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.

Overview

DISCUSSION QUESTIONS

This case study highlights the critical role of human oversight and ethical governance when integrating AI-driven recommender engines in educational settings. It underscores the importance of transparency and clear communication with end-users to avoid misleading or deceptive practices and ensure AI systems align with institutional values of fairness and trust.
Discussion Questions
Recommender engines, powered by artificial intelligence, are widely used in commercial settings to provide personalized content and suggestions. In education, these technologies could enhance learning experiences by offering tailored educational resources, guiding course selections, or optimizing administrative processes. However, the use of AI-driven recommender engines also raises ethical and transparency challenges, particularly concerning how recommendations are generated and presented to students, staff, and parents. Drawing on the example of TravelCo.com's recommender engine, this case study prompts the exploration of how educational institutions can apply human oversight, ethical governance, and intervention to manage the risks associated with these technologies. The case aims to highlight the importance of maintaining transparency, fairness, and trust in AI-driven systems and apply these considerations within educational contexts via a series of scenario based discussion questions.
Keywords
recommender engine, ethical AI, education, transparency, AI governance, human oversight
Learning Objectives
Practical Applications:
This case study can be integrated into professional development workshops for educators and administrators, providing scenarios to explore effective human oversight in AI-driven recommendation systems.
It can also support curriculum development in courses on AI ethics and governance, emphasizing transparency, fairness, and responsible data practices in educational settings. Additionally, the scenarios can be used in policy training sessions to highlight the importance of aligning AI technologies with institutional values and safeguarding students' rights.
  • Ensuring Fairness in High-Stakes Decisions: Scenario: Your institution considers using a recommender engine to support admissions management, including scholarship decisions. However, there is concern that the algorithm may inadvertently prioritize candidates from certain backgrounds due to biases in historical data. Question: 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?
  • Maintaining Trust Through Transparent AI Practices: Scenario: A parent approaches the institution to ask how AI-generated course recommendations influence their child's academic path from Yr 12 to university. They express concerns about potential commercial biases and the accuracy of the AI's suggestions. Question: 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?
  • Mitigating Risks of AI in Assessment and Academic Integrity: Scenario: Your university has integrated a recommender engine with a plagiarism detection tool to provide tailored academic resources to students. There are concerns that false positives could unfairly penalize students, impacting their assessments and academic record. Question: 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?
  • Supporting Students' Rights and Equity: Scenario: An analysis reveals that your instituion's AI system recommendations may unintentionally steer disadvantaged students toward less challenging courses, potentially limiting their academic and career opportunities. Question: 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?
  • Balancing Personalization with Ethical Responsibility: Scenario: You discover that the recommender engine uses browsing activity data to tailor course suggestions without students fully understanding how their data is used. Question: YOu need to suggest ways the institution can implement human oversight to ensure data collection practices are transparent, that students provide informed consent, and that AI-generated recommendations respect students' privacy and autonomy. What would you suggest?
  • 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

Within the context of your own school or intital teacher educaiton program, consider including: 
Additional resources could include governance framework templates, lesson plans on AI ethics, and tools for conducting stakeholder consultations that consider both educational integrity and fairness in AI recommendations.
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).
AI Governance for Education: case studies
CONTACT
janine.arantes@vu.edu.au
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