Ethical Challenges and Best Practices for Transparency in AIED: A Literature Review and Learner Centric Guidelines
Open accessAbstract
Artificial intelligence in education (AIED) is the use of artificial intelligence techniques and tools to enhance and support learning and teaching processes. However, AIED also raises ethical challenges related to transparency, which is the ability to understand how AIED systems make decisions that influence educational outcomes. This paper reviews the literature on transparency in AIED, and presents a set of learner centric guidelines for ensuring transparency. The paper uses the Transparency Index Framework by Chaudhry et al. (2022) as a guiding tool, and discusses the best practices for providing information, allowing oversight, and respecting rights and choices in AIED. The paper also illustrates these practices with examples of ethically and transparently designed AIED systems. The paper concludes by highlighting the trade-offs and precautions involved in ensuring transparency in AIED, and suggests future research directions. This paper contributes to the ethical discourse on transparency in AIED by providing a comprehensive overview, identifying the best practices and challenges, and proposing the learner centric guidelines.
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Rai et al. (2023) proposed the PEARL framework as a learner-centric approach to AI transparency in education.
“Rai et al. (2023) propose the PEARL framework (Personalized Explanation, Accountability, and Learner Rights) as a learner-centric approach to AI transparency in education”
From Harms and Ethical Risks of AI Detection Tools in Education: False Positives, Bias, Surveillance, and Student Rights (2026) · cites this paper as Rai et al. (2023)
Section 2.2.2 discusses strategies for learner autonomy and transparency in AI in education, supporting the learner-centric aspect of the claim. However, the paper's discussion of these strategies does not propose or name a PEARL framework; searches for “PEARL” and its stated expansion found no matching passage in our copy. The cited passages have no page numbers. (LLM, conf=0.85)
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- Chaudhry, M. A., Cukurova, M., & Luckin, R. (2022). A Transparency Index Framework for AI in Education. In M. M. Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners' and Doctoral Consortium (pp. 195-198). Springer International Publishing. https://doi.org/10.1007/978-3-031-11647-6_33 · DOI · Paper page
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- Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in Education: Towards a Community-Wide Framework. International Journal of Artificial Intelligence in Education, 32(3), 504-526. https://doi.org/10.1007/s40593-021-00239-1 · DOI · Paper page
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GİRAY, L., CANER, H. N., & AMOOZEGAR, A. (2026) · Journal of Narrative and Language Studies · doi:10.59045/nalans.2026.109
- Authors
- RAI, R. D., PAWAR, M., & RAJENDRAN, R.
- Affiliations
- Indian Institute of Technology Bombay (IN)
- Venue
- International Conference on Computers in Education
- Published
- 2023-12-04
- ISSN
- 3078-4360 (ISSN-L)
- OpenAlex
- W4406619171
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Conference on Computers in Education (publishedVersion)
- International Conference on Computers in Education (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- artificial intelligence in education, transparency, AI ethics, algorithmic decision-making, student rights, oversight, best practices
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