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AI Safety & Alignment

Ethics of AI in Education: Towards a Community-Wide Framework

W. Holmes(University College London), Kaśka Porayska‐Pomsta(University College London), Ken Holstein(Carnegie Mellon University), Emma Sutherland(Nesta), Toby T. Baker(Nesta), Simon Buckingham Shum(University of Technology Sydney), Olga C. Santos(Universidad Nacional de Educación a Distancia), Ma. Mercedes T. Rodrigo(Ateneo de Manila University), Mutlu Cukurova(University College London), Ig Ibert Bittencourt(Universidade Federal de Alagoas), Kenneth R. Koedinger(Carnegie Mellon University)
April 9, 2021International Journal of Artificial Intelligence in Education1,137 citations
PDFDOI

1.1k

Citations

71

Influential Citations

International Journal of Artificial Intelligence in Education

Venue

2021

Year

Abstract

Abstract While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically , to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first step towards addressing this critical gap, we invited 60 of the AIED community’s leading researchers to respond to a survey of questions about ethics and the application of AI in educational contexts. In this paper, we first introduce issues around the ethics of AI in education. Next, we summarise the contributions of the 17 respondents, and discuss the complex issues that they raised. Specific outcomes include the recognition that most AIED researchers are not trained to tackle the emerging ethical questions. A well-designed framework for engaging with ethics of AIED that combined a multidisciplinary approach and a set of robust guidelines seems vital in this context.

Analysis

Why This Paper Matters

This paper addresses a critical gap in the field of Artificial Intelligence in Education (AIED): the lack of explicit ethical frameworks. While AIED research inherently aims to support student learning, the authors argue that good intentions are insufficient. Drawing lessons from other AI domains, they emphasize the need to consider fairness, accountability, transparency, bias, autonomy, agency, and inclusion. The paper is significant because it moves beyond abstract ethical principles to a concrete, community-driven approach, surveying 60 leading researchers to identify practical challenges and solutions.

The timing of this work (2021) is crucial as AIED systems were being rapidly deployed, especially during the COVID-19 pandemic. By highlighting that most AIED researchers are not trained to tackle ethical questions, the paper underscores an urgent need for interdisciplinary collaboration and robust guidelines. This work serves as a wake-up call for the community to proactively address unintended consequences and pedagogical choices that are ethical.

Technical Contributions

  • Survey-based needs assessment: The authors designed and distributed a survey to 60 AIED leaders, collecting 17 detailed responses that reveal the community's ethical blind spots.
  • Framework proposal: The paper outlines a preliminary framework that combines multidisciplinary perspectives (e.g., ethics, education, computer science) with actionable guidelines.
  • Differentiation of ethical dimensions: It clearly distinguishes between "doing ethical things" (outcomes) and "doing things ethically" (processes), a nuanced contribution often overlooked in AI ethics.
  • Identification of key ethical issues: The work systematically catalogs concerns around fairness, accountability, transparency, bias, autonomy, agency, and inclusion specific to educational contexts.

Results

The survey results indicate that the majority of AIED researchers acknowledge they lack formal training in ethics. The paper does not provide quantitative metrics but instead offers qualitative insights: respondents emphasized the need for a well-designed framework that is both multidisciplinary and practical. The key outcome is the recognition that ethical guidelines must be co-developed with educators, students, and policymakers to be effective.

Significance

This paper has broad implications for the AI field, particularly for domains where AI directly impacts human development, such as education. It sets a precedent for community-wide ethical reflection and provides a template for other AI subfields (e.g., healthcare, criminal justice) to follow. By advocating for a framework that balances innovation with ethical safeguards, the work helps ensure that AIED systems are not only effective but also just and inclusive. The paper has already garnered 1137 citations, indicating its influence on subsequent research and policy discussions.

ai-ethicseducationfairnessaccountabilitytransparencybiasinclusionmultidisciplinary

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