Expert Cognition Dashboard: From Learning Analytics to Cognition Intelligence in AI-Driven Education

Synthesis: Current AI-driven educational systems primarily rely on behavioral analytics and performance metrics, lacking the ability to model expert cognition used to interpret learner development and identify misconceptions. This paper introduces the Expert Cognition Dashboard (ECD), a cognition-centered reporting infrastructure for AI Twin-driven education systems.

Key Findings

This paper proposes a paradigm shift from Learning Analytics to Cognition Intelligence, introducing the Expert Cognition Dashboard (ECD) as a three-layer cognitive middleware for AI-driven education. The ECD architecture models expert cognition — interpretation, identity cognition, value recognition, misconception patterns, and learning tension — across individual, class, and AI Twin expert levels. Rather than treating learner behaviors as raw signals, ECD transforms student interactions into interpretable cognition structures through AI Tutor analysis and dashboard aggregation. This advances the Learning Analytics paradigm beyond simple visualization toward embedded reasoning infrastructure, positioning dashboards as foundational cognition infrastructures in AI-native education systems. The framework aligns with efforts in Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning to make learner modeling interpretable, and extends Adaptive Learning by incorporating expert-like cognitive reasoning into adaptation decisions. The cognition-layer approach complements PersonaVLM: Long-Term Personalized Multimodal LLMs architectures that store longitudinal learner states, and intersects with Balancing Teacher and Student Agency: Co-Orchestration Tool Design Supporting Real-Time Dynamic Pairing by providing the cognitive infrastructure for AI Twins to support adaptive intervention and personalized scaffolding.

What this means for practice

• Instructors. Require dashboards to report interpretation, misconception patterns, and learning tension rather than only engagement and performance counts, since behavioral signals alone provide limited insight into expert cognition.
• Learners. Treat an AI-generated cognition report about you as an interpretation to contest, not a verdict, and ask what interaction evidence produced it.
• Software developers. Build uncertainty representation, human review, and learner contestability into cognition dashboards before surfacing inferences to instructors or learners, because over-interpretation is the framework's own stated failure mode.
• Administrators. Set AI Governance for long-term cognition modeling before piloting: define what is modeled, who can access the resulting interpretations, and how learners can challenge or revise them.

Limitations

• ECD is a conceptual and design framework: the authors state it "has not yet been validated through a full empirical deployment", so its contribution is theoretical and architectural rather than empirical.
• The cognition structures (interpretation, identity cognition, value recognition, cognitive tension, misunderstanding) are intended as initial conceptual categories rather than a fixed taxonomy, and the paper argues they cannot be reliably inferred from behavioral data alone.
• The framework flags over-interpretation risk directly: AI-generated cognition reports may appear precise or authoritative while being uncertain, incomplete, or biased, and the paper proposes uncertainty markers and human correction without testing them.
• Ethics and governance for longitudinal cognition modeling — privacy, consent, data ownership, interpretive accountability, and learner agency — are named as open concerns that future systems must resolve.

Citation

Annie Yuan (2026). Expert Cognition Dashboard: From Learning Analytics to Cognition Intelligence in AI-Driven Education. arXiv preprint (cs.HC).