The LEARN framework: five components for neuroscience-informed, PBL-based GAI assessment design

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Evidence · no claims cited

Description

The LEARN framework is a conceptual, neuroscience-informed model comprising five interrelated components: Lifelong Learning, Engagement, Active Processing, Reflection, and Neuro-based Design. It synthesises cognitive/educational neuroscience, constructivist and problem-based learning theory, and literature on ethical engagement with GAI, positioning GAI 'as a cognitive scaffold rather than as a cognitive substitute'. Each component is enacted through PBL practices, explained by neurocognitive mechanisms, and operationalised through assessable learning traces such as portfolios and AI-use disclosure.

Design Implications

Context

Requirements

• Instructional design must align with neurocognitive principles such as cognitive load management, reward-based motivation, and social connectedness

Constraints

• The framework is conceptual; the authors state that neuroscience principles 'are not presented as empirical outcomes of the LEARN framework itself, but as theoretical foundations'

Target Learners

• higher education students

Learning Goals

• deep, self-directed, and reflective learning
• critical evaluation of AI outputs
• ethical self-regulation in AI use

Claims

Related Designs

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

Page metadata

Field	Value
type	design
id	learn-framework-gai-assessment
title	The LEARN framework: five components for neuroscience-informed, PBL-based GAI assessment design
description	The LEARN framework is a conceptual, neuroscience-informed model comprising five interrelated components: Lifelong Learning, Engagement, Active Processing, Reflection, and Neuro-based Design.
status	review
generated	by process:settle-candidates on 2026-10-11
trust	unverified — no one has confirmed this page's content
sources	1 — lorna-uden-gwo-jen-hwang-2026

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