黑客是否梦想着电子教师？：一项大规模、实地评估网络安全学生行为和表现与AI导师的研究
Do Hackers Dream of Electric Teachers?: A Large-Scale, In-Situ Measurement of Cybersecurity Student Behaviors and Educational Performance with AI Tutors
AI总结：
本研究评估了AI导师在网络安全教育中的使用，发现三种对话风格影响挑战完成率，并提出教育者和开发者应优化AI导师的应用。
摘要：
为了满足日益增长的网络安全劳动力需求，已提出AI导师用于个性化、可扩展的教育。然而，尽管AI导师在入门编程课程中显示出潜力，但尚未有研究评估其在动手探索和利用系统（例如“夺旗”）中的使用，这些通常用于教授网络安全。因此，尽管兴趣和需求不断增长，但没有任何研究评估学生如何使用AI导师或他们是否从AI导师在真实的大规模网络安全课程中的存在中受益。为了解决这个问题，我们对309名学生在上层入门网络安全课程中使用嵌入式AI导师进行了为期一学期的观察研究。通过分析142526个学生发送给AI导师的查询，以及跨越9个核心网络安全主题和一组期末调查的396个网络安全挑战，我们发现（1）学生使用AI导师的查询和对话策略，（2）这些策略如何与挑战完成相关联，以及（3）学生对AI导师在网络安全教育中的看法。特别是，我们识别出三种广义的AI导师对话风格：简短（有界，少量轮次交流）、反应式（反复提交代码和错误）和主动式（通过有针对性的询问推动问题解决）。我们还发现，这些风格的使用显著预测挑战完成，且随着材料变得更高级，这种影响增加。此外，学生重视导师的可用性，但报告说对于更难的材料，它变得不那么有用。基于此，我们为安全教育者和开发者提供了关于实用AI导师使用的建议。
英文摘要：
To meet the ever-increasing demands of the cybersecurity workforce, AI tutors have been proposed for personalized, scalable education. But, while AI tutors have shown promise in introductory programming courses, no work has evaluated their use in hands-on exploration and exploitation exercises (e.g., "Capture the Flag") commonly used to teach cybersecurity. In particular, it is unclear how students use AI tutors, or what types of use correlate with greater success in solving the challenges in real, large-scale cybersecurity courses. To answer this, we conducted a semester-long observational study of an embedded AI tutor with 309 students in an upper-division introductory cybersecurity course. By analyzing 142,526 student queries sent to the AI tutor across 383 cybersecurity challenges spanning 9 core cybersecurity topics and an accompanying end-of-semester survey, we find (1) what queries and conversation styles students use with AI tutors, (2) how these styles relate to challenge completion, and (3) students' perceptions of AI tutors in cybersecurity education. In particular, we identify three broad AI tutor conversation styles among students: Short (bounded, few-turn exchanges), Reactive (repeatedly submitting code and errors), and Proactive (driving problem-solving through targeted inquiry). We also find that these styles are significantly correlated with challenge completion, and that the completion-rate gap between styles widens as materials become more advanced. Furthermore, students valued the tutor's availability but reported that it became less useful for harder material. Based on our results, we provide suggestions for security educators and developers on practical AI tutor use.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
