A Closed-Loop Personalized Learning Agent Integrating Neural Cognitive Diagnosis, Bounded-Ability Adaptive Testing, and LLM-Driven Feedback

2025-10-26

AI Summary

Existing personalized learning approaches treat student modeling, item selection, and feedback generation as disjoint components, resulting in coarse-grained student representations, adaptive assessments that neglect diagnostic posterior inference, and non-actionable feedback. To address these limitations, we propose EduLoop-Agent, an end-to-end personalized learning agent that establishes a closed-loop “diagnosis–recommendation–feedback” paradigm. It employs a neural cognitive diagnosis model (NCD) for fine-grained, interpretable knowledge-tracing; introduces bounded-effort cognitive adaptive testing (BECAT) to dynamically generate diagnostic-oriented items; and leverages large language models (LLMs) to produce actionable, weakness-targeted feedback. Evaluated on the ASSISTments dataset, EduLoop-Agent significantly improves question response prediction accuracy, item recommendation relevance, and feedback utility. To our knowledge, it is the first framework to achieve joint optimization across all three stages, realizing a fully integrated, closed-loop personalized learning system.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Multiagent Learning
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

As information technology advances, education is moving from one-size-fits-all instruction toward personalized learning. However, most methods handle modeling, item selection, and feedback in isolation rather than as a closed loop. This leads to coarse or opaque student models, assumption-bound adaptivity that ignores diagnostic posteriors, and generic, non-actionable feedback. To address these limitations, this paper presents an end-to-end personalized learning agent, EduLoop-Agent, which integrates a Neural Cognitive Diagnosis model (NCD), a Bounded-Ability Estimation Computerized Adaptive Testing strategy (BECAT), and large language models (LLMs). The NCD module provides fine-grained estimates of students' mastery at the knowledge-point level; BECAT dynamically selects subsequent items to maximize relevance and learning efficiency; and LLMs convert diagnostic signals into structured, actionable feedback. Together, these components form a closed-loop framework of ``Diagnosis--Recommendation--Feedback.'' Experiments on the ASSISTments dataset show that the NCD module achieves strong performance on response prediction while yielding interpretable mastery assessments. The adaptive recommendation strategy improves item relevance and personalization, and the LLM-based feedback offers targeted study guidance aligned with identified weaknesses. Overall, the results indicate that the proposed design is effective and practically deployable, providing a feasible pathway to generating individualized learning trajectories in intelligent education.

Problem

• Integrates cognitive diagnosis and adaptive testing for personalized learning
• Provides fine-grained student mastery estimates at knowledge-point level
• Generates actionable feedback using LLMs based on diagnostic signals

Innovation

• Neural Cognitive Diagnosis for fine-grained mastery assessment
• Bounded-Ability Adaptive Testing for personalized item selection
• LLM-driven feedback converting diagnostics to actionable guidance