Ethical AI & Dispute Resolution Mechanisms
This case study explores the ethical and procedural gaps that emerge when students and educators are given no avenue to contest or appeal AI-generated decisions. Set in a senior secondary college, the fictionalised scenario describes the rollout of an AI-powered assessment moderation tool. Despite its promise to ensure consistency and fairness, the tool began generating questionable grades and behaviour alerts—without clear pathways for students or teachers to challenge the outcomes. This case highlights the fundamental need for transparent contestability, human review, and accountability frameworks that uphold due process in education systems using AI.
This case study investigates how well-meaning AI systems can produce discriminatory outcomes when they are designed or deployed without a critical understanding of social context, structural bias, and inclusion. Set in a culturally and linguistically diverse school, the fictionalised—but research-informed—scenario traces the rollout of an AI-powered career pathways tool. Despite aiming to promote equity, the system reinforced stereotypes about gender, race, and disability in its recommendations. The case illustrates the importance of equity audits, inclusive datasets, and collaborative design processes to reduce harm and create AI systems that genuinely support all learners.
This case study explores how AI-driven decisions—while efficient and scalable—can contradict the principles of educational equity and justice if not designed and implemented with care. Set in a national education reform initiative, this fictionalised case illustrates how a centralised AI tool designed to automate school resourcing and teacher allocation inadvertently entrenched inequalities in low-income, remote, and historically underfunded communities. Despite being data-informed, the system failed to acknowledge structural disadvantage, cultural context, and Indigenous self-determination. This case highlights the need for AI systems to be accountable to justice—not just to performance metrics.