ALIGNAgent: Adaptive Learner Intelligence for Gap Identification and Next-step guidance

ALIGNAgent is a multi-agent LLM-based framework that integrates knowledge estimation, skill-gap identification, and personalized resource recommendation for undergraduate computer science courses, achieving 0.87-0.90 precision in proficiency estimation validated against exam performance. The system uses diagnostic reasoning to identify misconceptions and provides targeted interventions before students advance to subsequent topics.

Personalized learning systems have emerged as a promising approach to enhance student outcomes by tailoring educational content, pacing, and feedback to individual needs. However, most existing systems remain fragmented, specializing in either knowledge tracing, diagnostic modeling, or resource recommendation, but rarely integrating these components into a cohesive adaptive cycle. In this paper, we propose ALIGNAgent (Adaptive Learner Intelligence for Gap Identification and Next-step guidance),

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