Navigating the Fog: How University Students Recalibrate Sensemaking Practices to Address Plausible Falsehoods in LLM Outputs

DOI

LLM interfaces, such as ChatGPT, are widely used by students in higher education. However, their reliability is compromised by the tendency to generate plausible yet factually inaccurate content. This issue is particularly critical as the HCI community shows growing interest in designing LLM-based educational technology. Despite this interest, we have yet to learn how plausible falsehoods disrupt students' real-time sensemaking of outputs from imperfectly reliable LLMs, and how students currently attempt to mitigate these negative effects. Thus, we conducted a case study of 15 university students using ChatGPT through think-aloud tasks and semi-structured interviews. We identified recurring patterns of sensemaking, with students facing challenges such as relying on intuitive guesses and feeling overwhelmed by LLM's lengthy, sycophantic, and overconfident responses. They adapted by inducing inconsistencies from the LLM's responses and strategically dividing tasks between themselves and the LLM. Lastly, our study highlights several design implications for future reliable LLM interfaces.