Prompting LLMs for Game Learning Analytics: A Case Study with Computer Science Students

• Fabrizio Honda UEA / UFAM
• Fernanda Pires UEA
• Elaine Harada UFAM
• Marcela Pessoa UEA

DOI: https://doi.org/10.5753/educomp.2026.18681

Resumo

The mandatory inclusion of computer science in schools requires graduates capable of acting as learning designers. The development of educational games can aid this process by allowing the implementation of Game Learning Analytics (GLA) techniques. However, modeling data to list GLA variables is complex, motivating the use of Large Language Models (LLMs). This case study examines how a technique inspired by the chain-of-thought approach, combined with the inclusion of more game data in the prompt, affects the quality of GLA structures. Results indicate that the method is relevant; however, students struggle to guide the LLM. Furthermore, the excess of data in the prompt may have led the model to ignore important information.

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