Eliciting Student Authority over AI Feedback: The AI as Critic Mechanism in SAGE-Based Systems Analysis and Design Education
The integration of Generative AI into higher education has shifted the central assessment problem from whether students may use AI towards how human judgement over AI output can be preserved and evidenced. This article examines the AI as Critic mechanism within the Structured AI-Guided Education (SAGE) framework, in which students first create their own artefacts and then respond to AI review of that work. An artefact-based qualitative analysis was conducted on eight de-identified group submissions from a postgraduate Systems Analysis and Design assessment delivered across five Australian offerings, yielding forty unit-test records together with the associated AI critique logs, literature syntheses, and reflections. The analysis was organised through an Apply–Critique–Synthesise sequence. The analysed group submissions documented rejection of AI suggestions on principled grounds of scope, platform constraints, risk, compliance, and phasing rather than automatic deference to AI authority. Literature syntheses and reflections were found to contain critiques absent from AI-generated summaries, while accessibility was attended to only when explicitly cued. The findings are interpreted as evidence of what students can demonstrate under scaffolded conditions, and the limits of that evidence are made explicit. The contribution is artefact-based evidence that the AI as Critic mechanism can make human authority over AI suggestions observable and assessable, while the complementary question of how such judgement is verified as durable individual competence is left to the assurance strand of the SAGE program.