Exploring Learners' Interactions with GenAI Agents in Educational Games: Typologies and Emotional Factors in Human-Computer Interaction

This study explores and compares learners' interactions with Generative Artificial Intelligence (GenAI) within an educational game context. Data from 365 dialogues were collected from 76 K12 students at a middle school in Suzhou, China, who had conversations with GenAI agents in an educational game environment. Drawing upon a framework adapted from the classification system of learners' online asynchronous discussions, six categories were utilized: social and affective (ES), information (Inf), questions (Q&A), ideas (ID), agency (EA), and meta-recapitulation (MA). The study revealed several important findings. Firstly, students' responses contained less information (Inf) and meta-recapitulation (MA) due to the limitations of GenAI in providing authoritative and external resources of information. Secondly, players’ social and affective outputs (ES) were associated with mixed emotions, with the majority of emotions experienced by the students being negative (82.02%), and the reasons behind these negative emotions include mistrust of GenAI during or prior to the game, GenAI misinterpreting players’ ideas, a mismatch with players’ expectations, and GenAI repeatedly outputting the same text. Overall, this study sheds light on the complex dynamics of learners' dialogues with GenAI in educational environment, highlighting the learning opportunities and challenges for improving AI-driven educational experiences.