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PromptMirror: Visualizing LLM Use to Support STEM Student Reflection

MCML Authors

Ka Hei Carrie Lau

→ Group Enkelejda Kasneci
Human-Centered Technologies for Learning

Nađa Terzimehić

Dr.

→ Group Enkelejda Kasneci
Human-Centered Technologies for Learning

Enkelejda Kasneci

Prof. Dr.

Core PI

Human-Centered Technologies for Learning

Abstract

Large language models (LLMs) are increasingly embedded in students’ academic work, yet the increasing reliance can undermine learning depth and raise integrity concerns. While reflection has long been studied in HCI to foster awareness and behavior change, little is known about how to support students in reflecting on everyday LLM use. We present PromptMirror, a student-facing dashboard that processes LLM conversation logs and visualizes four perspectives, temporal, sentiment, intent, and thematic, to encourage reflection. We informed the design of PromptMirror with two focus groups (one expert and one student with four participants each) and subsequently conducted an online think-aloud with 20 university students who uploaded their own LLM use data. Findings provide preliminary evidence that PromptMirror may support students in recognizing their LLM use estimation gap and engaging in deeper reflection on LLM reliance. Our contributions are twofold: (1) a student-centric reflection system; (2) empirical insights into reflective analytics for everyday LLM tools.

inproceedings LTK26