Aug 2026· Frontiers in Education· 1 citation· 35 references
TL;DR
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.
Abstract
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.
A tripartite feedback framework is proposed that distinguishes three analytically distinct levels: low-level structural and presentational feedback, intermediate-level factual content validation, and high-level critical evaluation and synthesis, and examines current technological capabilities and limitations at each level.
A framework for AI-resilient assessment that shifts evaluation from product quality to demonstrable reasoning, decision-making, and ownership of learning is proposed, Illustrated primarily through health sciences education, with wider relevance to professional and practice-oriented disciplines.
Dragan Nikolić, M. Basta Nikolić· Frontiers in Artificial Inte...· 0 citations
Generative artificial intelligence (GenAI) has unsettled a central premise of higher-education assessment: that the quality of a submitted artefact is a sufficiently trustworthy proxy for the competence of the named student. This problem is acute in engineering, where text, code, calculations, models, design rationales and technical reports can increasingly be generated or transformed by general-purpose and specialised artificial intelligence (AI) systems. This critical narrative review examines how assessment in higher engineering education should be reconceptualised when GenAI is simultaneously a learning resource, an emerging professional tool and a source of construct-irrelevant assistance. Literature published from 1 January 2018 to 27 June 2026 was searched, with earlier foundational assessment research retained when conceptually necessary. Evidence was synthesised around assessment validity, engineering task vulnerability, authentic and process-based assessment, AI literacy and evaluative judgement, academic integrity and detection, feedback and grading, equity, and programme-level governance. The literature indicates that neither blanket prohibition nor unrestricted adoption provides a defensible general solution. Authenticity alone is also insufficient, because realistic take-home tasks can remain highly susceptible to undisclosed AI influence. A more robust approach separates two complementary purposes: protected evidence of independent competence in threshold and safety-relevant capabilities, and AI-integrated evidence of professional judgement in tasks where responsible tool use is itself an intended outcome. These forms of evidence should be triangulated through staged work, oral explanation, live demonstration, provenance, and programme-level assessment mapping. AI detectors are too unreliable and potentially inequitable to serve as stand-alone evidence of misconduct, while AI-assisted feedback and grading show promise but require human oversight, particularly for complex engineering work. The review proposes a Dual-Assurance Assessment Architecture as an evidence-derived organising model rather than a validated framework. Its central implication is that assessment reform should prioritise the validity of inferences about student capability, not merely the detectability of AI use.
Unknown authors· Asian Journal of Education a...· 0 citations
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
The emergence of Generative Artificial Intelligence (GenAI) technology is changing the ways of assessment and feedback procedures in higher education institutions by allowing for a more flexible and personalized process of learning. However, the effective use of GenAI requires pedagogic design, governance, and implementation strategies. The current study will discuss the institutional prerequisites to implement GenAI in assessment and feedback procedures (Activity A2.2 of the Erasmus+ HEGenAI project). Empirical data were gathered using structured questionnaires completed by educators (n=61) and students (n=254). The descriptive approach involving frequencies, percentages, means, and standard deviations helped to investigate current usage of AI-supported assessment procedures, institutional requirements for GenAI adoption, ways of implementing GenAI, educational advantages of GenAI, and associated risks. The results showed that while GenAI is used extensively to facilitate assessment-related processes, the usage remains predominantly informal and non-institutionalised. It is argued that there is a need to use licensed GenAI solutions, to train educators, to redesign assessment processes, and also to develop an appropriate governance framework. Academic integrity, critical thinking, and AI dependency emerged as the most important risks requiring human attention.
Unknown authors· International Journal of Adv...· 0 citations
Higher education is at a critical juncture as generative artificial intelligence (AI) redefines assessment practices. This article offers a conceptual framework illustrating how authentic AI‐enhanced assessments (AAA) can support the reclamation of higher education's core mission: developing critical thinking, creativity, ethical reasoning, and meaningful real‐world problem‐solving. With the advent of AI, current assessment practices may not adequately evaluate the deep interdisciplinary competencies required for today's complex professional environments. Instead of resorting to increased surveillance or outright bans on AI, this article contends that institutions should embrace AI as a supportive tool to reinforce authentic assessment strategies. The AAA framework emphasises not only the final output but also the iterative learning process—integrating reflective practice, student agency, and responsiveness to unexpected twists that compel students to think on their feet. The article demonstrates that AI‐enhanced authentic assessments—ranging from interactive tasks to multi‐stage applied projects—can transform evaluation across disciplines by supporting the development of a solid theoretical foundation and the practical, adaptable competencies demanded by modern professional practice.
Dilani Gedera· Future in Educational Resear...· 0 citations
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