Recent work in Technology-Enhanced Learning and Human-Computer Interaction highlights the importance of transparency and trust calibration in AI-supported learning environments as they pose a risk of hallucinations. In this study, we investigate whether a simple transparency intervention that warns students that a pedagogical agent may make mistakes affects learner behavior in a math intelligent tutoring system. We conducted a classroom experiment with 252 school students using two system versions: one including a warning message about potential system errors, and one that does not mention potential errors. Using log data, we analyzed students' problem-solving performance data, including help-seeking behavior, error rate, and time-on-task. Results show that students who were warned about potential AI errors requested significantly more hints than those in the other condition, even though the actual system behavior was exactly the same. This finding suggests that lightweight transparency interventions can influence learners' interaction strategies without necessarily improving or impairing immediate performance.

翻译：近期技术在增强学习与人机交互领域的研究强调了人工智能辅助学习环境中透明度与信任校准的重要性，因其存在产生幻觉的风险。本研究探讨了一项简单的透明度干预措施——警告学生教学代理可能会犯错误——如何影响学习者在数学智能辅导系统中的行为。我们以252名在校学生为对象进行了一项课堂实验，使用两个系统版本：一个包含关于系统潜在错误的警告信息，另一个则未提及潜在错误。通过分析日志数据，我们评估了学生的问题解决表现，包括求助行为、错误率和任务用时。结果表明，尽管实际系统行为完全相同，但被警告人工智能可能出错的学生请求提示的频率显著高于另一组。这一发现表明，轻量级的透明度干预可以影响学习者的交互策略，而不一定会即时改善或削弱其表现。