ChatGPT, Gemini or DeepSeek? An empirical study in Game Learning Analytics - Honda, Fabrizio; Pessoa, Marcela; Pires, Fernanda; Oliveira, Elaine H. T. (Anais do Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames); 2025: Anais do XXIV Simpósio Brasileiro de Jogos e Entretenimento Digital, 2025)
Introduction: Generative artificial intelligence (GenAI), through large language models (LLMs), is an emerging area that has transformed the way of solving problems in several areas of knowledge. This technology can also offer solutions to challenges still open in Game Learning Analytics (GLA). Modeling data for GLA and implementing data capture and analysis techniques in educational games to identify evidence of learning are not trivial tasks. The use of LLMs can bring benefits; however, the wide variety of models available, which work in different ways, makes choosing the most appropriate model challenging. Objective: In this context, this article presents a comparative analysis of how LLMs (ChatGPT, Gemini, and DeepSeek) perform activities related to GLA, including the generation of templates for data capture in the GLBoard model under zero, one, and few-shot learning conditions. Methodology: For this, an empirical study was conducted with steps that involved the selection of models, definition of questions, construction of prompts, and collection and analysis of data. Results: The results indicated that ChatGPT and DeepSeek presented more accurate responses under the fewshot learning condition, with DeepSeek standing out in both GLA activities.