Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and higher education: A systematic review
Open accessAbstract
The use of Artificial Intelligence or AI is rising in higher education. With this rise, the morality of AI programs is being questioned. There is as such a need to understand how notions of Fairness, Accountability, Transparency, and Ethics or FATE are identified in the AI and higher education studies to date. This systematic review paper aims to understand definitions and studies on FATE and AI in higher education literature. The contribution of this work is to provide a summary of FATE development and the synthesis of the challenges and potentials of each of the reviewed studies. A total of 33 review publications from SCOPUS and Web of Science (WoS) were included in this systematic literature review. We examined definitions of FATE noted in the reviewed articles (may have been multiple in each study) and grouped them into descriptive (understandable by laypeople) and technical (containing jargon) definitions. We also examined the main FATE term studied in detail in each reviewed article and grouped them into qualitative and quantitative studies. Findings show more descriptive definitions exist (especially for fairness) and similarly quantitative definitions mostly emerge for Fairness. Findings also show more quantitative studies exist (especially for fairness) and qualitative definitions mostly emerge for ethics. Generally, though, there are more definitions than relevant studies conducted in the literature. This systematic literature review offers a summary of definitions and studies conducted for FATE terms and AI in higher education literature. Future work may benefit from bridging the gap between laypeople and experts by linking descriptive definitions with technical ones as well as qualitative studies with quantitative ones. Moreover, future work can study accountability and transparency further and make the study of FATE terms more longitudinal, open-access, and reproducible.
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Responsible use may promote ethical reflection and conscientious academic behavior.
“Responsible use may promote ethical reflection and conscientious academic behavior (Memarian & Doleck, 2023), yet uncritical reliance can undermine originality, authorship norms, and institutional trust (Chan, 2023;Eaton, 2022;Fowler, 2023;Grassini, 2023;Katsamakas et al., 2024;Malinka et al., 2023;Nazaretsky et al., 2025).”
From Academic Integrity and Students’ Ethical Use of ChatGPT in Higher Education (2026) · cites this paper as Memarian & Doleck, 2023
Rights-aware educational research establishes the relevance of learner choice, academic values, legal literacy, and context-specific governance.
“Rights-aware educational research establishes the relevance of learner choice, academic values, legal literacy, and context-specific governance(Berendt et al., 2020;Lukianova et al., 2025;Memarian & Doleck, 2023;Oncioiu & Bularca, 2025;Panagopoulou et al., 2025;Temper et al., 2025).”
From Responsible AI in Polish Higher-Education Administration under the EU AI Act: High-Risk Use Cases, Fundamental-Rights Impact Assessment, and Institutional Accountability (2026) · cites this paper as Memarian & Doleck, 2023
Disparities in access to high-quality tools, digital literacy, and linguistic fluency may be interpreted as digital, cultural, and linguistic capital that benefits some groups and disfavors others.
“The disparities in the access to high-quality tools, digital literacy, and linguistic fluency may be interpreted as digital, cultural, and linguistic capital, which benefits some groups and disfavors others (Memarian and Doleck, 2023).”
From Mapping the impact of generative AI in higher education: A scoping review of psychological and equity dimensions. (2026) · cites this paper as Memarian and Doleck, 2023
AI outcomes can have discriminatory bias resulting from biased data, model design faults, or societal inequalities in training data.
“This can result from biased data, faults in the design of the model used, or societal inequalities where inbuilt into training data sets.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [40]
The success of bias mitigation efforts can be used as a metric for how well an organization aligns its AI applications with ethical standards.
“In such a situation, the successes of bias mitigation efforts can be held as an essential metric toward measuring how well an organization aligns its applications of AI with ethical standards38–40.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [40]
Bias is a key ethical issue for AI.
“Bias is a key ethical issue for AI - sometimes, the outcome of a computation can have an unacceptably discriminatory bias.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [40]
AI fairness is applied to education technology to ensure equal opportunity for students regardless of their background.
“Artificial intelligence fairness is applied to education technology to ensure equal opportunity for students regardless of their background [273].”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [273]
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The impact of AI perceived transparency on trust in AI recommendations in healthcare applications
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Crossing Boundaries: The Ethics of AI and Geographic Information Technologies
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From Shadow It to Shadow AI–Threats, Risks and Opportunities for Organizations
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Bullshit universities: the future of automated education
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Fair and Transparent Student Admission Prediction Using Machine Learning Models
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TeachTune: Reviewing Pedagogical Agents Against Diverse Student Profiles with Simulated Students
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Artificial Intelligence for Education and Research: Pilot Study on Perception of Academic Staff
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Muringa, T. P. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1628019 · Cited by 31 · see citing works
AI Technologies in Modern Taxation: Applications, Challenges, and Strategic Directions
Wang, M. (2024) · International Journal of Finance and Investment · doi:10.54097/v2kgty49 · Cited by 22 · see citing works
Wang, S. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13359-1 · Cited by 22 · see citing works
Unpacking the ethics of using AI in primary and secondary education: a systematic literature review
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Teaching in the Age of AI/ChatGPT in Mental-Health-Related Fields
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Ethical Use of Generative Artificial Intelligence Among Ecuadorian University Students
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Using Explainable AI to Rank Educational Success Factors Driving the Job Market
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To err is human: Bias salience can help overcome resistance to medical AI
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Developing a Social Support Framework: Understanding the Reciprocity in Human-Chatbot Relationship
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Abulibdeh, A. O. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.101033 · Cited by 34 · see citing works
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Ethics of Artificial Intelligence and Robotics: Key Issues and Modern Ways to Solve Them
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AI Ethics in the Fields of Education and Research: A Systematic Literature Review
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Artificial Intelligence Tools Applied to Education: A Systematic Literature Review
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Formation of Ethical Competences for AI Use in English Foreign Language Teachings
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AI, Personalized Education, and Challenges
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Yapay genel zekâ çağında öğretmen rolünün yeniden tanımlanması: öngörüler
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The Challenges of The Artificial Intelligence of Law in The Context of Technological Development
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- Authors
- Memarian, B., & Doleck, T.
- Affiliations
- Simon Fraser University (CA)
- Corresponding author
- Memarian, Bahar
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2023
- Volume
- 5
- Pages
- 100152
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4382133845
- Semantic Scholar
- 77018f7f53c3c524c88994ea69d528411595b250
- Cited by
- 416
- References
- 56
- Citations by year
- 2026: 138 · 2025: 173 · 2024: 93 · 2023: 12
- FWCI
- 16.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Canada Research Chairs
- Simon Fraser University
- Canada Foundation for Innovation
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- fairness, accountability, transparency, ethics, artificial intelligence in higher education, AI ethics
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