TeachTune: Reviewing Pedagogical Agents Against Diverse Student Profiles with Simulated Students
<|reference_start|>TeachTune: Reviewing Pedagogical Agents Against Diverse Student Profiles with Simulated Students: Large language models (LLMs) can empower educators to build pedagogical conversational agents (PCAs) customized for their students. As students have different prior knowledge and motivation levels, educators must evaluate the adaptivity of their PCAs to diverse students. Existing chatbot evaluation methods (e.g., direct chat and benchmarks) are either manually intensive for multiple iterations or limited to testing only single-turn interactions. We present TeachTune, where educators can create simulated students and review PCAs by observing automated chats between PCAs and simulated students. Our technical pipeline instructs an LLM-based student to simulate prescribed knowledge levels and characteristics, helping educators explore diverse conversation patterns. Our pipeline could produce simulated students whose behaviors correlate highly to their input knowledge and motivation levels within 5% and 10% accuracy gaps. Thirty science teachers designed PCAs in a between-subjects study, and using TeachTune resulted in a lower task load and higher student profile coverage over a baseline.<|reference_end|>
@article{jin2024teachtune:,
    title={TeachTune: Reviewing Pedagogical Agents Against Diverse Student Profiles
  with Simulated Students},
    author={Hyoungwook Jin, Minju Yoo, Jeongeon Park, Yokyung Lee, Xu Wang, and
  Juho Kim},
    journal={arXiv preprint arXiv:2410.04078},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04078},
    primaryClass={cs.HC}
}