Implementing AI-Driven Recommender Engines in Education:

A case to explore how we might balance transparency and Fairness

Human Oversight & Intervention in AI

how to cite this learning scenario

Arantes, J. (2025). Implementing AI-Driven Recommender Engines in Education. Case Studies in AI Governance for Education. www.AI4education.org. Licensed under a Creative Commons Attribution 4.0 International License.
abstract
This case study examines the ethical and practical challenges of implementing AI-driven recommender engines in educational settings, drawing on the example of TravelCo.com's approach to using AI technology for hotel booking recommendations. It explores how similar technologies might be adapted for use in schools, TAFE, and higher education institutions to provide personalized learning resources, course recommendations, or administrative tools. The study emphasizes the importance of human oversight, transparency, and intervention to ensure that AI recommendations are not misleading and that they align with ethical and educational values. It highlights the need for clear communication with end-users, including students, staff, and parents, about how AI influences decision-making processes. The scenario based questions prompts consideraiton of practical insights for educational leaders, policymakers, and IT professionals around responsibly integrating AI recommender systems while safeguarding fairness and trust in educational contexts.

Effective use of AI-driven recommender engines in education requires not only technological precision but also ethical oversight to ensure transparency, fairness, and trust for both students and staff.

Implementing AI-Driven Recommender Engines in Education

A higher education institution considered implementing a recommender engine to personalize learning resources and support student course selections. The institution partnered with an AI vendor, similar to XYZ in the TravelCo.com example provided in the Voluntary Standards, to develop an engine that would analyze students' academic records, browsing activities on the learning management system, and feedback to generate tailored content recommendations. During the development phase, educational leaders identified potential risks related to transparency and fairness. The recommender engine used several factors to rank content suggestions, including alignment with the institution's strategic goals and partnerships with specific educational resource providers. However, it became evident that students and staff might not understand how these recommendations were generated or that commercial interests could influence the results. For example, a particular course or resource could appear more prominently, not necessarily because it was the best fit for the student's needs, but due to institutional partnerships. Through a rigorous risk management process and applying the Voluntary AI Safety Standard, the institution prioritized human oversight and ethical intervention. They introduced a clear and prominent notice with each recommendation, explaining how the AI-generated the results and the factors influencing them. The institution also changed its messaging from suggesting the "best" or "most relevant" resources to stating that it provides tailored suggestions based on a range of factors, including academic performance, course requirements, and institutional priorities. This process demonstrated how active human intervention could prevent potential misinformation and ensure that AI technologies are used responsibly. The institution's approach reinforced transparency and fairness, helping maintain trust among students, educators, and parents. By highlighting how AI influences educational content and choices, the institution set a strong example for ethical AI governance in education.

Research topics

Research Questions

  • What human oversight mechanisms would you suggest, could be established to regularly audit AI-driven admissions and scholarship recommendations to ensure decisions are fair, transparent, and equitable?
  • How can the institution proactively communicate the role of AI in generating recommendations, including explaining the factors considered by the system and how human oversight helps maintain trust and integrity?
  • What processes would you suggest to the institution to put in place to ensure that human oversight is involved in reviewing AI-generated plagiarism reports, preventing unjust outcomes and supporting fair assessment practices?
  • What strategies can the institution adopt to ensure that AI-generated recommendations do not negatively impact students' life trajectories, particularly considering children's rights and promoting equitable access to educational opportunities?
  • Evaluate institutional practices that ensure human oversight in AI-driven educational recommendations to maintain fairness and equity.
  • Develop strategies to enhance transparency and build trust in AI-generated educational content and course recommendations.
  • Implement oversight mechanisms that support ethical data practices, informed consent, and privacy in AI systems used in education.

supplementary materials

Gather examples through document analysis and stakeholder interviews to identify existing or needed resources—such as governance templates, AI ethics lesson plans, and consultation tools—that support educational integrity and fairness in AI use within your school or initial teacher education program.
https://www.industry.gov.au/sites/default/files/2024-09/voluntary-ai-safety-standard.pdfhttps://rm.coe.int/artificial-intelligence-and-education-2nd-working-conference-provision/1680b314a3
This case study was written by Dr. Janine Arantes after reading Example 2: Facial recognition technology in AUstralia's Voluntary AI Safety Guidelines. This case study is therefore grounded in actual events as reported by these sources, and the original prompt is acknowledged.
Creative Commons Attribution-NonCommercial 4.0 International License (CC-BY-NC 4.0).
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Acknowledgement of Country We acknowledge the Ancestors, Elders and families of the Kulin Nation (who are the traditional owners of the land. where this work has bene predominantly completed. As we share our own knowledge practices, we pay respect to the deep knowledge embedded within the Aboriginal community and recognise their ownership of Country. We acknowledge that the land on which we meet, learn, and share knowledge is a place of age-old ceremonies of celebration, initiation and renewal, and that the Traditional Owners' living culture and practices have a unique role in the life of this region.