LLM-Generated Tips Rival Expert-Created Tips in Helping Students Answer Quantum-Computing Questions
Lars Krupp, Jonas Bley, Isacco Gobbi, Alexander Geng, Sabine M\"uller,, Sungho Suh, Ali Moghiseh, Arcesio Castaneda Medina, Valeria Bartsch, Artur, Widera, Herwig Ott, Paul Lukowicz, Jakob Karolus, Maximilian, Kiefer-Emmanouilidis

TL;DR
This study shows that GPT-4 can generate educational tips for quantum computing questions that are as helpful as expert-created tips, potentially easing educators' workload in large classes.
Contribution
It demonstrates that LLM-generated tips are comparable to expert tips in helping students understand quantum computing concepts.
Findings
LLM tips are more helpful and conceptually relevant than expert tips.
Participants performed better with LLM-labeled tips, indicating a placebo effect.
LLM tips can effectively replace expert tips for quantum computing education.
Abstract
Individual teaching is among the most successful ways to impart knowledge. Yet, this method is not always feasible due to large numbers of students per educator. Quantum computing serves as a prime example facing this issue, due to the hype surrounding it. Alleviating high workloads for teachers, often accompanied with individual teaching, is crucial for continuous high quality education. Therefore, leveraging Large Language Models (LLMs) such as GPT-4 to generate educational content can be valuable. We conducted two complementary studies exploring the feasibility of using GPT-4 to automatically generate tips for students. In the first one students (N=46) solved four multiple-choice quantum computing questions with either the help of expert-created or LLM-generated tips. To correct for possible biases towards LLMs, we introduced two additional conditions, making some participants…
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