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Agentic Knowledge Tracing: A Multi-Agent LLM Architecture for Stealth Assessment of Financial Literacy in Serious Games
Researchers propose the Agentic BKT pipeline, a multi-agent LLM architecture that stealthily assesses financial competencies from open-ended gameplay events without disrupting the learning experience.
Assessing financial literacy during gameplay without disrupting the learning experience remains a key challenge in serious games for education. A new paper posted on arXiv on June 25 proposes the Agentic BKT pipeline, a multi-agent large language model architecture designed for stealth assessment of financial competencies from open-ended gameplay events. The pipeline processes events from a 2D platformer serious game aligned with the OECD/INFE financial literacy framework through four phases, enabling real-time evaluation without interrupting gameplay. The paper is available on arXiv under ID 2606.25358 and contributes a novel approach to integrating LLMs into educational game assessment, particularly for financial literacy education.
Why it matters
This research introduces a new multi-agent architecture paradigm for stealth assessment in educational games, potentially advancing evaluation methods in financial literacy education.