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

TL;DR

ALIGNAgent is a comprehensive adaptive learning framework that integrates knowledge estimation, skill-gap identification, and resource recommendation to personalize education effectively, demonstrated through high accuracy in real student data.

Contribution

The paper introduces ALIGNAgent, a novel multi-agent system that unifies diagnostic reasoning and resource recommendation for personalized learning, filling a gap in existing fragmented systems.

Findings

High accuracy in knowledge proficiency estimation (precision 0.87-0.90)

Effective skill-gap identification and targeted resource recommendation

Validated on authentic undergraduate course datasets

Abstract

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), a multi-agent educational framework designed to deliver personalized learning through integrated knowledge estimation, skill-gap identification, and targeted resource recommendation.ALIGNAgent begins by processing student quiz performance, gradebook data, and learner preferences to generate topic-level proficiency estimates using a Skill Gap Agent that employs concept-level diagnostic reasoning to identify specific misconceptions and knowledge deficiencies. After identifying skill gaps, the Recommender Agent retrieves preference-aware learning materials aligned with diagnosed deficiencies, implementing a continuous feedback loop where interventions occur before advancing to subsequent topics. Extensive empirical evaluation on authentic datasets from two undergraduate computer science courses demonstrates ALIGNAgent's effectiveness, with GPT-4o-based agents achieving precision of 0.87-0.90 and F1 scores of 0.84-0.87 in knowledge proficiency estimation validated against actual exam performance.