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.
Mahmoud Elkhodr, E. Gide· Technology, Knowledge and Le...· 0 citations
Global population ageing is intensifying the demand for scalable approaches to elderly mental health support. Existing care models remain largely episodic and insufficient for continuous monitoring, concurrent at-risk status assessment, and coordinated response. This paper presents the AI-Integrated Mental Health Support System (AIMHSS), a layered framework that combines Artificial Intelligence, Internet of Things, Virtual Reality, and blockchain technologies to support elderly mental health monitoring and intervention planning. The framework comprises four functional layers, namely Continuous Sensing, Predictive Intelligence, Adaptive Intervention, and Stakeholder Engagement, supported by cross-cutting trust, data integrity, and ethical governance mechanisms. Technical feasibility was examined through a two-part offline evaluation using two publicly available datasets containing authentic observational data. First, a Random Forest classifier of concurrent at-risk status was trained and evaluated on the OASIS-2 longitudinal clinical dataset under leakage-safe subject-isolated validation, achieving an AUROC of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.823 \pm 0.038$$\end{document}, a sensitivity of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.664 \pm 0.079$$\end{document}, and a specificity of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.845 \pm 0.090$$\end{document}. Second, Fitbit wearable activity and sleep traces were used for trace-driven orchestration testing with a fixed synthetic clinical baseline, enabling behavioural monitoring and tiered intervention triggering across a 12-user cohort comprising 331 user-days. These evaluations establish architecture-level and pipeline-level feasibility for integrated elderly mental health support and define a clear next step of prospective multi-modal validation within unified clinical cohorts.
This paper conceptualises the SAGE Defend step, the sixth stage of the Structured AI-Guided Education framework, as a format-agnostic assurance checkpoint for AI-integrated higher education assessment, and proposes a three-class assurance-task typology, an epistemic matching framework, and six design principles for embedding SAGE Defend within assessment sequences.
Mahmoud Elkhodr, E. Gide· Frontiers in Education· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
