BoilerTAI：在教育论坛中利用生成式AI增强教学的平台
BoilerTAI: A Platform for Enhancing Instruction Using Generative AI in Educational Forums
AI总结：
本文提出BoilerTAI平台，将GenAI与教育论坛集成，由AI-TA预回答并审核学生问题；实验表明其可降低认知负荷且不影响学生接受度。
摘要：
贡献：这篇属于研究类别轨道的完整论文描述了一个实用、可扩展的平台，该平台将生成式AI（GenAI）与在线教育论坛无缝集成，提供了一种增强教职人员教学能力的新方法。该平台通过促进学生帖子与大语言模型（LLM）之间的交互，使教学人员能够高效管理、完善和批准回复。这一贡献提升了教学支持的效率和有效性，并显著改善了提供给学生的回复质量与速度，从而丰富整体学习体验。背景：本研究以维果茨基的社会文化理论和“更有知识的人”（MKO）概念为基础，考察GenAI如何充当辅助性MKO，以丰富学生与教师之间的教育对话。研究问题：当GenAI用于预回答学生发布在教育讨论论坛上的问题时，它在减少教学人员工作量方面的效果如何？方法：在大型编程入门课程中采用混合方法，人工教学助理（AI-TA）使用一个AI辅助平台预回答学生问题。我们分析了效率指标，例如对AI生成回复的修改频率，并收集了来自AI-TA的定性反馈。发现：结果表明，与人工教师提供的回复相比，学生对AI-TA生成回复的接受度没有显著差异。这表明，在得到适当管理的情况下，GenAI能够有效满足教育需求。此外，AI-TA感受到回复问题所需的认知负荷降低，表明GenAI有潜力在不损害教育质量的情况下提高教学效率。
英文摘要：
Contribution: This Full paper in the Research Category track describes a practical, scalable platform that seamlessly integrates Generative AI (GenAI) with online educational forums, offering a novel approach to augment the instructional capabilities of staff. The platform empowers instructional staff to efficiently manage, refine, and approve responses by facilitating interaction between student posts and a Large Language Model (LLM). This contribution enhances the efficiency and effectiveness of instructional support and significantly improves the quality and speed of responses provided to students, thereby enriching the overall learning experience. Background: Grounded in Vygotsky's socio-cultural theory and the concept of the More Knowledgeable Other (MKO), the study examines how GenAI can act as an auxiliary MKO to enrich educational dialogue between students and instructors. Research Question: How effective is GenAI in reducing the workload of instructional staff when used to pre-answer student questions posted on educational discussion forums? Methodology: Using a mixed-methods approach in large introductory programming courses, human Teaching Assistants (AI-TAs) employed an AI-assisted platform to pre-answer student queries. We analyzed efficiency indicators like the frequency of modifications to AI-generated responses and gathered qualitative feedback from AI-TAs. Findings: The findings indicate no significant difference in student reception to responses generated by AI-TAs compared to those provided by human instructors. This suggests that GenAI can effectively meet educational needs when adequately managed. Moreover, AI-TAs experienced a reduction in the cognitive load required for responding to queries, pointing to GenAI's potential to enhance instructional efficiency without compromising the quality of education.
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。
