In recent years, the application of generative AI's (GAI) adaptive characteristics in education has rapidly emerged. However, general GAI in educational games often lacks realistic situations and operational fidelity, leading to limited authentic experiences and difficulties in learning transfer. Additionally, GAI frequently produces inaccurate or off-topic responses. To address these limitations, this study designs a historical education problem-solving game based on our previous framework of using GPT as an educational game Non-Player Character (NPC) (Chen & Hou, 2024). The game incorporates realistic story situations and reduces off-topic NPC responses. In the game, learners play as the close friend of a girl and collaborate with GPT-based NPC peers designed by the research team. They conduct online information searches within a limited time to find the historical period in which the girl disappeared and the name of the current building to save her. A total of 17 participants engaged in the empirical evaluation of this study. The study found that learners exhibited a high flow state, perceived the game as highly playable and enjoyable, and had a high acceptance of the game. Additionally, 70% of learners believed that the game helped with historical learning, and nearly 50% felt that it was closer to real human interaction compared to typical GPT conversations. The study demonstrates that a GPT-based NPC, enhanced with contextual stories and reduced off-topic responses, may effectively improve learners' gaming experience.

Authors:
Cheng-Yuan Wei, National Taiwan University of Science and Technology, Taiwan
Yu-Chi Chen, National Taiwan University of Science and Technology, Taiwan
Chih-Chung Chien, National Taiwan University of Science and Technology, Taiwan
Huei-Tse Hou, National Taiwan University of Science and Technology, Taiwan

About the Presenter(s)
Professor Huei-Tse Hou is a University Assistant Professor/Lecturer at National Taiwan University of Science and Technology in Taiwan

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