Date
Publisher
arXiv
Current AI-driven educational systems primarily rely on behavioural analytics, performance metrics, and content-level interactions to model learning. While these approaches provide useful indicators of learner activity, they are insufficient for representing the expert cognition used to interpret learner development, identify misconceptions, and make adaptive pedagogical decisions. Existing learning analytics dashboards largely visualise learner behaviour for human instructors, rather than embody expert cognition as a reasoning infrastructure for AI-native education.
This paper introduces the Expert Cognition Dashboard (ECD), a cognition-centred reporting infrastructure for AI Twin-driven education systems. ECD models expert cognition within dashboard systems, enabling learner behaviours to be interpreted through expert-like cognitive structures rather than treated as raw behavioural signals. The proposed framework transforms student interactions into interpretable cognition structures through AI Tutor analysis and multi-level dashboard aggregation. Its architecture organises cognition across three layers: individual cognition dashboards, class cognition dashboards, and AI Twin expert dashboards for cross-group reasoning and adaptive intervention.
Building on the AI Expert Feedback Ecology framework, ECD redefines dashboards as cognitive middleware that connects learner behaviours with AI-driven expert reasoning. By modelling interpretation, identity cognition, value recognition, misconception patterns, and learning tension, ECD enables AI Twins to identify recurring learner difficulties, generate adaptive tasks, and support personalised intervention. The paper argues for a shift from learning analytics toward Cognition Intelligence, positioning dashboards as foundational cognition infrastructures that embed expert reasoning into future AI-native education systems.
Study at a glance
- Question: How can educational dashboards be designed to model expert thinking and support AI-driven tutoring and teaching?
- Setting and data: Not stated in the paper
- Method: Conceptual framework and prototype interface design
Why it matters
Educational AI systems track surface-level student behaviors but lack the cognitive structures experts use to interpret learning. Dashboards typically display raw metrics for humans rather than supporting expert-like reasoning for AI-native education.
Key findings
- The Expert Cognition Dashboard framework models expert-like thinking structures—such as identity, interpretation, tension, values, and misconceptions—rather than displaying only surface behavioral metrics.
- Dashboards can function as cognitive middleware across three interconnected levels: individual student dashboards, class-level aggregation dashboards, and ecosystem-wide AI Twin expert dashboards.
- The closed-loop AI Expert Feedback Ecology connects AI Tutor analysis, dashboard aggregation, and AI Twin intervention planning into a continuous cycle of adaptive support.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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