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LEA

Product or Programme · All products and programmes
Evidence · 2 claims (2 mixed) · 2 studies (1 review, 1 design), q2 · 0 of 2 report an effect size · 2 claims rest on one study

Description

LEA is an adaptive AI study-aid platform that infers higher-education learners' cognitive, motivational, and engagement states from behavioural and performance indicators to guide scaffolding.

Components

  • Integrated Psychological Framework: cognitive load, ZPD, engagement, and motivation theories grounding LEA's learner-state modelling: LEA's adaptive mechanisms are grounded in four frameworks — Cognitive Load Theory, Zone of Proximal Development and constructivist learning theory, engagement theory (Self-Determination Theory and Situated Expectancy-Value Theory), and motivation and self-efficacy theory (Bandura's Social Cognitive Theory and Zimmerman's self-regulation model). Together these "define the Integrated Psychological Framework that informs LEA's learner-state modelling and scaffolding decisions via the Agent Orchestrator." Cognitive load is computed as a weighted combination of task difficulty, recent accuracy, fatigue, and task type; ZPD is the mean accuracy across the last four responses targeting a 50–80% performance band; motivation state is inferred from persistence, sentiment, and performance trends. (Rumble et al. (2026))

Claims

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Key Sources

  • Rumble, T., Zarrin, J., Lovell, P. G., Falconer, R. (2026). Learning Engagement Assistant (LEA): Cross-Course Scalability and Classroom Evaluation of an Agentic AI Tutoring System. arXiv. https://arxiv.org/abs/2607.13370. doi:10.48550/arxiv.2607.13370
Page metadata
Field Value
type product
id lea
title LEA
description LEA is an adaptive AI study-aid platform that infers higher-education learners' cognitive, motivational, and engagement states from behavioural and performance indicators to guide scaffolding.
status review
product_kind software
generated by process:settle-candidates on 2026-10-11
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