Transparency & Explainability in AI Decisions
Nobody Told Us
This case study explores what happens when educational institutions fail to inform students, families, and staff about the presence and role of AI in learning environments. In this fictionalised but research-informed scenario, a secondary school quietly integrated AI into its assessment, attendance, and behavioural management processes—without notifying the school community. When issues emerged, students and parents were shocked to learn decisions had been partially automated. The case highlights the ethical and legal imperative of disclosure, informed consent, and transparent communication when AI is used in educational settings.
Did a Human Write This?
It Just Said No
This case study explores the ethical, pedagogical, and trust-related implications of using AI-generated content in education without clearly labelling its origin. In this fictionalised scenario, a university unknowingly circulated AI-authored feedback and learning materials without disclosing their source. Students and staff were misled, and confusion around authorship compromised academic integrity. The case highlights the growing need for transparent labelling of AI-generated content across all educational touchpoints, including feedback, teaching resources, assessments, and policy documents.
This case study explores the risks and frustrations that arise when AI-driven decisions in education are opaque and unexplained. In this fictionalised scenario, a TAFE institution uses an AI-based admissions and placement system that issues seemingly arbitrary decisions—without providing applicants or staff with reasons. As complaints increase, trust in the system diminishes. This case highlights the need for explainability frameworks, accessible communication strategies, and a human-first approach to AI deployment in education.