发表机构
Bath Spa University（巴斯斯巴大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究评估课程专属RAG系统Beacon，通过提供私密、即时的课程对齐支持，降低学生求助障碍，促进独立学习，并被视为正式支持前的可信第一站。
AI 中文摘要
获得学术支持是学生成功的关键决定因素，但学生对此体验并不平等：有些学生乐于向讲师或导师求助，而另一些学生则因焦虑、害怕被评判、对期望不确定或对自身理解缺乏信心而犹豫不决。这在计算教育中可能尤为明显，因为编程任务具有累积性和认知高要求性。尽管学生越来越多地使用通用生成式AI工具，但这些工具可能产生不准确、情境化不足或与课程期望不符的回应。本研究介绍并评估了Beacon，一个提供私密、即时、与课程对齐的学术支持的课程专属检索增强生成（RAG）系统。Beacon基于经批准的教学材料进行回应，旨在降低求助障碍，同时鼓励独立学习。采用基于设计的研究方法，Beacon经过迭代开发，并通过混合方法进行评估，结合了问卷和对高等教育机构学生及员工的半结构化访谈。学生认为Beacon的回应与课程内容高度一致，且比不受限制的生成式AI工具更可信，重视其使用伪代码和分步解释而非直接给出解决方案。尽管参与者对未经核实就信任AI生成的回应仍持谨慎态度，但他们将该系统视为在咨询讲师或官方资源之前的有价值的第一支持点。研究结果表明，精心设计的课程专属AI系统可能通过占据独立学习与正式支持之间的中间空间来减少学术支持障碍。教育AI并非取代教育者，而是在扩大指导获取渠道的同时保留讲师的教学角色时可能最有价值。
英文摘要
Access to academic support is a key determinant of student success, yet students experience it unequally: some readily seek help from lecturers or tutors, while others hesitate due to anxiety, fear of judgement, uncertainty about expectations, or low confidence in their understanding. This may be especially evident in computing education, where programming tasks are cumulative and cognitively demanding. Although students increasingly turn to general-purpose generative AI tools, these can produce responses that are inaccurate, insufficiently contextualised, or misaligned with module expectations. This study presents and evaluates Beacon, a course-specific Retrieval-Augmented Generation (RAG) system providing private, immediate, module-aligned academic support. Grounding responses in approved teaching materials, Beacon was designed to lower barriers to help-seeking while encouraging independent learning. Using a design-based research approach, Beacon was developed iteratively and evaluated via mixed methods, combining questionnaires and semi-structured interviews with students and staff at a Higher Education institution. Students described Beacon's responses as closely aligned with module content and more trustworthy than unrestricted generative AI tools, valuing its use of pseudocode and scaffolded explanations over direct solutions. Although participants remained cautious about trusting AI-generated responses without verification, they viewed the system as a valuable first point of support before consulting lecturers or official resources. The findings suggest that carefully designed course-specific AI systems may reduce barriers to academic support by occupying an intermediary space between independent study and formal support. Rather than replacing educators, educational AI may be most valuable when it broadens access to guidance while preserving the pedagogical role of lecturers.
