A multi-agent AI system uses Large Language Models (LLMs) and Bayesian Knowledge Tracing to covertly assess financial literacy in educational games.

In plain terms

Educational games, especially for complex topics like financial literacy, often struggle to assess what students truly learn without interrupting the game with tests. This challenge is known as "stealth assessment." Researchers developed an AI system called "Agentic BKT pipeline," which employs multiple large language models (LLMs) to observe students playing a financial literacy game. First, an LLM categorizes every player action; then, four specialized LLM "agents," each an expert in areas like investing or credit, analyze the player's behavior in their specific domain. These agents feed their observations into a statistical model called Bayesian Knowledge Tracing (BKT), which estimates a student's mastery level in each area, and an expert judge LLM combines these for an overall score. They found that this multi-agent system produced mastery scores that accurately reflected how much students learned and their scores on post-game tests, proving significantly more effective at predicting learning than a simpler single-LLM approach.