Institutional recommendations: shift from detection to process-oriented assessment, transparent AI-use protocols, and revised integrity policies¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article issues three strategic recommendations for institutions integrating GAI into assessment. First, 'institutions should transition from a reliance on detection-based approaches to a process-oriented philosophy', structuring assessments around learning processes and iterative development. Second, institutions should implement transparent AI-use protocols requiring students to document, disclose, and reflect on GAI use. Third, integrity policies should be revised so that responsible, well-documented AI integration is recognised as part of emerging digital and learning literacies rather than framed as misconduct.

Design Implications¶

Context¶

Requirements¶

• Assessment structures that focus on learning processes, cognitive engagement, and iterative development rather than verification of final products

Constraints¶

• The article notes detection-based responses 'remain limited in reliability', motivating the pedagogical rather than technical approach

Target Learners¶

• higher education students

Target Learning Goals¶

• academic integrity
• critical AI literacy
• responsible AI use

Affordances¶

• Learn Framework Gai Assessment

Related Strategies¶

• Stage GAI use in assessment: permit exploration, restrict independent reasoning, require justification
• Renew writing assessment and integrity practice through AI-literacy instruction, process-focused assessment design, and contextual detector training
• Address generative AI in higher education through assessment redesign and proportional reflective practices rather than surveillance or punitive controls
• Move beyond generic AI policies to method-based ethical policies and process-oriented assessment
• Recalibrate detection as a supplementary layer within multi-layer integrity governance rather than its foundation
• Provide explicit instructional boundary cues, process-oriented assessment, and peer modeling of verification to frame responsible GenAI use
• Position AI detection tools as one component within a broader institutional decision-making process, with regularly updated benchmarks
• Strategic steps to move beyond informal AI innovation: recognise redesign in workload models, embed innovation in promotion criteria, set institutional AI principles, redesign assessment, and fund scaling pilots
• Redesign assessment toward progressive, authentic, and reflective methods rather than intensive paperwork

Examples¶

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Key Sources¶

• Lorna Uden · Gwo-Jen Hwang. (2026). The LEARN framework for responsible use of generative AI in education: a neuroscience-informed model for problem-based learning. Journal of Computers in Education. https://doi.org/10.1007/s40692-026-00398-x