Three-level–four-module AI teaching paradigm operationalizing cognitive-information theory¶
Design · All designs
Evidence · no claims cited
Description¶
A cognitive informatics–driven teaching paradigm integrating a micro-meso-macro three-tier architecture with four closed-loop AI modules (cognitive diagnosis → case generation → explainable guidance → adaptive scheduling). The micro level handles task-level real-time feedback, the meso level course-level scheduling via reinforcement learning, and the macro level longitudinal ability transfer tracking. The article states it 'operationalizes cognitive-information theory' and addresses fragmentation of existing single-module AI educational systems through closed-loop integration.
Design Implications¶
Context¶
Requirements¶
- Integration of cognitive diagnosis outputs directly into the RL reward function to achieve 'state-scheduling' synergy
- Unified data flow and feedback mechanism across all three levels with global parameter updates at the end of each weekly cycle
Constraints¶
- Developed and tested specifically for postgraduate medical imaging education
Target Learners¶
- Postgraduate medical imaging students
Learning Goals¶
- Diagnostic image interpretation accuracy
- Skill transfer across imaging modalities and disease types
- Cognitive load management
Claims¶
Related Designs¶
Examples¶
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Key Sources¶
- An Jie, Leng Xi, Liu Yujie, Zhou Jie, Liang Yi, Chen Yuna, Qiu Shijun (2026). Rebuilding ability acquisition in medical imaging teaching: a multi-level AI-driven teaching paradigm based on cognitive-information theory. BMC Medical Education. https://doi.org/10.1186/s12909-026-09002-3
Page metadata
| Field | Value |
|---|---|
type |
design |
id |
three-level-four-module-ai-teaching-paradigm |
title |
Three-level–four-module AI teaching paradigm operationalizing cognitive-information theory |
description |
A cognitive informatics–driven teaching paradigm integrating a micro-meso-macro three-tier architecture with four closed-loop AI modules (cognitive diagnosis → case generation → explainable guidance → adaptive schedul... |
status |
review |
generated |
by process:settle-candidates on 2026-11-05 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — an-jie-2026 |
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