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. The study responds to a verification gap identified in earlier SAGE research, in which process documentation and AI interaction logs were found to support transparency but not, by themselves, to verify individual ownership of reasoning in group-based AI-integrated submissions. Adopting a design-informed conceptual approach grounded in design-based research principles, the paper integrates a multi-year programme of empirical SAGE studies, a structured synthesis of the assurance-task literature, and diagnostic observations from three Defend-proximate assessment implementations across undergraduate and postgraduate units at Central Queensland University. It distinguishes between assurance tasks that directly require students to demonstrate reasoning or performance, controlled assurance conditions that restrict the assessment environment, and corroborative assurance signals that provide corroborating but non-stand-alone evidence. On this basis the paper proposes a three-class assurance-task typology, an epistemic matching framework, and six design principles for embedding SAGE Defend within assessment sequences. It further argues that assurance should be distributed across the assessment sequence of a unit, so that each learning outcome is verified at a point and intensity proportionate to its stakes rather than concentrated in a single terminal examination. The paper frames this response as assurance by design, an approach that, echoing the established engineering principles of security by design and privacy by design, builds verification into the assessment sequence rather than appending it after the fact, and it names the compounding cost of the retrofitted alternative as assurance debt. Rather than presenting SAGE Defend as an oral examination model or claiming empirical validation of a single format, the paper positions Defend as a design principle through which educators can align verification tasks with the cognitive, professional, or technical competency being assessed. The contribution is therefore conceptual and practice-informed, offering a structured basis for the future empirical validation of specific Defend formats across disciplines, cohorts, and delivery modes.
Mahmoud Elkhodr, E. Gide· Frontiers in Education· 0 citations
