This study investigates how situational interest (SI) influences student interview responses during game-based learning. Using real-time interviews conducted within the What-If Hypothetical Implementations in Minecraft (WHIMC) environment, we analyzed differences in discourse between high- and low-SI students. Interview transcripts were coded using a structured codebook through a hybrid approach combining humans and GPT-4o. Epistemic Networks of student reflections revealed that high-SI students were more likely to offer Brief and Enthusiastic responses across all question types. These students used excited language, reacted aloud to game events, and expressed interest in specific gameplay elements. Low-SI students provided more Explanatory and Neutral responses. They often paused to describe their plans, explain in-game decisions, or reason through moments of uncertainty. Ordered Networks of interviewer statements revealed that interviewers’ strategies remained consistent for low- and high-SI groups, ruling out interviewer behavior as the cause of differences in student responses. These results shed light on the ways student interest levels may impact interview responses during game-based learning experiences.