Effects of a Prompt Engineering Intervention on Undergraduate Students' AI Self-Efficacy, AI Knowledge and Prompt Engineering Ability: A Mixed Methods Study
Precious Blood Secondary School（宝血女子中学）；The University of Hong Kong（香港大学）
Prompt engineering is critical for effective interaction with large language models (LLMs) such as ChatGPT. However, efforts to teach this skill to students have been limited. This study designed and implemented a prompt engineering intervention, examining its influence on undergraduate students' AI self-efficacy, AI knowledge, and proficiency in creating effective prompts. The intervention involved 27 students who participated in a 100-minute workshop conducted during their history course at a university in Hong Kong. During the workshop, students were introduced to prompt engineering strategies, which they applied to plan the course's final essay task. Multiple data sources were collected, including students' responses to pre- and post-workshop questionnaires, pre- and post-workshop prompt libraries, and written reflections. The study's findings revealed that students demonstrated a higher level of AI self-efficacy, an enhanced understanding of AI concepts, and improved prompt engineering skills because of the intervention. These findings have implications for AI literacy education, as they highlight the importance of prompt engineering training for specific higher education use cases. This is a significant shift from students haphazardly and intuitively learning to engineer prompts. Through prompt engineering education, educators can faciitate students' effective navigation and leverage of LLMs to support their coursework.
When Does It Become Cheating? Exploring the Roles of Self-Efficacy and the Impostor Phenomenon in Computing Students' Perceptions of Academic Dishonesty
XSS for the Masses: Integrating Security in a Web Programming Course using a Security Scanner
网络安全教育被认为是本科计算机课程的重要组成部分，但许多机构只在专门的课程或轨道中教授它。这种选择性有可能使学生毕业时接触到行业中预期的安全编码实践的机会有限。另一种方法是在非安全课程中整合网络安全概念，以便让学生了解安全与其他计算子领域之间的相互作用。在本文中，我们报告我们的经验，应用安全集成的方法，本科网络编程课程。特别是，我们添加了安全编码的实用介绍，通过示例突出了OWASP Top 10漏洞，并演示了如何使用开箱即用的安全扫描工具（例如ZAP）识别它们。此外，我们通过提供奖励分数来激励学生在自己的课程项目中使用这些工具。为了评估这种干预的影响，我们扫描了过去三年学生的项目代码，发现漏洞数量减少了。最后，在焦点小组和调查中，学生们分享了我们的干预有助于提高认识，但他们也强调了分级激励的重要性以及更早教授安全内容的必要性。
Measuring How Students Rely on Generative AI in Academic Writing: Development and Multi-Source Validation of the Generative AI Reliance Types Scale (GenAI-RTS)
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；University at Buffalo（布法罗大学）
Allocation Requires Prediction Only if Inequality Is Low
University of California, Berkeley（加州大学伯克利分校）；Harvard Society of Fellows（哈佛学会）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Tübingen AI Center（蒂宾根人工智能中心）
