Graduate student Bismack Tokoli will be defending his thesis titled “ALIGNAgent: Adaptive Learner Intelligence for Gap Identification and Next-step guidance.”

Bismack Tokoli thesis defense

• Date: Tuesday, April 28
• Time: 2:30-3:30 p.m.
• Location: BARC 1123
• Current major: M.S. of data science
• Thesis committee chair: Dr. Luis Jaimes
• Committee members: Dr. Susan LeFrancois, Dr. James Dewey, and Dr. Ayesha Dina.

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 thesis, 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.

For more information, please contact Bismack Tokoli.