Aug 2026· AI in Education· 0 citations· 30 references
TL;DR
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.
Abstract
Assessment feedback on complex written reports remains one of the most persistent and resource-intensive challenges in higher education. However, no principled framework exists for deciding which feedback tasks might appropriately involve artificial intelligence and which must remain human responsibilities. This paper addresses that gap by proposing a tripartite feedback framework that distinguishes three analytically distinct levels: low-level structural and presentational feedback, intermediate-level factual content validation, and high-level critical evaluation and synthesis. Grounded in established feedback theory, including Hattie and Timperley’s feedback model and Boud and Molloy’s sustainable feedback design principles, the framework provides pedagogically justified criteria for allocating tasks between AI systems and human assessors, rather than automating whatever technology can technically perform. Five non-negotiable boundary principles govern any AI involvement at the intermediate level, preserving human oversight, academic accountability, and assessment integrity. This paper examines current technological capabilities and limitations at each level, proposes a phased implementation pathway with explicit human-in-the-loop requirements, and addresses implications for feedback literacy, student agency, equity, and security. A comprehensive mixed-methods evaluation design specifying the evidence required for empirical validation is also presented. The framework’s contribution lies not in prescriptive solutions but in providing structured categories, explicit boundary conditions, and validation criteria to guide context-sensitive institutional decision-making about AI integration in assessment.
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
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
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
ACCELERATE: Assessment Principles for Best Practice (2025) updates the foundational American Association for Higher Education (AAHE) principles with contemporary emphasis on equity, collaboration, and transparency. This pilot study documents the development of an AI chatbot providing programmatic assessment guidance grounded in ACCELERATE principles, examining which principles translate effectively to AI-mediated coaching versus those requiring direct human involvement. Using retrospective analysis of seven authentic challenges from Year 1 programmatic assessment evaluations at one institution, we examined AI-mediated coaching responses for evidence of effective principle translation and structural tensions that emerge when human-centered values encounter algorithmic operationalization. Preliminary findings suggest that six principles may translate effectively to AI support, while four (Collaborative/Co-Creative, Energized by Expertise, Responsiveness-Oriented, and Enduring/Evolving) carry structural tensions that appear to resist technological resolution. We propose a framework that positions AI as a tool for technical work, while humans remain guarantors of relational work in principled assessment practice.
Stavros Hadjisolomou, Rita W. El-Haddad· Intersection: A Journal at t...· 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
Online assessment offers flexibility, scalability and rapid feedback, yet generative artificial intelligence (GenAI) has weakened the assumption that a submitted product reliably represents a student’s independent learning. This critical essay argues that higher education should shift from product-centred assessment to a process-plus-performance model that makes reasoning, evidence selection, revision, feedback use, ethical judgement and intellectual ownership visible. Drawing on process-writing theory, constructivism, constructive alignment, assessment for learning, self-regulated learning, the Community of Inquiry, authentic assessment and Universal Design for Learning, it examines the limitations of take-home essays, remote multiple-choice tests, AI detection and surveillance. It proposes staged assignments, critical evaluation and transparent disclosure of AI use, authentic and localised tasks, reasoned multiple-choice questions, brief oral verification and targeted secure assessment at key progression points. Because process records can also be fabricated, trustworthy judgement should triangulate written development with live explanation or practical performance. This layered approach supports academic integrity without treating surveillance as the default, while strengthening critical thinking, AI literacy, inclusion and professional accountability.
S. Munjita, Phebby Mwangala Kasimba· Journal of Digital Pedagogy· 0 citations
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