﻿Implementing AI-Driven Recommender Engines in Education﻿

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. This case study offers practical insights for educational leaders, policymakers, and IT professionals on responsibly integrating AI recommender systems while safeguarding fairness and trust in educational contexts. 
Incident: Released as part of the DISR examples 
Case Study Construction: March 2025

﻿Should we have Facial Recognition Technology in Educational Settings?﻿

Hands Off Learning

This case study examines the ethical and practical challenges of implementing facial recognition technology (FRT) in schools, TAFE, and higher education institutions, emphasizing the critical role of human oversight and intervention. It draws on the example of EcoRetail’s decision-making process to explore how educational institutions can apply robust governance frameworks to assess AI technologies intended to enhance safety and streamline administrative tasks. The study demonstrates how proactive stakeholder engagement, rigorous risk assessment, and a focus on ethical considerations led to the decision not to implement FRT. It emphasizes the need for specific frameworks that safeguard the rights of minors, ensure the privacy of all students, and maintain safe and supportive working conditions for teachers and staff. This narrative offers practical insights for school leaders, educators, policymakers, and IT professionals on how to integrate AI responsibly while balancing innovation with ethical and legal responsibilities in educational contexts.﻿
Incident: Released as part of the DISR examples 
Case Study Construction: March 2025

This case study investigates the dangers of diminishing human control in AI-driven educational environments. Drawing on real-world practices, the fictionalised scenario follows a K–12 school system that implemented AI systems to handle student engagement tracking, curriculum delivery, and even behavioural interventions. While initially welcomed as time-saving, these systems gradually replaced teacher judgement and student voice. As the AI tools became more autonomous, opportunities for critical reflection and human intervention disappeared. This case reinforces the need for meaningful human control at all stages of the AI lifecycle—from design and deployment to review and retirement.

This case study explores how automation bias can creep into assessment practices when educators rely too heavily on AI-generated feedback or marks. Based on widespread trends and current research, this fictionalised scenario follows a tertiary institution where automated essay scoring and feedback tools were introduced to reduce marking load. Over time, staff became increasingly dependent on these tools—often accepting AI outputs as final. The result: overlooked nuances, unfair marks, and reduced opportunities for students to receive formative, human-centred feedback. This case highlights the need to train educators in critically managing AI interactions to avoid over-trusting algorithmic decisions.

more coming soon

This case study explores how the role of educators can be undermined—or overlooked—when artificial intelligence systems are deployed in education without clear role definitions or professional boundaries. In this fictionalised but research-informed scenario, teachers are expected to act on AI-generated insights related to student progress, behaviour, and mental health, yet are given no training, authority, or support to challenge or contextualise these insights. The case calls for institutions to define and empower educators' roles in AI-supported environments, ensuring that their professional expertise remains central in both learning and wellbeing decisions.