational content, the barriers to entry for educators and learners can be lowered. This promotes collaboration and encourages the development of standardized and accessible AI solutions in education.

In addition, ethical guidelines can be established to ensure that AI solutions are used responsibly and in the best interest of students. These guidelines can address issues such as privacy, data protection, and bias within AI algorithms. By incorporating ethical considerations into AI education, we can create a framework that balances intellectual property rights with the welfare of students.

In conclusion, when integrating artificial intelligence into education, it is essential to consider intellectual property rights. These rights protect the creators and incentivize innovation, but ethical considerations must also be taken into account to ensure equitable access to education and responsible use of AI solutions in the classroom.

Emotional and Psychological Impact

As artificial intelligence (AI) continues to make significant advancements in education, there are ethical concerns surrounding its potential emotional and psychological impact on learners. While AI has the potential to enhance educational experiences and improve learning outcomes, it also brings forth a set of challenges that need to be addressed.

Ethical Problems

One of the main concerns is the risk of emotional manipulation by AI. As AI systems become more sophisticated, they have the ability to tailor content and experiences to each learner’s emotional state. While this may be beneficial in some cases, it raises questions about privacy, consent, and the potential for exploitation. Learners should have control over their emotional well-being and should not be manipulated for the sake of educational gains.

Another ethical problem is the potential for AI to reinforce biases and stereotypes. AI algorithms learn from existing data, which may contain biases from the real world. If these biases are not addressed, AI systems can perpetuate discrimination and inequality, impacting learners’ emotional well-being. It is crucial to ensure that AI systems are unbiased and promote inclusivity in education.

To address the ethical concerns surrounding the emotional and psychological impact of AI in education, several solutions can be implemented:

• Transparency and accountability: AI systems should be transparent about their decision-making process and biases. Developers should be held accountable for any potential harm caused by the system.
• Ethical guidelines: Establishing clear ethical guidelines for AI in education can help mitigate potential risks and ensure responsible deployment. These guidelines should address issues such as privacy, consent, and bias mitigation.
• Education and awareness: Promoting education and awareness about the ethical implications of AI in education can empower learners, educators, and policymakers to make informed decisions. It can also foster a culture of responsible AI use.
• Diversity in AI development: Increasing diversity among AI developers and researchers can bring different perspectives and help address biases and inequalities in AI systems.

By implementing these solutions, it is possible to mitigate the potential emotional and psychological impact of AI in education and ensure that learners’ well-being is prioritized. Ethical considerations should remain at the forefront of AI development and implementation to create a safe and inclusive learning environment.

Social and Economic Inequality

Social and economic inequality is a significant problem that affects societies worldwide. It refers to the unequal distribution of wealth, resources, opportunities, and privileges among individuals and groups within a society. This disparity can lead to various social and economic issues, such as poverty, limited access to education and healthcare, and lack of social mobility.

In the context of artificial intelligence (AI) education, social and economic inequality can pose ethical concerns. AI education may exacerbate existing inequalities if not carefully addressed. For example, limited access to AI education opportunities can widen the gap between individuals who can afford quality AI education and those who cannot.

Addressing social and economic inequality in AI education requires comprehensive solutions. One approach is to promote equal access to AI education resources and opportunities. This can be achieved by making AI education more affordable and accessible, especially for underprivileged communities. Additionally, providing scholarships and grants to individuals from marginalized backgrounds can help bridge the gap.

Another solution is to ensure diverse representation within the AI education field. By promoting diversity and inclusion, AI education programs can address the needs and concerns of individuals from different social and economic backgrounds. This can help create a more equitable AI ecosystem and minimize the risks of perpetuating social and economic inequalities.

Furthermore, integrating ethics and social responsibility into AI education can help foster a sense of ethical awareness among AI practitioners. By teaching the ethical implications of AI development and deployment, students can understand the potential impact of their work on society. This approach can encourage responsible and ethical AI practices, leading to a more equitable and inclusive AI ecosystem.

By implementing these solutions, society can work towards reducing social and economic inequality in the field of AI education. This will not only benefit individuals and communities that have been historically marginalized but also contribute to the development of a more ethical and inclusive AI ecosystem.

Cultural and Ethical Sensitivity

Addressing ethical concerns in artificial intelligence education requires a strong emphasis on cultural and ethical sensitivity. As AI becomes more integrated into our lives, it is important to ensure that students are aware of the ethical implications of the technology they are using and creating. This includes understanding how AI can perpetuate cultural biases and discrimination, and developing strategies to mitigate these issues.

One solution is to incorporate diverse perspectives and voices into AI education. By including a variety of cultural, ethnic, and gender perspectives, students can gain a broader understanding of the ethical implications of AI and its potential impact on different communities. This can help foster a more inclusive and equitable approach to AI development and ensure that all voices are heard and considered.

Additionally, teaching students about the cultural and historical context of AI can help them develop a deeper understanding of the ethical concerns surrounding the technology. By examining the ways in which AI has been used in the past, both positively and negatively, students can gain a greater appreciation for the potential consequences of their own work. This can help them make more informed ethical decisions and develop a sense of responsibility and accountability for the impact of their AI creations.

Finally, incorporating ethics education into AI curriculum is essential for ensuring that students are equipped with the necessary knowledge and skills to navigate complex ethical issues. This can include teaching students about ethical frameworks and principles, such as fairness, transparency, and privacy, and providing them with opportunities to apply these principles to real-world AI scenarios. By integrating ethics into AI education, students can develop the critical thinking and ethical reasoning skills needed to address the ethical concerns that arise in AI development and deployment.

In conclusion, cultural and ethical sensitivity is crucial in addressing ethical concerns in AI education. By incorporating diverse perspectives, teaching students about the cultural and historical context of AI, and integrating ethics education into the curriculum, we can prepare students to navigate the ethical challenges of artificial intelligence with awareness, empathy, and informed decision-making.

Strategies for Addressing Ethical Concerns

Educating individuals about the potential ethical problems arising from artificial intelligence is an essential step in addressing these concerns. By providing education on the ethical implications of AI, individuals can make better-informed decisions regarding the development and use of artificial intelligence technologies.

One strategy is to incorporate ethical considerations into AI education programs. This can be done by integrating ethical discussions and case studies into AI courses and providing students with opportunities to reflect on the ethical implications of their work.

Another solution is to promote interdisciplinary collaboration in AI education. Bringing together experts from various fields, such as philosophy, ethics, and computer science, can help in addressing the complex ethical challenges posed by artificial intelligence. This interdisciplinary approach encourages a holistic understanding of the ethical concerns surrounding AI.

Furthermore, creating guidelines and ethical frameworks for AI development and use can help in mitigating ethical concerns. These frameworks can serve as a reference point for developers and stakeholders, guiding them in making ethical decisions and ensuring that AI technologies are developed and deployed responsibly.

Regular ethical audits and assessments of AI systems can also be valuable in addressing ethical concerns. These audits can help identify any unintended biases, privacy concerns, or other ethical issues that may arise from the use of AI technologies. By conducting regular ethical assessments, organizations can continuously monitor and improve the ethical performance of their AI systems.

In conclusion, addressing ethical concerns in artificial intelligence education requires a multi-faceted approach. By incorporating ethical considerations into education programs, promoting interdisciplinary collaboration, creating ethical frameworks, and conducting regular audits, we can work towards the responsible development and use of AI technologies.

Education and Awareness

Addressing ethical concerns in artificial intelligence (AI) education requires a comprehensive approach that emphasizes the importance of education and awareness. As AI becomes more prevalent in society, it is crucial to educate individuals about the potential problems and challenges associated with this technology.

First and foremost, education plays a key role in helping people understand the ethical implications of AI. By providing individuals with knowledge about the capabilities and limitations of AI, they can make informed decisions about its use. This includes understanding the potential biases and discrimination that can arise from AI algorithms, as well as the impact on privacy and security.

Furthermore, awareness is essential in ensuring that individuals are conscious of the ethical considerations surrounding AI. This involves promoting discussions and debates about the ethical dilemmas AI presents, encouraging critical thinking and evaluating the societal impact of AI technologies. By increasing awareness, individuals can develop a sense of responsibility and actively contribute to the development of ethical solutions.

Education and awareness also play a crucial role in addressing the potential risks and challenges associated with AI. By teaching individuals about the ethical principles that should guide the development and use of AI, they can actively participate in shaping policies and frameworks that promote ethical practices. This includes promoting transparency and accountability in AI algorithms, as well as ensuring fairness and inclusivity in the use of AI technologies.

In conclusion, education and awareness are essential in addressing the ethical concerns surrounding artificial intelligence. By providing individuals with knowledge and fostering awareness, we can promote responsible and ethical practices in the development and use of AI. Through education and awareness, we can work towards creating a society that benefits from the advancements in AI while minimizing the potential risks and harms.

Ethical Guidelines and Frameworks

Addressing ethical concerns in artificial intelligence education requires the development of ethical guidelines and frameworks. These guidelines and frameworks serve as solutions to the various ethical problems that can arise in the field of artificial intelligence education.

One approach to creating ethical guidelines is by considering the potential impact of artificial intelligence in education on individual privacy. Privacy concerns are one of the most significant ethical issues in the field, as the collection and analysis of personal data can have far-reaching consequences. Therefore, ethical frameworks should prioritize the protection of individual privacy rights.

Transparency and Accountability

Another essential aspect of ethical guidelines in artificial intelligence education is transparency and accountability. It is crucial that the algorithms and decision-making processes used in AI systems are explainable and that there is accountability for their actions. This ensures that the solutions provided by artificial intelligence in education are fair, unbiased, and do not perpetuate discrimination or inequality.

Collaboration and Multidisciplinary Approach

Developing effective ethical guidelines and frameworks requires collaboration between experts in various fields. Ethical considerations in AI education involve not just computer scientists and educators but also ethicists, psychologists, and policy experts. A multidisciplinary approach ensures that the ethical guidelines consider a wide range of perspectives and avoid potential biases.

By establishing robust ethical guidelines and frameworks, artificial intelligence education can address ethical concerns and contribute to the development of responsible and beneficial AI systems.

Collaborative Decision Making

Ethical concerns in artificial intelligence (AI) education extend beyond the development of AI solutions and into the decision-making processes used in the educational context. Collaborative decision making is a crucial aspect of AI education that aims to ensure that ethical considerations are taken into account when developing and implementing AI technologies in educational settings.

The Importance of Collaboration

Collaborative decision making involves bringing together stakeholders from different backgrounds, including educators, AI experts, students, and policymakers, to collectively make decisions that impact AI education. By involving multiple perspectives and expertise, collaborative decision making helps to identify and address ethical concerns that may arise during the development and implementation of AI technologies.

Through collaborative decision making, the potential biases and limitations of AI technologies can be identified and mitigated. This ensures that AI solutions used in education are fair, transparent, and respectful of privacy, providing a more equitable learning experience for all students.

Using Data and Evidence

Collaborative decision making in AI education relies on the use of data and evidence to inform the decision-making process. By collecting and analyzing data related to the impact of AI technologies in educational settings, informed decisions can be made to address ethical concerns.

For example, data on the performance of AI-powered educational tools can be analyzed to identify any biases or discriminatory patterns that may exist. This information can then be used to inform decisions on how to modify or improve the AI technologies to eliminate these biases and ensure equitable educational opportunities for all students.

Furthermore, evidence-based decision making helps to build trust and accountability within the AI education community. By using data and evidence, decisions can be made in a transparent and objective manner, ensuring that ethical concerns are not overlooked or ignored during the development and implementation of AI technologies.

Collaboration and Ethical AI Education Solutions

Collaborative decision making is essential for addressing ethical concerns in artificial intelligence education. By bringing together diverse stakeholders, using data and evidence, and promoting transparency and accountability, collaborative decision making ensures that AI solutions used in education are ethically sound.

Through collaborative efforts, ethical AI education solutions can be developed and implemented, benefiting both educators and students. By prioritizing transparency, fairness, and equity, AI technologies can support inclusive and effective learning environments, providing students with the necessary skills to thrive in a world increasingly shaped by artificial intelligence.

Overall, collaborative decision making plays a vital role in addressing ethical concerns in artificial intelligence education and is essential for ensuring the responsible and ethical use of AI technologies in educational settings.

Ethical Impact Assessments

Ethical impact assessments are a crucial aspect of artificial intelligence education. As AI becomes more prevalent in our society, it is important to address the ethical concerns that arise from its implementation.

These assessments involve evaluating the ethical implications of AI systems and solutions. They aim to identify potential problems and develop strategies to mitigate any negative impact they may have on individuals, communities, and society as a whole.

Identifying Ethical Problems

One key aspect of ethical impact assessments is identifying potential ethical problems that may arise from the use of AI in education. This includes identifying biases in the data used to train AI systems, as well as the potential for discrimination or privacy violations.

It is important to consider the ethical implications of using AI to make decisions about student performance, such as grading or predicting future outcomes. Ethical impact assessments can help identify potential issues and develop solutions to ensure fairness and accountability.

Developing Ethical Solutions

Once potential ethical problems have been identified, the next step is to develop ethical solutions. This may involve implementing safeguards and guidelines to prevent biases or discrimination, or creating transparent and explainable AI systems to ensure accountability.

Ethical impact assessments can also help in designing AI education systems that promote inclusivity and accessibility, ensuring that all students, regardless of their background or abilities, have equal access to education.

By conducting ethical impact assessments, we can address the ethical concerns associated with AI in education and develop solutions that promote fairness, accountability, and inclusivity. This will help ensure that AI technologies are used responsibly and ethically in the field of education.

Regulation and Policy Development

In order to address the ethical problems surrounding artificial intelligence education, it is imperative to implement regulation and policy development. With the rapid advancements in technology and the integration of AI into various aspects of education, there is a need for guidelines and rules to ensure the responsible use of this technology.

Ethical concerns can arise in areas such as data privacy, algorithmic biases, and the impact of AI on the job market. It is important for governments and educational institutions to work together to establish regulations that protect the rights and privacy of students and ensure fairness in the use of AI algorithms.

Regulation and policy development in AI education should also focus on promoting transparency and accountability. Educators should be transparent about the use of AI in their teaching methods, and decisions made by AI systems should be explainable and auditable. This can help to build trust among students, parents, and the wider community.

Furthermore, regulation and policy development should address the need for ongoing education and training for educators. AI technology is continually evolving, and educators need to be equipped with the knowledge and skills to effectively and ethically use AI in the classroom. This can include training on topics such as bias detection and mitigation, as well as understanding the potential limitations and risks of AI systems.

In conclusion, regulations and policies play a crucial role in addressing the ethical concerns surrounding artificial intelligence education. By establishing guidelines and rules, governments and educational institutions can ensure the responsible and ethical use of AI in education, while also promoting transparency, accountability, and ongoing education for educators.

Importance of Ethical AI Education

Artificial intelligence (AI) has the potential to revolutionize many aspects of society, from healthcare to transportation to entertainment. However, as we develop and deploy AI solutions, we must also address the ethical concerns that arise from this technology. An essential aspect of this process is ensuring that individuals are well-educated in ethical AI practices.

Addressing Ethical Problems

AI systems are designed to make decisions and process vast amounts of data, often without human intervention. This autonomy raises ethical concerns, as AI can potentially make biased or discriminatory decisions. The lack of transparency in AI algorithms makes it even more challenging to hold them accountable for their outputs. By educating individuals in ethical AI practices, we can address these issues and develop solutions that are fair, transparent, and unbiased.

Promoting Responsible Use of AI

Another vital aspect of ethical AI education is promoting responsible use of AI technology. As AI becomes more prominent in our daily lives, it is crucial to ensure that individuals understand the ethical implications of its use. This includes recognizing biases in data, considering the potential impact on individuals and communities, and being aware of the limitations and risks of AI systems. By educating individuals in these areas, we can foster a culture of responsible and ethical AI use.

Ensuring Inclusivity and Accessibility

Ethical AI education is also essential for ensuring inclusivity and accessibility. By understanding the biases and potential discrimination in AI systems, individuals can work towards creating solutions that are accessible to all users, regardless of their background or abilities. Additionally, ethical AI education can help individuals recognize and challenge biases, ensuring that AI systems do not perpetuate existing inequalities and instead promote fairness and equal opportunities.

In conclusion, the importance of ethical AI education cannot be overstated. It plays a vital role in addressing ethical concerns, promoting responsible use of AI, and ensuring inclusivity and accessibility. As AI continues to advance, it is essential that individuals are equipped with the knowledge and skills to navigate the ethical challenges it presents. Ethical AI education is not only necessary but also critical for crafting a future where AI benefits society as a whole.

Ensuring Fair and Just AI Systems

As artificial intelligence (AI) systems become increasingly integrated into various aspects of our lives, it is crucial to address the ethical concerns that arise with their use in education. One of the primary concerns is ensuring the fairness and justice of these AI systems.

AI systems have the potential to exacerbate existing problems and biases in education. For example, if an AI algorithm is trained on a dataset that contains biased information, it can perpetuate those biases in its decision-making process. This can lead to unequal opportunities and outcomes for students, particularly those from marginalized groups.

To ensure fair and just AI systems in education, it is essential to actively address and mitigate biases in the development and deployment of AI technologies. This involves carefully selecting and curating datasets that are diverse, representative, and free from biases. Additionally, transparency in the algorithms used and the decision-making process of AI systems is crucial. Educators and developers should be able to understand and explain how these systems arrive at their recommendations or decisions.

Another important aspect of ensuring fairness is incorporating ethical considerations into the training and education of AI practitioners. By promoting awareness of potential biases and ethical challenges in AI development, future professionals in the field can better design and deploy AI systems that are fair and just.

Moreover, ongoing monitoring and evaluation of AI systems is necessary to identify and address any biases or discriminatory outcomes that may arise. By regularly assessing the performance and impact of these systems, adjustments can be made to mitigate any unintentional harm or inequality.

Overall, ensuring fair and just AI systems in education requires a multi-faceted approach that involves careful dataset selection, algorithmic transparency, ethical education, and ongoing monitoring. By addressing these ethical concerns, we can strive for AI systems that promote equal opportunities and outcomes for all learners.

Fostering Trust and Public Acceptance

Addressing ethical concerns in artificial intelligence education is essential to foster trust and public acceptance. As AI technology becomes more pervasive, it is crucial to develop ethical solutions to tackle the problems that may arise.

One of the key aspects of fostering trust and public acceptance is transparency. Educators and developers should provide clear explanations and justifications for AI technologies and their applications in education. This transparency helps to build trust and ensures that users understand the ethical considerations behind the technology.

Additionally, involving diverse stakeholders in the development and implementation of AI education is critical. This includes students, educators, parents, policymakers, and experts from various disciplines. By incorporating diverse perspectives, ethical concerns can be properly addressed, and the needs and values of different stakeholders can be taken into account.

An open dialogue about ethical concerns is also crucial for fostering trust and public acceptance. This includes discussing potential biases, privacy concerns, and the impact of AI on human decision-making. By engaging in these conversations, educators can help students develop a critical understanding of AI technologies and their ethical implications.

Furthermore, ensuring that AI technologies are accessible and inclusive is vital for fostering trust and public acceptance. Educators should consider the potential barriers that AI may create, such as the digital divide or the exacerbation of educational inequalities. By addressing these issues and working towards equitable access to AI education, trust can be built among all members of society.

In conclusion, addressing ethical concerns in artificial intelligence education is essential for fostering trust and public acceptance. Through transparency, stakeholder involvement, open dialogue, and inclusivity, ethical solutions can be developed to address the problems that arise from AI technology. By promoting trust and understanding, AI education can be harnessed for the benefit of all.

Promoting Responsible AI Innovation

In the field of artificial intelligence (AI), responsible innovation plays a crucial role in addressing ethical concerns and ensuring that AI technology is used ethically and responsibly. As AI continues to evolve and integrate into various aspects of our lives, it is paramount to prioritize the education and awareness of ethical issues related to AI.

Educating on Ethical Problems

One of the key steps in promoting responsible AI innovation is the education of AI developers, policymakers, and the general public about the potential ethical problems associated with AI technology. By providing comprehensive education on the ethical implications of AI, individuals and organizations can make informed decisions and take necessary precautions to mitigate any negative consequences.

AI education programs should focus on highlighting potential biases in AI algorithms, the risks of AI replacing human decision-making, and the impact of AI on privacy and security. By emphasizing these topics, individuals can recognize the potential harm caused by AI technology and work towards developing ethical solutions.

In addition to educating individuals on ethical concerns, promoting responsible AI innovation also involves developing and implementing ethical solutions. This can include the establishment of guidelines and best practices for AI development, as well as creating frameworks for accountability and transparency in AI systems.

Collaboration between AI developers, policymakers, and ethicists is essential in developing these ethical solutions. By involving various stakeholders, a more holistic and comprehensive approach can be taken to address the ethical challenges posed by AI technology.

Furthermore, organizations should prioritize the promotion of diversity and inclusivity in AI development teams. By bringing together individuals from different backgrounds and perspectives, the development of AI technology can be more ethical and less prone to biases.

Conclusion:

Promoting responsible AI innovation requires a multi-faceted approach involving education, collaboration, and the development of ethical solutions. By addressing the ethical concerns associated with AI and ensuring that technology is used in a responsible manner, we can create a future where AI benefits society as a whole.

Mitigating Potential Harms and Risks

As artificial intelligence (AI) becomes increasingly integrated into education, it is crucial to address the potential harms and risks associated with its use. While AI technology has the potential to enhance learning experiences, it also presents several challenges and ethical concerns that need to be mitigated.

Identifying Problems

One of the key problems with AI in education is the potential for bias. AI systems are trained using data, and if the data used for training contains biases or reflects societal inequalities, then those biases can be perpetuated in the AI algorithms. This can lead to unfair treatment or discrimination against certain student groups. It is important to carefully evaluate the data used to train AI models and ensure that it is representative and unbiased.

Another problem is the lack of transparency in AI algorithms. Many AI systems in education operate as black boxes, meaning that their decision-making processes are not transparent or understandable. This can be problematic because it can make it difficult for educators and students to understand why certain decisions are being made. Implementing explainable AI models and providing transparency in decision-making can help address this issue.

Implementing Solutions

To mitigate these problems, several solutions can be implemented. Firstly, there should be a strong emphasis on diversity and inclusivity in AI education. This includes ensuring that the data used for training AI systems is diverse and representative of different student groups. It also involves promoting diversity in AI research and development teams to avoid biases in the design and implementation of AI technologies.

Secondly, there should be a focus on developing explainable AI models. Educators and students should be able to understand the reasoning behind AI decisions to foster trust and accountability. This can be achieved by designing AI systems that provide explanations for their outputs or by using interpretable algorithms that provide insights into decision-making processes.

Lastly, there should be ongoing monitoring and evaluation of AI systems in education. This includes regularly assessing for biases and discrimination and making necessary adjustments to improve fairness and equity. Educators and students should also be provided with the opportunity to provide feedback on AI systems and their impact on the learning experience.

Safeguarding Human Rights and Dignity

As artificial intelligence continues to advance and become a prominent part of our daily lives, it is crucial to address the ethical concerns that arise. One of the most pressing issues is how AI technologies can safeguard human rights and dignity.

When it comes to AI, there are several ethical problems that can arise, posing risks to human rights and dignity. Some of these problems include:

These problems can have serious implications on individuals and society as a whole. Therefore, it is essential to find solutions that address these ethical concerns.

To safeguard human rights and dignity in the context of artificial intelligence, a multi-faceted approach is necessary. Some possible solutions include:

By addressing these ethical concerns and implementing appropriate solutions, we can ensure that artificial intelligence respects and protects human rights and dignity.

Question-answer:,

What are some ethical concerns in ai education.

Some ethical concerns in AI education include issues of privacy, bias, and the impact of AI on the job market.

How can ethical concerns be addressed in AI education?

Ethical concerns in AI education can be addressed through curriculum development that includes discussions on ethics, transparency, and responsibility. It is also important for educators to emphasize the importance of ethical considerations in AI development.

Should AI education include discussions on bias and discrimination?

Yes, AI education should definitely include discussions on bias and discrimination. It is important for students to understand how bias can be inadvertently incorporated into AI algorithms, and ways to mitigate and address such biases.

What are some potential consequences of not addressing ethical concerns in AI education?

The potential consequences of not addressing ethical concerns in AI education include the creation and deployment of AI systems that perpetuate biases, invade privacy, and have negative impacts on society as a whole. It could lead to harmful consequences for marginalized communities.

Should AI education address the potential job displacement caused by AI?

Yes, AI education should address the potential job displacement caused by AI. Students should be educated about the potential impact of AI on the job market and be prepared for the changes and challenges that may arise.

What are the ethical concerns in artificial intelligence education?

Some of the ethical concerns in artificial intelligence education include bias in data sets, lack of transparency in algorithms, and concerns about privacy and security.

How can bias in data sets be a problem in AI education?

Bias in data sets can be a problem in AI education because it can lead to biased outcomes and discrimination. If the data used to train AI systems is biased, the AI system may learn and perpetuate those biases, leading to unfair decision-making and unequal treatment.

Why is transparency in algorithms important in AI education?

Transparency in algorithms is important in AI education because it allows users and educators to understand and evaluate how AI systems make decisions. Without transparency, it becomes difficult to detect and address biases or unfairness in AI systems.

What are the privacy and security concerns in AI education?

Privacy and security concerns arise in AI education because AI systems often require access to personal data such as student information. There is a risk of this data being mishandled or misused, leading to privacy breaches or unauthorized access to sensitive information.

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AI ethics are ignoring children, say Oxford researchers

Researchers from the Oxford Martin Programme on Ethical Web and Data Architectures (EWADA) , University of Oxford, have called for a more considered approach when embedding ethical principles in the development and governance of AI for children.

In a perspective paper published this week in Nature Machine Intelligence , the authors highlight that although there is a growing consensus around what high-level AI ethical principles should look like, too little is known about how to effectively apply them in principle for children. The study mapped the global landscape of existing ethics guidelines for AI and identified four main challenges in adapting such principles for children’s benefit:

• A lack of consideration for the developmental side of childhood, especially the complex and individual needs of children, age ranges, development stages, backgrounds, and characters.
• Minimal consideration for the role of guardians (e.g. parents) in childhood. For example, parents are often portrayed as having superior experience to children, when the digital world may need to reflect on this traditional role of parents.
• Too few child-centred evaluations that consider children’s best interests and rights. Quantitative assessments are the norm when assessing issues like safety and safeguarding in AI systems, but these tend to fall short when considering factors like the developmental needs and long-term wellbeing of children.
• Absence of a coordinated, cross-sectoral, and cross-disciplinary approach to formulating ethical AI principles for children that are necessary to bring about impactful practice changes.

The incorporation of AI in children’s lives and our society is inevitable. While there are increased debates about who should ensure technologies are responsible and ethical, a substantial proportion of such burdens falls on parents and children to navigate this complex landscape. Dr Jun Zhao , lead author, Oxford Martin Fellow and Department of Computer Science  

The researchers also drew on real-life examples and experiences when identifying these challenges. They found that although AI is being used to keep children safe, typically by identifying inappropriate content online, there has been a lack of initiative to incorporate safeguarding principles into AI innovations including those supported by Large Language Models (LLMs). Such integration is crucial to prevent children from being exposed to biased content based on factors such as ethnicity, or to harmful content, especially for vulnerable groups, and the evaluation of such methods should go beyond mere quantitative metrics such as accuracy or precision. Through their partnership with the University of Bristol, the researchers are also designing tools to help children with ADHD, carefully considering their needs and designing interfaces to support their sharing of data with AI-related algorithms, in ways that are aligned with their daily routes, digital literacy skills, and need for simple yet effective interfaces.

In response to these challenges, the researchers recommended:

• Increasing the involvement of key stakeholders, including parents and guardians, AI developers, and children themselves;
• Providing more direct support for industry designers and developers of AI systems, especially by involving them more in the implementation of ethical AI principles;
• Establishing legal and professional accountability mechanisms that are child-centred; and
• Increasing multidisciplinary collaboration around a child-centred approach involving stakeholders in areas such as human-computer interaction, design, algorithms, policy guidance, data protection law, and education.

In an era of AI powered algorithms children deserve systems that meet their social, emotional, and cognitive needs. Our AI systems must be ethical and respectful at all stages of development, but this is especially critical during childhood. Professor Sir Nigel Shadbolt , co-author, Director of the EWADA Programme, Professor of Computing Science at the Department of Computer Science  

Dr Jun Zhao , Oxford Martin Fellow, Senior Researcher at the University’s Department of Computer Science, and lead author of the paper, said: ‘This perspective article examined existing global AI ethics principles and identified crucial gaps and future development directions. These insights are critical for guiding our industries and policymakers. We hope this research will serve as a significant starting point for cross-sectoral collaborations in creating ethical AI technologies for children and global policy development in this space.’

The authors outlined several ethical AI principles that would especially need to be considered for children. They include ensuring fair, equal, and inclusive digital access, delivering transparency and accountability when developing AI systems, safeguarding privacy and preventing manipulation and exploitation, guaranteeing the safety of children, and creating age-appropriate systems while actively involving children in their development.

The study ‘Challenges and opportunities in translating ethical AI principles into practice for children’ has been published in Nature Machine Intelligence .

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The imperative of ethical AI practices in higher education

The journey towards building ethical ai is challenging, yet it also presents an opportunity to shape a future where technology serves as a force for good.

Key points:

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In the exponentially-evolving realm of artificial intelligence (AI), concerns surrounding AI bias have risen to the forefront, demanding a collective effort towards fostering ethical AI practices. This necessitates understanding the multifaceted causes and potential ramifications of AI bias, exploring actionable solutions, and acknowledging the key role of higher education institutions in this endeavor.

Unveiling the roots of AI bias

AI bias is the inherent, often systemic, unfairness embedded within AI algorithms. These biases can stem from various sources, with data used to train AI models often acting as the primary culprit. If this data reflects inequalities or societal prejudices, it can unintentionally translate into skewed algorithms perpetuating those biases. But bias can also work the other way around: take the recent case of bias by Google Gemini, where the generative AI created by Google, biased by the necessity of more inclusiveness, actually generated responses and images that have nothing to do with the reality it was prompted to depict.

Furthermore, the complexity of AI models, frequently characterized by intricate algorithms and opaque decision-making processes, compounds the issue. The very nature of these models makes pinpointing and rectifying embedded biases a significant challenge.

Mitigating the impact: Actionable data practices

Actionable data practices are essential to address these complexities. Ensuring diversity and representativeness within training datasets is a crucial first step. This involves actively seeking data encompassing a broad spectrum of demographics, cultures, and perspectives, ensuring the AI model doesn’t simply replicate existing biases.

In conjunction with diversifying data, rigorous testing across different demographic groups is vital. Evaluating the AI model’s performance across various scenarios unveils potential biases that might otherwise remain hidden. Additionally, fostering transparency in AI algorithms and their decision-making processes is crucial. By allowing for scrutiny and accountability, transparency empowers stakeholders to assess whether the AI functions unbiasedly.

The ongoing journey of building ethical AI

Developing ethical AI is not a one-time fix; it requires continuous vigilance and adaptation. This ongoing journey necessitates several key steps:

• Establishing ethical guidelines: Organizations must clearly define ethical standards for AI development and use, reflecting fundamental values such as fairness, accountability, and transparency. These guidelines serve as a roadmap, ensuring AI projects align with ethical principles.
• Creating multidisciplinary teams: Incorporating diverse perspectives into AI development is crucial. Teams of technologists, ethicists, sociologists, and individuals representing potentially impacted communities can anticipate and mitigate biases through broader perspectives.
• Fostering an ethical culture: Beyond establishing guidelines and assembling diverse teams, cultivating an organizational culture prioritizes ethical considerations in all AI projects is essential. Embedding ethical principles into an organization’s core values and everyday practices ensures ethical considerations are woven into the very fabric of AI development.

The consequences of unchecked bias

Ignoring the potential pitfalls of AI bias can lead to unintended and often profound consequences, impacting various aspects of our lives. From reinforcing social inequalities to eroding trust in AI systems, unchecked bias can foster widespread skepticism and resistance toward technological advancements.

Moreover, biased AI can inadvertently influence decision-making in critical areas such as healthcare, employment, and law enforcement. Imagine biased algorithms used in loan applications unfairly disadvantaging certain demographics or in facial recognition software incorrectly identifying individuals, potentially leading to unjust detentions. These are just a few examples of how unchecked AI bias can perpetuate inequalities and create disparities.

The role of higher education in fostering change

Higher education institutions have a pivotal role to play in addressing AI bias and fostering the development of ethical AI practices:

• Integrating ethics into curricula: By integrating ethics modules into AI and computer science curricula, universities can equip future generations of technologists with the necessary tools and frameworks to identify, understand, and combat AI bias. This empowers them to develop and deploy AI responsibly, ensuring their creations are fair and inclusive.
• Leading by example: Beyond educating future generations, universities can also lead by example through their own research initiatives. Research institutions are uniquely positioned to delve into the complex challenges of AI bias, developing innovative solutions for bias detection and mitigation. Their research can inform and guide broader efforts towards building ethical AI.
• Fostering interdisciplinary collaboration: The multifaceted nature of AI bias necessitates a collaborative approach. Universities can convene experts from various fields, including computer scientists, ethicists, legal scholars, and social scientists, to tackle the challenges of AI bias from diverse perspectives. This collaborative spirit can foster innovative and comprehensive solutions.
• Facilitating public discourse: Universities, as centers of knowledge and critical thinking, can serve as forums for public discourse on ethical AI. They can facilitate conversations between technologists, policymakers, and the broader community through dialogues, workshops, and conferences. This public engagement is crucial for raising awareness, fostering understanding, and promoting responsible development and deployment of AI.

Several universities and higher education institutions, wallowing in the above principles, have created technical degrees in artificial intelligence shaping the artificial intelligence professionals of tomorrow by combining advanced technical skills in AI areas such as machine learning, computer vision, and natural language processing while developing in each one of them ethical and human-centered implications.

Also, we are seeing prominent universities throughout the globe (more notably, Yale and Oxford) creating research departments on AI and ethics.

The journey towards building ethical AI is challenging, yet it also presents an opportunity to shape a future where technology serves as a force for good. By acknowledging the complex causes of AI bias, adopting actionable data practices, and committing to the ongoing effort of building ethical AI, we can mitigate the unintended consequences of biased algorithms. With their rich reservoir of knowledge and expertise, higher education institutions are at the forefront of this vital endeavor, paving the way for a more just and equitable digital age.

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Ethical principles for artificial intelligence in education

Andy nguyen.

1 Learning & Educational Technology Research Unit (LET), University of Oulu, Oulu, Finland

Ha Ngan Ngo

2 Faculty of Education, Victoria University of Wellington, Wellington, New Zealand

Yvonne Hong

3 School of Information Management, Victoria University of Wellington, Wellington, New Zealand

Bich-Phuong Thi Nguyen

4 Faculty of English Language Teacher Education, VNU University of Languages and International Studies, Hanoi, Vietnam

Associated Data

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

The advancement of artificial intelligence in education (AIED) has the potential to transform the educational landscape and influence the role of all involved stakeholders. In recent years, the applications of AIED have been gradually adopted to progress our understanding of students’ learning and enhance learning performance and experience. However, the adoption of AIED has led to increasing ethical risks and concerns regarding several aspects such as personal data and learner autonomy. Despite the recent announcement of guidelines for ethical and trustworthy AIED, the debate revolves around the key principles underpinning ethical AIED. This paper aims to explore whether there is a global consensus on ethical AIED by mapping and analyzing international organizations’ current policies and guidelines. In this paper, we first introduce the opportunities offered by AI in education and potential ethical issues. Then, thematic analysis was conducted to conceptualize and establish a set of ethical principles by examining and synthesizing relevant ethical policies and guidelines for AIED. We discuss each principle and associated implications for relevant educational stakeholders, including students, teachers, technology developers, policymakers, and institutional decision-makers. The proposed set of ethical principles is expected to serve as a framework to inform and guide educational stakeholders in the development and deployment of ethical and trustworthy AIED as well as catalyze future development of related impact studies in the field.

Introduction

The application of artificial intelligence (AI) in education has been featured as one of the most pivotal developments of the century (Becker et al., 2018 ; Seldon with Abidoye, 2018 ). Despite the rapid growth of AI for education (AIED) and the surge in its demands under the COVID-19 impacts, little is known about what ethical principles should be in guiding the design, development, and deployment of ethical and trustworthy AI in education. And even if those are addressed, the depth and breadth to which contemporary ethical and regulatory frameworks are able to capture the impacts of AI evolvement remain unfolded.

The complexity and “intelligence” of this technology have led to potentially extensive ethical threats that trigger a pressing need for risk-intensive procedures to ensure the quality of delivery. Indeed, a sense of flexibility that acknowledges human values within the developing momentum of AI is vital to fostering sustainable innovations. In the wake of such demand, UNESCO launched global standards for AI ethics which were agreed and signed by its 193 member countries on November 25, 2021. The document, whilst recognizing the “profound and dynamic” influences of AI, also highlights related flourishing dangers to the cultural, social, and ecological diversity (United Nations Educational, Scientific and Cultural Organization [UNESCO], 2021 ). Notably, it stipulates a universal framework of values for ethics which provides stakeholder-driven guidelines in adopting AI. This historic cross-border agreement marks the globally significant role of ethics in AI; however, it provides a relatively generic framework across disciplines and settings. In fact, for the development and governance of AI technologies, neither laissez-faire nor one-size-fits-all approach is adequate and appropriate across contexts. In the literature, ongoing debates regarding ethics of data exploitation in decision making and interventions occur cross-disciplines (Jalal et al., 2021 ; Farris, 2021 ; medical care as in Reddy et al., 2020 ) or human resources management, as in Tambe et al., ( 2019 ); sports performance analysis as in Araújo et al., ( 2021 ). Recently, researchers and international organizations have specifically examined the ethics of AI in education (Holmes et al., 2021 ). Despite there being some overlaps and common agreements among these ethical guidelines and reports, no previous study has systematically assessed a global consensus on ethics for AIED.

Our study attempts to fill these gaps by examining and matching ethical guidelines and reports from UNESCO Ethics AI (Ad Hoc Expert Group [AHEG], 2020), UNESCO Education & AI (Miao et al., 2021 ), Beijing Consensus (UNESCO, 2019), OECD (Organization for Economic Co-operation and Development [OECD], 2021), European Commission ( 2019 ), and European Parliament Report AI Education (2021). We sought to prescribe a set of ethical principles for trustworthy AIED based on the thematic analysis results. The establishment of unified ethical principles for AIED gives the research agenda in this domain a new opportunity to meet the demands of a widespread digitalization of education.

This paper is organized as follows. We first introduce a holistic picture of AI in education and present the emerging opportunities. Then, we provide a critical review of the extant literature on ethical issues of AI in education. Next, we present the thematic analysis results of the relevant ethical guidelines and reports for AIED then discuss the implications for associated educational stakeholders. Finally, we conclude by highlighting the significance of ethics in the contemporary discussion of education through which propose several key ethical principles that underpin ethical and trustworthy AIED.

Opportunities of artificial intelligence in education

The penetration of AI in every sphere of educational practices has undeniably filtered teachers’ and students’ personal and professional development with numerous opportunities (Xu & Ouyang, 2021 ; Ouyang et al., 2022 ). Existing literature has witnessed a wide diversity of perspectives on the use of AI in education, ranging from the non-teaching aspects (e.g., timetabling, resource allocation, student tracking, provision of information about students to their parents/guardians (reports) to the personalization of teaching and learning (tailored design and marking of assessments, curriculum and AI apps that support learners, or locate changes in learner engagement during foreign language learning (Fahimirad & Kotamjani, 2018 ; Luckin, 2017 ; Reiss, 2021 ; Skinner et al., 2019 ). Hwang et al., ( 2020 ) identified four key roles of AI in education driven by an applications-based perspective that espouses the position of AI as an intelligent tutor, tutee, learning tool/partner, or policy-making advisor.

AIED is seen as an influential tool to empower new paradigms of instruction, technology advancement, and innovations in educational research that are deemed unfeasible in the conventional classroom settings, for instance, the implementation of artificial neural networks, machine learning or CALL (Computer-Assisted Language Learning) in formal, non-formal and informal learning scenarios (Holmes et al., 2019; Hwang et al., 2020 ). It enables computer-assisted collaborative learning or asynchronous discussion groups, allows cost-wise personalized learning through a navigation system underpinned by algorithms (Nye, 2015 ), promoted by the use of automated assessment,facial recognition systems, and predictive analytics (Akgun & Greenhow, 2021 ). Hence, there are growing evidence for the roles of AIED to “foster a transformation of knowledge, cognition, and culture” (Hwang et al., 2020 , p.1). However, the implementation of AIED has faced several challenges related to ethical concerns and justification.Although recent attempts have been made to provide ethical guidelines for AIED, there remains the question of a global consensus and standard guidelines for AIED. As the regulation and ethical consensus of these technologies is needed for utilizing their various capabilities in education, this paper sought to offer an integrated overview of ethical guidelines for AIED.

Ethical issues of AI in education

Despite its capability to revolutionize education, numerous challenges also linger for researchers and practitioners who are involved in associated activities or systems (Kay & Kummerfeld, 2019 ) as AIED is, by nature, a “highly technology-dependent and cross-disciplinary field” (Hwang et al., 2020 , p.2). At a global level, UNESCO (2019) pinpointed six challenges in achieving sustainable development of AIED: comprehensive public policy, inclusion and equity in AIED, preparing teachers for AI-powered education,preparing AI to understand education, developing quality and inclusive data systems, making research on AIED significant, ensuring ethics and transparency in data collection, use, and dissemination. At the individual level, challenges range from critical societal drawbacks such as systemic bias, discrimination, inequality for marginalized groups of students, and xenophobia (Hwang et al., 2020 ) to thorny ethical issues relating to privacy and bias in data collection and processing (Holmes et al., 2021 ). In fact, the widespread ramifications of AIED have also led to emerging concerns over the negative realities that it brings, such as the widening gaps of inequalities among learners’ commercialization of education, or the home-school divide in education (Reiss, 2021 ). AI may become pervasive in every sense where those involved may be exposed to risks without being aware of them, and the situation can be even intensified under the ongoing impacts of the COVID-19 pandemic (Borenstein & Howard, 2021 ). Such obstacles essentialize an urgent demand to induct and acquaint teachers and students with the ethical concerns surrounding AIED and how to navigate them.

Furthermore, AIED also carries ethical implications and privacy risks which call for critical attention to differentiate between doing ethical things and doing things ethically (Holmes et al., 2021 ), or as in the words of Russell and Norvig ( 2002 ) “all AI researchers should be concerned with the ethical implications of their work” (p. 1020). Indeed, a proliferation of studies has revealed the emergence of contrasting ethical themes relating to general AI and AIED, most of which are associated with the liability of data across settings, such as in higher education (Zawacki-Richter et al., 2019 ), K-12 (Holstein et al., 2019 ), schools (Luckin, 2017 ), and subjects (Hwang & Tu, 2021 ). These covered the issues of informed consent, privacy breach, biased data assumption, fairness, accountability, and statistical apophenia. Others also question the impacts of AI-related fields such as surveillance and consent, learner privacy (Sacharidis et al., 2020 ), identity configuration, user confidentiality, integrity, and inclusiveness (Deshpande et al., 2017 ). Another stream of discussion has been drawn upon the ethics of data designated for educational use and analytics learning (e.g., Kay & Kummerfeld 2019 ; Kitto & Knight, 2019 ; Slade & Prinsloo, 2013 ). These incorporate the spheres of data interpretation and management, different perspective on the data usage, and the power relation among involved stakeholders such as students, teachers, and the educational objectives (Slade & Prinsloo, 2013 ). Other ethical issues for AIED include the problems with data collection, restricted availability of data sources, bias and representation, data ownership and control, data autonomy, AIED systems, and human agency (Akgun & Greenhow, 2021 ; Miao et al., 2021 ). That said, it is crucial to fully comprehend these values and principals before making ethically and accountability-driven decisions, and being aware of possible, even unexpected outcomes in education.

Although recent work has attempted to establish different ethical frameworks for general AI use (e.g., Ashok et al., 2022 ), ethical and privacy issues are suggested to be contextualized (Ifenthaler & Schumacher, 2016 ), hence the prior guidelines established in other disciplines might not be appropriate for education. The contextual approach to the ethical design and use of AIED could play an essential role in addressing the issues of ethical and privacy concerns in education context. Prior research has emphasised the importance of the sociotechnical context configured by educational technology and educations practices in ethical considerations (Kitto & Knight, 2019 ). The understanding of ethics and privacy from various perspectives could promote the design of ethical and trustworthy AIED and the adoption of such systems. Furthermore, we extended the ethical view from published studies reviewed by Ashok et al., ( 2022 ) to the policies and guidelines proposed by the international organizations such as UNESCO, OECD, and European Union. The consensus assessment of policies and guidelines would inform a comprehensive and integrated instructions for different stakeholders in adopting AIED. This contributes to establishing a common ground and solid foundation for further development and implementation of AIED.

Ethical principles for artificial intelligence in education (AIED)

There are continued calls for substantial ethical guidelines and open communications with beneficiaries: educators, students, parents, AI developers, and policymakers (Berendt et al., 2020 ; Nigam et al., 2021 ; Hagendorff, 2020 ) stated that more emphasis is necessary to enforce ethical guidelines for AI systems to better align with societal values. Safeguard measures and human oversight are required to oversee how these AI systems are designed, how they function and evolve. The knowledge of behavioral science, equipped with self-awareness and empathy at the fore, is argued to intrinsically motivate AI developers to develop more trustworthy and responsible AI (Dhanrajani, 2018 ).

We conducted thematic analysis on relevant ethical guidelines and reports related to AIED found from international organizations, including UNESCO Ethics AI (AHEG, 2020), UNESCO Education & AI (Miao et al., 2021 ), Beijing Consensus (UNESCO, 2019), OECD (Organization for Economic Co-operation and Development, 2021 ), European Commission ( 2019 ), and European Parliament Report AI Education (2021). The paper focused on identifying and developing a set of main principle themes by using inductive analysis, based on Braun & Clarke ( 2012 )’s thematic analysis process. The analysis consists of initial familiarization with the ethical guidelines and reports. This involved re-reading of reports and noting down patterns, such as similar use of words, points of discussion, and definitions. This is followed by an open coding approach where the terms and definitions are meaningfully categorized, followed by labeling each category with a code. This resulted in a total of 39 codes. Next, these codes were examined and collated into patterns of broader meaning, resulting in 7 themes (i.e. principles). The coding and themes generation process was conducted iteratively, where a researcher-researcher corroboration method was also in place to ensure the reliability and validity (Patton, 2015 ) of the proposed principles and corresponding code mapping in Table  1 .

Ethical Principles for Artificial Intelligence in Education

Principle of governance and stewardship

A recurring theme across AI policies is the issue of governance and stewardship of AIED (Ashok et al., 2022 ). For example, the 2021 UNESCO Education & AI 2021 asserted the need to “set up a system wide organizational structure for policy governance and coordination” (Miao et al., 2021 , p32). This is further acknowledged in other papers such as OECD (2021, p.4) recommendation for “Principles for responsible stewardship of trustworthy AI”. AIED governance and stewardship declares and manages how AI should be employed in education and relevant mechanisms to assure the compatibility between the role of the technology being deployed and its designed purposes, to optimize educational stakeholders’ needs and benefits. AI principle of governance has been formally defined as “the practice of establishing and implementing policies, procedures and standards for the proper development, use and management of the infosphere.” (Floridi, 2018 , p.3). Meanwhile, AI stewardship could be defined as ethics embodied in the careful and responsible management of the design and use of AIED. Although governance and stewardship have been mentioned in most ethical guidelines and policies for AIED, these issues have been surprisingly disregarded from many contemporary ethical debates in the literature (Ashok et al., 2022 ). While governance refers to “a structure or pattern, stewardship is an activity” (Greer, 2018 , p.42). In other words, taking action on issues such as building capacity or developing transparency from a long list of policies can be seen as good stewardship or setting up a better governance. According to OECD Principles for responsible stewardship of trustworthy AI (OECD, 2021), there are five complementary principles relevant to all stakeholders: (i) inclusive growth, sustainable development and well-being; (ii) human-centred values and fairness; (iii) transparency and explainability; (iv) robustness, security and safety; and (v) accountability. While the first and second principles sought to attain the inclusiveness and human-centredness in AIED, the later three OECD principles share several common intersections with data ethics and physical safety in using AIED. Accordingly, we propose that the governance and stewardship of AIED should accomplish all ethical aspects of relevant domains.

Principle of governance and stewardship: The governance and stewardship of AIED should carefully take into account the interdisciplinary and multi-stakeholder perspectives as well as all ethical considerations from relevant domains, including but not limited to data ethics, learning analytics ethics, computational ethics, human rights, and inclusiveness.

The consideration of soft and hard ethics from relevant domains in the governance and stewardship of AIED is critical for the ethical design and use of trustworthy AIED and enhancing its societal implications.

Principle of transparency and accountability

Data ethics emphasized the need for transparency in data usage in AIED (Larsson & Heintz, 2020 ). AI tools have been gradually applied quite extensively in education to enhance learning and teaching practices (Wang & Cheng, 2021 ), but the challenge remains unaddressed regarding the transparency of the data generated. Cope & Kalantzis ( 2019 ) highlighted that this ethical principle is essential to teachers and students as data visualization represents learner behavior, and accentuates additional support that educators could provide. It should be noted that the transparency lies in what the data itself is, where it is collected, what it shows, what happens to it, and how it is used (Digital Curation Centre, 2020). These questions could be answered once data ownership, accessibility, and explainability are sustained.

The notion of data ownership, by nature, is a matter of transparency and fairness (Remian, 2019 ), dealing with who owns and is entitled to the rights to access the personal data of learners. Although technically speaking, consent may often be given to data collectors, whether the data usage intrudes on learners’ privacy has long remained controversial. A valid argument aligned with the integrity of the motive for data collection could be proposed, in which the ownership should be granted to students themselves. Indeed, students are those providing data, thereby having the rights to own and control how data should be used to benefit their own learning (Holmes et al., 2021 ). Meanwhile, there comes a plausible claim about the rights of institutions to access and use student data since interactions and performance of learners, in essence, are recorded using a structured learning system provided by these educational institutions.

The concept of explainability in AI and data is closely linked to the transparency of the AI system and data generated. Indeed, data should feature the ability to explain some predictions from a technical viewpoint of a particular human. AI explainability underscores the insights in how AI systems functioned and made a decision should be well informed and explicable to the stakeholders, though the explicability relies on their technical expertise and role (Kazim & Koshiyama, 2021 ; UNESCO, 2019). The opaque nature of AI often poses many challenges for stakeholders to fathom the logic of this “black box” behind its decision-making. For instance, the absence of explainability could result in teachers being unable to use AIED effectively and timely detect the problems related to students’ behavior and learning performance (Remian, 2019 ). Therefore, this ethical concern centers on the intelligibility of the operation and outcomes of AI educational systems.

Principle of transparency in data and algorithms: The process of collecting, analyzing, and reporting data should be transparent with informed consent and clarity of data ownership, accessibility, and the purposes for how data will be used. The AI algorithms should be explainable and justifiable for specific educational purposes.

The transparency of AIED has been highlighted in several ethical guidelines, including the European Commission’s ethics guidelines for trustworthy AI (2019), European Parliament ( 2021 ), UNESCO Education & AI (Miao et al., 2021 ), Beijing Consensus UNESCO (2019), and OECD’s Principles for responsible stewardship of trustworthy AI (2021). However, the components and descriptions of transparency vary among these reports and guidelines. For instance, while the European Commission ( 2019 , p.18) explained it as “closely linked with the principle of explicability and encompasses transparency of elements relevant to an AI system: the data, the system and the business model”, UNESCO 2020 Draft points to its association “to adequate responsibility and accountability measure” (AHEG, 2020, p.10). Alongside the transparency in data and algorithms, transparency should be of utmost significance to all AIED regulations.

Principle of Transparency in Regulation: The process of establishing, conducting, monitoring, and controlling regulations of AIED should be transparent, traceable, explainable, and communicable in an open and clear manner with clarity of regulatory roles, accessibility, responsibilities, the purposes for how AI will be developed and used, and under which conditions. Additionally, the regulation of AIED should be transparent in its auditability, and it also links with the next ethical principle of regulatory accountability.

Principle of accountability: The regulation of AIED should explicitly address acknowledgment and responsibility for each stakeholder’s actions involved in the design and use of AIED, including auditability, minimization, and reporting of negative side effects, trade-offs, and compensation.

The accountability of AIED relates to the concept of “responsible AI” that features the ethical practice of designing, developing, and implementing AI with good intentions to empower relevant stakeholders and society fairly. Though ‘responsible AI’ has become increasingly popular, the terms accountability and responsibility are rarely defined (Jobin et al., 2019 ). Nevertheless, it is commonly referred to as acting with integrity and clearly determining the attribution of responsibility and legal liability with careful consideration of potentially harmful factors. AI has been questioned over whether it should be held accountable in a human-like manner, or whether humans should always be the sole actors responsible for AI as technological artifacts. In conjunction with human-centered AIED that encouraged human oversight over AI, we recommended the latter case that educational stakeholders should always be the responsible ones for AIED. Furthermore, some AI policies have highlighted that regulation of AIED should step beyond the scope of individual and organizational accountability to also consider sustainability and proportionality (AHEG, 2020).

Principle of sustainability and proportionality

Similar to other technology advances, the development and deployment of AI should also take into account environmental concerns to the extent that is referenced (AHEG, 2020; OECD, 2021). Particularly, sustainability calls for the design, development, and use of AIED to consider optimizing energy efficiency and minimizing its ecological footprint (European Commission, 2019 ). Accordingly, regulations of AIED are required to create policies ensuring these considerations are accomplished throughout the processes of developing and deploying AIED. Moreover, regulation of AIED must consider other sustainable domains, including economic and societal aspects such as employability, culture, and politics (European Parliament, 2021 ).

Principle of sustainability and proportionality: AIED must be designed, developed, and used in a justifiable way that they would not disrupt the environment, world economy, and society, such as the labor market, culture, and politics.

For instance, regulation of AIED should consider ensuring policies supporting accountability of potential job losses and to leverage challenges as an opportunity for innovation (UNESCO, 2019). Careful deliberations of sustainability and proportionality will make AIED more approachable and beneficial to all.

Principle of privacy

Personal privacy also emerged as a critical ethical concern in the implementation of AIED. Privacy, by nature, could be defined as “the right to be left alone”, which underscores the right of having personal information being protected (Muller, 2020 ). This digital revolution in education, particularly the use of AI and learning analytics in the field of education, entails a massive amount of personal data generated, captured, and analyzed to optimize learning experiences (Tzimas & Demetriadis, 2021 ; Pardo & Siemens, 2014 ). The personal data of teachers and learners may run the risk of privacy breaches. For instance, in respect of agent-based personalized education, personal information of learning performance accumulated in the past could be utilized for future prediction. However, this is considered against the will of many students (Li, 2007 ).

To protect and support the right of learners’ privacy and social well-being while learning in the context of increasingly knowledgeable machines and computer agents, AIED developers need to assess the views of teachers and students to decide how AI should be deployed in the classroom (Miao et al., 2021 ). For instance, an ethical concern may arise from a real-time facial expression recognition system used to predict the affective state (e.g. Jian-Ming Sun et al., 2008 ) or attendance of the learners without their consent (e.g. Pattnaik & Mohanty 2020 ). Developers and educators should embed transparency and visibility to AIED-related threats while explaining potential ramifications to students’ learning, careers, and social lives. The objective is to cultivate trust among learners and provide them with insights to leverage their skills across contexts while maintaining control of their respective data and digital identities (Jobin et al., 2019 ).

Principle of privacy: AIED must ensure well-informed consent from the user and maintain the confidentiality of the users’ information, both when they provide information and when the system collects information about them.

In most cases, when AIED tools are used to engage users in a particular learning activity, users are assumed to give consent, by which they would agree on terms of use of technology and how their personal data is collected, managed, and processed. Aligned with the principle of transparency and accountability, consent must be well informed as a pragmatic approach to building trust among students since the consent demonstrates their ease with the use of data by teachers to enhance their own learning performance (Li et al., 2021 ; Sedenberg & Hoffmann, 2016 ) also highlighted the significance of this consent to show respect towards students and reinforce their autonomy and freedom of choice. Once data is garnered, questions arise about how data management works, where and how long their personal information should be stored, and to whom the rights of accessibility should be granted (Corrin et al., 2019 ).

Principle of Security and Safety

One of the main functions of educational learning systems is to collect data of users, from which predictions about the learning behaviors and performance of users will be made. However, it is inevitable to envisage a scenario when the data is probably manipulated or corrupted by another party, or even worse, by cybercriminals.

Principle of Security: AIED should be designed and implemented in a manner that ensures the solution is robust enough to safeguard and protect data effectively from cybercrimes, data breaches and corruption threats, ensuring the privacy and security of sensitive information.

The concept of incorruptibility in AIED traces its root from incorruptibility in AI, or robustness against malicious manipulation by external factors. Bostrom & Yudkowsky ( 2014 ) pointed out that AI systems must be “robust against human adversaries deliberately searching for exploitable flaws in the algorithm” (p. 317). Therefore, it can be stated that the incorruptible nature and integrity of the data go hand in hand with data security. It is essential to protect the personal data of stakeholders, including students, teachers, and schools, to prevent any misuse or violation. The protection of data privacy and security is even more essential in the current context of normalizing virtual learning, and it requires concerted effort and the self-awareness of all the stakeholders.

Whereas learning analytics are governed by data ethics, many AIs are forms of intelligence expressed by some artifact (Bryson & Theodorou, 2019 ) that interacts with humans at various levels, such as robots and self-driving cars (Manoharan, 2019 ; O’Sullivan et al., 2019 ). This raises a universe of technical safety concerns regarding AI operation throughout its lifecycle in normal use, especially in harsh conditions or where other agents (both human and artificial) can interfere with the system.

Principle of Safety: AIED systems to be designed, developed, and deployed in a risk-management approach so that users are protected from unintended and unexpected harm, and that fatalities are mitigated.

As a result, it is pivotal that AIED developers take great care to design, train, pilot test, and validate the safety of AI systems (Leslie, 2019 ). Multistakeholder groups, including product developers, educators, and public authorities, should establish appropriate oversight, assessment, and due diligence mechanisms to ensure accountability and robustness throughout the AI lifecycle (AHEG, 2020). This group should produce detailed guidelines and ensure that AI users (educators and learners) receive adequate training to operate the system safely within the defined environment.

Principle of inclusiveness

Previous ethical discourse suggested that AI systems should contribute to global justice and be equally accessible to all (European Commission, 2018 ). Accessibility is vital to allow society to gain significant benefits from these systems. The exclusion of any individual is a violation of human rights. It is, hence, paramount that accessibility entails affordability, user-friendly designs catering to individuals of different demographics, cultures, and particularly those with disabilities (Kazim & Koshiyama, 2021 ). As highlighted in the European Commission Report 2021, inclusion and fairness of access to AI-powered education stress the basic needs and availability for internet coverage, followed by next-generation digital infrastructure.

Principle of Inclusiveness in Accessibility: AIED design, development, and deployment must take into account the infrastructure, equipment, skills, and societal acceptance that will accommodate a wide range of individuals in the intended region, allowing equitable access and use of AIED.

The current digital gap evidently widens after COVID-19, where countries with poor infrastructure hamper their aspirations of thriving in digitalization (Palomares et al., 2021 ). Furthermore, the fundamental lack of access to technologies, such as students from socially disadvantaged backgrounds not owning personal digital devices (Sá et al., 2021 ), calls for collective discussions with all educational stakeholders on the aspects of inclusion in AIED (i.e. addressing the lack of opportunities, resource sharing efforts to counter areas suffering from deprivation of learning resources, and knocking down discriminatory structures) to reduce educational inequities (Office of the High Commissioner for Human Rights, 2019 ).

Another aspect of inclusiveness is non-discrimination or unbiased AI algorithms. Quality education is fundamental in fostering a flourishing society, where all learners are viewed equally regardless of their gender, race, beliefs, sexual orientation, and any other conditions or circumstances (Palomares et al., 2021 ). AIED design requires careful considerations to avoid discrimination against certain groups, as AIED relies on and will only be as good as its trained data. Hence, it is crucial that AI developers take precautions by training the AIED with comprehensive and diverse data to reduce instances where the AIED would manifest a particular bias (Hogenhout, 2021 ) and violate the non-maleficence principle.

Principle of Inclusiveness in Data and Algorithms: AIED design, development, and deployment must apply non-discrimination and unbiased data and algorithms to ensure fairness and equality among different groups of beneficiaries.

Data quality plays a crucial role in determining whether AIED could make valid and unbiased decisions since bias manifests itself in the AIED system with the biased training data (Borgesius, 2018 ; Digital Curation Centre, 2020). Several aspects of biased data relating to gender, race, ethnicity, and special learning needs. An illuminating example in language education technology is given by West-Smith et al., ( 2018 ) that input data in the form of rubric writing and scoring system may place a constraint on the task choice and writing styles of students. Thus, there is a need for bias-free data in AIED to avoid biased algorithms.

Principle of human-centered AIED

In recognition of autonomy as a modern moral and political value (Calvo et al., 2020 ), the development and regulation of AIED need to adopt a human-centric approach that safeguards and empowers human autonomy This principle emphasizes the importance of supporting learners in developing their own potential (Miao et al., 2021 ; UNESCO, 2019).

Principle of Human-Centred AIED: The goal of AIED should be to complement and enhance human cognitive, social, and cultural capabilities while preserving meaningful opportunities for freedom of choice, securing human control over AI-based work processes.

Human autonomy, according to Deci and Ryan ( 2020 ), refers to the capacity to live one’s life according to one’s own motivation that is not the result of deception or manipulation. AI assistants today serve a variety of functions, generally intending to provide and assist individuals with some recommendations. In a sense, these can be considered external factors that affect an individual’s cognitive bias and emotions undermining or manipulating one’s intrinsic motivation (Vesnic-Alujevic et al., 2020 ). The design and operation of AI must thus, avoid misleading information, compromising users’ autonomy in developing independent thoughts, or negatively affecting users’ emotions and social well-being.

Research and development on AIED must avoid algorithms and wordings that serve as computational propaganda (Brundage et al., 2018 ; Nobre, 2020 ) in the form of automated feedback, learning assessment, and suggestions. This is particularly pertinent in an educational context, where many users are children and young people, constituting a vulnerable group that deserves special care and protection (European Parliament, 2021 ). There should be training programs supporting educators to gain the required skills to implement AIED. They should be able to adapt, filter or reduce automation that might coerce and manipulate learners’ thinking, impeding rather than supporting their motivation and identity development.

The focus of the previous dimension may be defined as the autonomy of will (Caughey et al., 2009 ) or positive freedom, referring to the capacity to develop one’s independent wishes and intrinsic motivations. Nevertheless, autonomy also underpins the autonomy of action (Möller, 2009 ), which refers to the ability to act on preferences without external restrictions. The AIED system relies on vast amounts of data to make predictions, which, in many cases, results in undesirable deductions of options to prevent users from engaging in or performing actions that the system views as errors (Bryson & Theodorou, 2019 ). Facebook’s decision to change its algorithm to prevent fake news and use fake IDs is one example. These interventions from AI, regardless of best intentions, potentially limit individual freedom of expression (an identity) or perform certain actions. Therefore, Fagan & Levmore ( 2019 ) suggested that humans ought to remain in the center of AI design and implementation, to be the ones presumably deciding the goals of AI and have the power to overrule machine decisions.

Of all AIED, a tool for assessing and providing guidance for students, predominantly referred to as an “intelligent tutoring system”, is the longest researched and most common application (Miao et al., 2021 ). The system mapped out learning materials and activities based upon experts’ knowledge of the subject and cognitive sciences, as well as student misconceptions and success. With increasing automated decisions and shortcut suggestions made by machines, it is likely that AIED will reduce learners’ interaction with others and their ability to cultivate individual resourcefulness, metacognition, self-regulation, and independent thought. One of AIED’s main ethical concerns is the possibility of undermining learner agency and pertaining to breaching autonomy of action.

To ensure a human-centric AIED that emphasizes the learner agency, researchers, developers, and practitioners must adopt an interdisciplinary approach to developing negotiation-based adaptive learning systems that emphasize but are not limited to transversal competencies (European Parliament, 2021 ). AIED should allow learners the power to negotiate the type and frequency of received support, scaffolding of not only knowledge but also metacognition and self-regulation skills (Chou et al., 2018 ; Daradoumis & Arguedas, 2020 ). Governments and educators should be aware of the AI literacy skills crucial for effective human-machine collaboration to develop and integrate the appropriate curriculum into education practices. As a result, not only will students and teachers remain in control and at the center of AI implementation, but humans and machines will also collaborate for improved educational outcomes rather than using AI to usurp humans (Bryson & Theodorou, 2019 ).

Final remarks and future directions

The education system faces a paradox of artificial intelligence. Though regarded as vital for AI generation of high-quality educational outcomes, AIED and related large-scale collection and analysis of personal data about learners are of considerable concern to human-rights advocates. This paper contributes to the discussions of the benefits of AI in education, and at the same time, raises concerns for the adverse impacts on fundamental issues surrounding human rights. The intricacy of AI necessitates a holistic and applicable set of ethical principles for AI in the educational context. By systematically analyzing well-documented general AI principles, we propose a set of ethical tenets for AIED as a starting point to engage and spark further debates on the robustness of these guidelines, followed by actionable and shared policies to ensure the AIED systems developed are essentially ethical by design. The proposed set of ethical principles should be considered when developing and implementing ethical and trustworthy AI systems for education.

Nevertheless, given the growing interest of AIED in a post-Covid era, it is foreseeable that this debate concerning will continually evolve and move forward in the long run. A natural progression to be witnessed within the literature, namely a precise mechanism of ethical principles in AIED, remains to be elucidated. Indeed, while educational practitioners and AI developers have the best intentions of developing and implementing AI to improve education, the guiding ethical principles for AIED are yet to be set in stone. Furthermore, the education system has been confronting a paradox of applications of artificial intelligence technology across teaching and learning contexts. Despite existing theoretical frameworks investigating ethics of AI in general, no universal consensus has been reached on the best ethical theory in general, with moderate attention given to a practical set of ethical standards in the field of education in particular. Additionally, though regarded as vital for AI generation of high-quality educational outcomes, AIED and related large-scale collection and analysis of personal data about learners are of considerable concern to human-rights advocates. Such challenges call for greater attention in effectively and appropriately addressing associated ethical dilemmas. Given the interdisciplinary nature of AI, this is anticipated to be an arduous task to achieve since ethical principles are basically derived from human being judgement which can be largely abstract-driven, frequently inextricably intertwined with subjective interpretations. The engagement of diverse stakeholders in any educational discourse further impedes ethical principles from being widely applied either in a formal or deductive manner. Hence, based on our findings as preliminary ground, future scholarship is encouraged to extend the focus of inquiry to the implementation stage, where the issues of accessibility assurance, bias and equity in adopting AIED, or developmental and neurological influences of AIED to vulnerable groups such as young children and handicapped would be another interesting sphere that deserves continued exploration. Considerably more work will need to be done to establish and validate a common understanding and standards on ethics in AIED. A natural progression of this work is to publish a website about this set of ethical principles for AIED in order to capitalize feedback and improvement suggestions about the use of this framework. Furthermore, an automatic method by text analysis could be conducted in further work to provide complementing findings. Last but not least, embedding the principles of AI ethics in education, and also ethics issues such as responsibility, inclusion, fairness, security and explainability in conducting educational research will not only mitigate emerging societal abuses rooted from algorithmic injustice, but also bear instrumental implications to the landscape of AI governance and policy making for the long-term development of significant industries. Among one of the first papers to spark on the applicability of a set of ethical guidelines and practice standards for artificial intelligence in education, it is also our expectation that this will be a fruitful step towards guiding future educators and learners to exercise, if not instill, stronger accountability and responsibility in adopting AI and the technology that they employ for their teaching and learning in the future.

Overall, this paper contributes to the ongoing discussions of the benefits of AI in education, and at the same time, raises concerns for the adverse impacts on fundamental issues surrounding human rights. The intricacy of AI necessitates a holistic and applicable set of ethical principles for AI in the educational context. By systematically analyzing well-documented general AI principles, we propose a set of ethical tenets for AIED as a starting point to engage and spark further debates on the robustness of these guidelines, followed by actionable and shared policies to ensure the AIED systems developed are essentially ethical by design.

Open Access funding provided by University of Oulu including Oulu University Hospital. This work was funded in part by Finnish Academy project no. 350249

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The authors have NO conflict of interest to disclose.

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• Ad Hoc Expert Group (2020). Outcome document: First draft of the recommendation on the ethics of artificial intelligence. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000373434
• Akgun, S., & Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics , 1–10. 10.1007/s43681-021-00096-7 [ PMC free article ] [ PubMed ]
• Araújo, D., Couceiro, M., Seifert, L., Sarmento, H., & Davids, K. (2021). Artificial Intelligence in Sport Performance Analysis . Routledge. 10.4324/9781003163589
• Ashok M, Madan R, Joha A, Sivarajah U. Ethical framework for Artificial Intelligence and Digital technologies. International Journal of Information Management. 2022; 62 :102433. doi: 10.1016/j.ijinfomgt.2021.102433. [ CrossRef ] [ Google Scholar ]
• Becker, S. A., Brown, M., Dahlstrom, E., Davis, A., DePaul, K., Diaz, V., & Pomerantz, J. (2018). NMC Horizon Report: 2018 Higher Education Edition . Educause. https://library.educause.edu/~/media/files/library/2018/8/2018horizonreport.pdf
• Berendt B, Littlejohn A, Blakemore M. AI in education: learner choice and fundamental rights. Learning Media and Technology. 2020; 45 (3):312–324. doi: 10.1080/17439884.2020.1786399. [ CrossRef ] [ Google Scholar ]
• Borenstein J, Howard A. Emerging challenges in AI and the need for AI ethics education. AI and Ethics. 2021; 1 (1):61–65. doi: 10.1007/s43681-020-00002-7. [ CrossRef ] [ Google Scholar ]
• Borgesius, F. Z. (2018). Discrimination, artificial intelligence and algorithmic decision-making. Strasbourg: Council of Europe. https://rm.coe.int/discrimination-artificial-intelligence-and-algorithmic-decision-making/1680925d73
• Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. In K. Frankish, & W. Ramsey (Eds.), The Cambridge Handbook of Artificial Intelligence (pp. 316–334). Cambridge University Press. 10.1017/CBO9781139046855.020
• Braun, V., & Clarke, V. (2012). Thematic Analysis. In APA Handbook of Research Methods in Psychology: Vol 2 (pp. 57–71). American Psychological Association. 10.1037/13620-004
• Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., Anderson, H., Roff, H., Allen, G. C., Steinhardt, J., Flynn, C., hÉigeartaigh, S., Beard, S., Belfield, H., Farquhar, S., & Amodei, D. (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. ArXiv:1802.07228 [Cs] . http://arxiv.org/abs/1802.07228
• Bryson, J. J., & Theodorou, A. (2019). How society can maintain human-centric artificial intelligence. In Human-centered digitalization and services (pp. 305–323). Springer. 10.1007/978-981-13-7725-9_16
• Calvo, R. A., Peters, D., Vold, K., & Ryan, R. M. (2020). Supporting human autonomy in AI systems: A framework for ethical enquiry. In Ethics of Digital Well-Being (pp. 31–54). Springer. 10.1007/978-3-030-50585-1_2
• Caughey, D., Cohon, A., & Chatfield, S. (2009). Defining, measuring, and modeling bureaucratic autonomy. Annual Meeting of the Midwest Political Science Association, Chicago , 2
• Chou CY, Lai KR, Chao PY, Tseng SF, Liao TY. A negotiation-based adaptive learning system for regulating help-seeking behaviors. Computers & Education. 2018; 126 :115–128. doi: 10.1016/j.compedu.2018.07.010. [ CrossRef ] [ Google Scholar ]
• Cope B, Kalantzis M. Education 2.0: Artificial intelligence and the end of the test. Beijing International Review of Education. 2019; 1 :528–543. doi: 10.1163/25902539-00102009. [ CrossRef ] [ Google Scholar ]
• Corrin, L., Kennedy, G., French, S., Buckingham Shum, S., Kitto, K., Pardo, A., West, D., Mirriahi, N., & Colvin, C. (2019). The Ethics of Learning Analytics in Australian Higher Education: A Discussion Paper. https://melbournecshe.unimelb.edu.au/research/research-projects/edutech/the-ethical-use-of-learning-analytics
• Daradoumis T, Arguedas M. Cultivating students’ reflective learning in metacognitive activities through an affective pedagogical agent. Educational Technology and Society. 2020; 23 (2):19–31. [ Google Scholar ]
• Deci EL, Ryan RM. The” what” and” why” of goal pursuits: Human needs and the self-determination of behavior. Psychological inquiry. 2000; 11 (4):227–268. doi: 10.1207/S15327965PLI1104_01. [ CrossRef ] [ Google Scholar ]
• Deshpande, M., & Rao, V. (2017, December). Depression detection using emotion artificial intelligence. In 2017 International Conference on Intelligent Sustainable Systems (iciss) (pp. 858–862). IEEE. 10.1109/ISS1.2017.8389299
• Dhanrajani, S. (2018). 3 Ways To Human Centric AI. https://www.forbes.com/sites/cognitiveworld/2018/12/12/3-ways-to-human-centric-ai/?sh=495e42804a38
• Digital Curation Centre, The University of Edinburgh (2020). The Role of Data in AI: Report for the Data Governance Working Group of the Global Partnership of AI . https://www.research.ed.ac.uk/en/publications/the-role-of-data-in-ai
• European Commission (2018). Statement on artificial intelligence, robotics and ‘autonomous’ systems. European Union Publications Office. https://op.europa.eu/en/publication-detail/-/publication/dfebe62e-4ce9-11e8-be1d-01aa75ed71a1/language-en/format-PDF/source-78120382
• European Commission (2019). The European Commission’s high-level expert group on artificial intelligence: Ethics guidelines for trustworthy AI. European Union Publications Office. https://digital-strategy.ec.europa.eu/en/policies/expert-group-ai
• European Parliament (2021). Report on artificial intelligence in education, culture and the audiovisual sector (2020/2017(INI)). Committee on Culture and Education. https://www.europarl.europa.eu/doceo/document/A-9-2021-0127_EN.html
• Fagan F, Levmore S. The impact of artificial intelligence on rules, standards, and judicial discretion. S Cal L Rev. 2019; 93 :1. doi: 10.2139/ssrn.3362563. [ CrossRef ] [ Google Scholar ]
• Fahimirad M, Kotamjani SS. A review on application of artificial intelligence in teaching and learning in educational contexts. International Journal of Learning and Development. 2018; 8 (4):106–118. doi: 10.5296/ijld.v8i4.14057. [ CrossRef ] [ Google Scholar ]
• Farris AB, Vizcarra J, Amgad M, Cooper LA, Gutman D, Hogan J. Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples. Histopathology. 2021; 78 (6):791–804. doi: 10.1111/his.14304. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Floridi L. Soft Ethics and the Governance of the Digital. Philosophy & Technology. 2018; 31 (1):1–8. doi: 10.1007/s13347-018-0303-9. [ CrossRef ] [ Google Scholar ]
• Greer, S. L. (2018). Organization and governance: Stewardship and governance in health systems. Health Care Systems and Policies. New York, NY: Health Services Research. Springer . 10.1007/978-1-4614-6419-8_22-1
• Hagendorff T. The ethics of AI ethics: An evaluation of guidelines. Minds and Machines. 2020; 30 (1):99–120. doi: 10.1007/s11023-020-09517-8. [ CrossRef ] [ Google Scholar ]
• Hogenhout, L. (2021). Unite Paper | A Framework for Ethical AI at the United Nations . https://unite.un.org/news/unite-paper-framework-ethical-ai-united-nations
• Holmes W, Porayska-Pomsta K, Holstein K, Sutherland E, Baker T, Shum SB, Santos OC, Rodrigo MT, Cukurova M, Bittencourt II, Koedinger KR. Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education. 2021 doi: 10.1007/s40593-021-00239-1. [ CrossRef ] [ Google Scholar ]
• Holstein, K., McLaren, B. M., & Aleven, V. (2019). Designing for complementarity: Teacher and student needs for orchestration support in ai-enhanced classrooms. In International Conference on Artificial Intelligence in Education (pp. 157–171). Springer, Cham. 10.1007/978-3-030-23204-7_14
• Hwang GJ, Tu YF. Roles and Research Trends of Artificial Intelligence in Mathematics Education: A Bibliometric Mapping Analysis and Systematic Review. Mathematics. 2021; 9 (6):584. doi: 10.3390/math9060584. [ CrossRef ] [ Google Scholar ]
• Hwang GJ, Xie H, Wah BW, Gašević D. Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence. 2020; 1 :100001. doi: 10.1016/j.caeai.2020.100001. [ CrossRef ] [ Google Scholar ]
• Ifenthaler D, Schumacher C. Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development. 2016; 64 (5):923–938. doi: 10.1007/s11423-016-9477-y. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jalal S, Parker W, Ferguson D, Nicolaou S. Exploring the role of artificial intelligence in an emergency and trauma radiology department. Canadian Association of Radiologists Journal. 2021; 72 (1):167–174. doi: 10.1177/0846537120918338. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sun, J. M., Pei, X. S., & Shi-Sheng, Z. (2008). Facial emotion recognition in modern distant education system using SVM. 2008 International Conference on Machine Learning and Cybernetics , 3545–3548. 10.1109/ICMLC.2008.4621018
• Jobin, A., Ienca, M., & Vayena, E. (2019). Artificial Intelligence: The global landscape of ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. 10.1038/s42256-019-0088-2
• Kay J, Kummerfeld B. From data to personal user models for life-long, life‐wide learners. British Journal of Educational Technology. 2019; 50 (6):2871–2884. doi: 10.1111/bjet.12878. [ CrossRef ] [ Google Scholar ]
• Kazim E, Koshiyama AS. A high-level overview of AI ethics. Patterns. 2021; 2 (9):100314. doi: 10.1016/j.patter.2021.100314. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kitto K, Knight S. Practical ethics for building learning analytics. British Journal of Educational Technology. 2019; 50 (6):2855–2870. doi: 10.1111/bjet.12868. [ CrossRef ] [ Google Scholar ]
• Larsson S, Heintz F. Transparency in artificial intelligence. Internet Policy Review. 2020; 9 (2):1–16. doi: 10.14763/2020.2.1469. [ CrossRef ] [ Google Scholar ]
• Leslie, D. (2019). Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector. 10.5281/zenodo.3240529
• Li W, Sun K, Schaub F, Brooks C. Disparities in students’ propensity to consent to learning analytics. International Journal of Artificial Intelligence in Education. 2021 doi: 10.1007/s40593-021-00254-2. [ CrossRef ] [ Google Scholar ]
• Li X. Intelligent agent-supported online education. Decision Sciences Journal of Innovative Education. 2007; 5 (2):311–331. doi: 10.1111/j.1540-4609.2007.00143.x. [ CrossRef ] [ Google Scholar ]
• Luckin R. Towards artificial intelligence-based assessment systems. Nature Human Behaviour. 2017; 1 (3):1–3. doi: 10.1038/s41562-016-0028. [ CrossRef ] [ Google Scholar ]
• Manoharan S. An improved safety algorithm for artificial intelligence enabled processors in self driving cars. Journal of Artificial Intelligence. 2019; 1 (02):95–104. doi: 10.36548/jaicn.2019.2.005. [ CrossRef ] [ Google Scholar ]
• Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). AI and education: Guidance for policy-makers. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000376709
• Möller K. Two Conceptions of Positive Liberty: Towards an Autonomy-based Theory of Constitutional Rights. Oxford Journal of Legal Studies. 2009; 29 (4):757–786. doi: 10.1093/ojls/gqp029. [ CrossRef ] [ Google Scholar ]
• Müller, V. C. (2020). Ethics of Artificial Intelligence and Robotics. https://plato.stanford.edu/entries/ethics-ai/
• Nigam A, Pasricha R, Singh T, Churi P. A systematic review on ai-based proctoring systems: Past, present and future. Education and Information Technologies. 2021; 26 (5):6421–6445. doi: 10.1007/s10639-021-10597-x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nobre, G. (2020). Artificial Intelligence (AI) in communications: Journalism, public relations, advertising, and propaganda . 10.13140/RG.2.2.33598.31040
• Nye BD. Intelligent tutoring systems by and for the developing world: A review of trends and approaches for educational technology in a global context. International Journal of Artificial Intelligence in Education. 2015; 25 (2):177–203. doi: 10.1007/s40593-014-0028-6. [ CrossRef ] [ Google Scholar ]
• O’Sullivan, S., Nevejans, N., Allen, C., Blyth, A., Leonard, S., Pagallo, U., Holzinger, K., Holzinger, A., Sajid, M. I., & Ashrafian, H. (2019). Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery. The International Journal of Medical Robotics and Computer Assisted Surgery , 15 (1), e1968. 10.1002/rcs.1968 [ PubMed ]
• Office of the High Commissioner for Human Rights (2019). Transforming our world: the 2030 Agenda for Sustainable Development . https://sdgs.un.org/2030agenda
• Organization for Economic Co-operation and Development (2021). OECD Recommendation of the Council on Artificial Intelligence . OECD/LEGAL/0449. https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449
• Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies , 1–33. 10.1007/s10639-022-10925-9
• Palomares, I., Martínez-Cámara, E., Montes, R., García-Moral, P., Chiachio, M., Chiachio, J., & Herrera, F. (2021). A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects. Applied Intelligence , 1–31. 10.1007/s10489-021-02264-y [ PMC free article ] [ PubMed ]
• Pardo A, Siemens G. Ethical and privacy principles for learning analytics. British Journal of Educational Technology. 2014; 45 (3):438–450. doi: 10.1111/bjet.12152. [ CrossRef ] [ Google Scholar ]
• Patton MQ. Qualitative research & evaluation methods: integrating theory and practice. 4. Los Angeles: SAGE; 2015. [ Google Scholar ]
• Pattnaik, P., & Mohanty, K. K. (2020). AI-Based Techniques for Real-Time Face Recognition-based Attendance System- A comparative Study. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) , 1034–1039. 10.1109/ICECA49313.2020.9297643
• Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of AI in health care. Journal of the American Medical Informatics Association. 2020; 27 (3):491–497. doi: 10.1093/jamia/ocz192. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Reiss MJ. The use of AI in education: Practicalities and ethical considerations. London Review of Education. 2021; 19 (1):1–14. doi: 10.14324/LRE.19.1.05. [ CrossRef ] [ Google Scholar ]
• Remian, D. (2019). Augmenting education: Ethical considerations for incorporating artificial intelligence in education. ScholarWorks at UMass Boston . University of Massachusetts Boston. https://scholarworks.umb.edu/instruction_capstone/52
• Russel, S. J., & Norvig, P. R. (2002). Artificial Intelligence: A modern approach (2nd Ed.) Prentice Hall Upper Saddle River, NJ, USA
• Sá MJ, Santos AI, Serpa S, Ferreira M. Digitainability—Digital Competences Post-COVID-19 for a Sustainable Society. Sustainability. 2021; 13 (17):9564. doi: 10.3390/su13179564. [ CrossRef ] [ Google Scholar ]
• Sacharidis, D., Mukamakuza, C. P., & Werthner, H. (2020). Fairness and diversity in social-based recommender systems. In Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 83–88). 10.1145/3386392.3397603
• Sedenberg, E., & Hoffmann, A. L. (2016). Recovering the history of informed consent for data science and internet industry research ethics. https://arxiv.org/abs/1609.03266
• Seldon, A., & Abidoye, O. (2018). The fourth education revolution . Legend Press Ltd.
• Skinner, G., & Walmsley, T. (2019, February). Artificial intelligence and deep learning in video games a brief review. In 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) (pp. 404–408). IEEE. 10.1109/CCOMS.2019.8821783
• Slade, S., & Prinsloo, P. (2013). Learning Analytics: Ethical Issues and Dilemmas. American Behavioral Scientist , 57 (10), 10.1177/0002764213479366. 1510 – 1529
• Tambe P, Cappelli P, Yakubovich V. Artificial intelligence in human resources management: Challenges and a path forward. California Management Review. 2019; 61 (4):15–42. doi: 10.1177/0008125619867910. [ CrossRef ] [ Google Scholar ]
• Tzimas D, Demetriadis S. Ethical issues in learning analytics: a review of the field. Education Technology Research Development. 2021; 69 :1101–1133. doi: 10.1007/s11423-021-09977-4. [ CrossRef ] [ Google Scholar ]
• United Nations Educational, Scientific and Cultural Organization, & Organization, C. (2021). Recommendation on the Ethics of Artificial Intelligence . United Nations Educational. https://unesdoc.unesco.org/ark:/48223/pf0000379920.page=14
• United Nations Educational, Scientific and Cultural Organization (2019). Beijing Consensus on artificial intelligence and education. Outcome document of the International Conference on Artificial Intelligence and Education, Planning Education in the AI Era: Lead the Leap, Beijing, 2019 . United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000368303
• Vesnic-Alujevic L, Nascimento S, Pólvora A. Societal and ethical impacts of artificial intelligence: Critical notes on European policy frameworks. Telecommunications Policy. 2020; 44 (6):101961. doi: 10.1016/j.telpol.2020.101961. [ CrossRef ] [ Google Scholar ]
• Wang T, Cheng ECK. An investigation of barriers to Hong Kong K-12 schools incorporating Artificial Intelligence in education. Computers and Education: Artificial Intelligence. 2021; 2 :100031. doi: 10.1016/j.caeai.2021.100031. [ CrossRef ] [ Google Scholar ]
• West-Smith, P., Butler, S., & Mayfield, E. (2018). Trustworthy automated essay scoring without explicit construct validity. In Proceedings of the AAAI Spring Symposium on AI and Society: Ethics, Safety and Trustworthiness in Intelligent Agents. https://help.turnitin.com/Resources/RA%20Curriculum%20Resources/Research/Revision%20Assistant%20Validity%20AAAI%202018.pdf
• Xu, W., & Ouyang, F. (2021). A systematic review of AI role in the educational system based on a proposed conceptual framework. Education and Information Technologies , 1–29. 10.1007/s10639-021-10774-y
• Zawacki-Richter O, Marín VI, Bond M, Gouverneur F. Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education. 2019; 16 (1):1–27. doi: 10.1186/s41239-019-0171-0. [ CrossRef ] [ Google Scholar ]

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Research on Ethical Issues of Artificial Intelligence in Education

• Juan Chu 14 ,
• Linjin Xi 14 ,
• Qunlu Zhang 14 &
• Ruyi Lin 14  
• Conference paper
• First Online: 12 August 2022

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The application of artificial intelligence technology in the field of education is becoming more and more extensive, and the ethical issues that come with it are common. The development of responsible and trustworthy artificial intelligence has become a global consensus, but if we want to explore the philosophical problems behind technology, we must have a systematic understanding of the epistemological aspects of technology. Therefore, by analyzing the research results of scholars, this paper wants to try to clarify the problems that are not yet clear. So, this paper wants to (1) define the concept of the ethics of artificial intelligence in education; (2) clarify the ethical issues of artificial intelligence in education include: the ethics of people, the ethics of technology itself, and the ethics of education; (3) put the ethics of artificial intelligence in education into the category of Technical Application Ethics of Social Education (TAESE); and (4) obey the principles of people-oriented, accountability, ethical constraints, transparency, fairness and justice during constructing standards that how to apply AI ethically in the field of education. Artificial intelligence can better empower each other with education, to realize "education for artificial intelligence, not artificial intelligence for education".

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Topal, A., Eren, C., Geçer, A.: Chatbot application in a 5th grade science course. Educ Inf Technol (Dordr). (2021)

Google Scholar  

Molenaar, I.: “Personalisation of learning: Towards hybrid human-AI learning technologies”, in OECD Digital Education Outlook 2021: Pushing the frontiers with AI, blockchain, and robots, OECD Publishing (2021)

DeFalco, J., Rowe, J., Paquette, L., Georgoulas-Sherry, V., Brawner, K., Mott, B., Lester, J.: “Detecting and Addressing Frustration in a Serious Game for Military Training”, Int. J. artif. intell. educ. (2018)

Aiken, R. M., & Epstein, R. G.: Ethical guidelines for AI in education: Starting a conversation. 11, 163-176, Int. J. artif. intell. educ. (2000)

Analytics Insight. https://www.analyticsinsight.net/integration-of-data-and-artificial-intelligence/

World Economic Forum. https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence/

Holmes, W., Bektik, D., Woolf, B., Luckin, R.: Ethics in AIED: Who cares? In: 20th International Conference on Artificial Intelligence in Education (2019)

Artz, J.: Thinking About Technology: Foundations of the Philosophy of Technology. Ethics Inf Technol. (2000)

Dictionary Editing Room, Institute of Linguistics, Chinese Academy of Social Sciences. Modern Chinese Dictionary (1996)

Nalini, B. The hitchhiker’s guide to AI ethics. (2020)

Akgun, S., & Greenhow, C.: Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI Ethics. (2021)

Siau, K., & Wang, W.: Artificial intelligence (AI) ethics: ethics of AI and ethical AI. J Database Manage. (2020)

Borenstein, J., & Howard, A.: Emerging challenges in AI and the need for AI ethics education. AI Ethics. (2021)

Bruneault, F., Laflamme, A. S., & Mondoux, A.: AI Ethics Training in Higher Education: Competency Framework. (2022)

Li, X., Zhang, J., Wang, D.: Ethical Research Outline on Artificial Intelligence in Education. Open. Educ. Res. (2021)

Li, Z.: Ethics Thinking of Artificial Intelligence Empowering Education. China. Educ. Technol. (2021)

FAQ, https://ethics-of-ai.mooc.fi/chapter-1/2-what-is-ai-ethics

Zhang, L., Liu, X., Chang, J.: Ethical Issues of Artificial Intelligence Education and its Regulations. e-Educ. Res. (2021)

Deng, G., Li, M.: Research on Ethical Issues and Ethical Principles of Educational Artificial Intelligence. e-Educ. Res. (2020)

Sun, T.: How is It Possible for Technology to Be Good. High. Educ. Explor. (2021)

Akgun, S., Greenhow, C.: Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI Ethics (2021)

Schiff, D.: Education for AI, not AI for Education: The Role of Education and Ethics in National AI Policy Strategies. Int. J. artif. intell. educ. (2021)

UNESCO, https://unesdoc.unesco.org/ark:/48223/pf0000373434

IEEE, https://standards.ieee.org/wp-content/uploads/import/documents/other/ead_v2.pdf

European Commission, https://www.euractiv.com/wp-content/uploads/sites/2/2018/12/AIHLEGDraftAIEthicsGuidelinespdf.pdf

Dawson, D., Schleiger, E., Horton, J., McLaughlin, J., Robinson, C., Quezada, G., ... & Hajkowicz, S.: Artificial intelligence: Australia’s ethics framework-a discussion paper. (2019)

Hagendorff T.: The ethics of AI ethics: An evaluation of guidelines. Mind Mach. (2020)

Kieslich, K., Keller, B., & Starke, C.: Artificial intelligence ethics by design. Evaluating public perception on the importance of ethical design principles of artificial intelligence. Big Data Soc. (2022)

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Koedinger, K.: Ethics of AI in education: Towards a community-wide framework. Int. J. artif. intell. educ. (2021)

Remian, D.: Augmenting education: ethical considerations for incorporating artificial intelligence in education. (2019)

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Chu, J., Xi, L., Zhang, Q., Lin, R. (2022). Research on Ethical Issues of Artificial Intelligence in Education. In: Yang, J., et al. Resilience and Future of Smart Learning. ICSLE 2022. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5967-7_12

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