Teaching assistants (TAs) are essential to grading and feedback provision in proof-based courses, yet these tasks are time-intensive and difficult to scale. Although Large Language Models (LLMs) have been studied for grading and feedback, their effectiveness in proof-based courses is still unknown. Before designing LLM-based systems for this context, a necessary prerequisite is to understand whether LLMs can meaningfully assist TAs with grading and feedback. As such, we present a multi-part case study functioning as a technology probe in an undergraduate proof-based course. We compare rubric-based grading decisions made by an LLM and TAs with varying levels of expertise and examine TAs' perceptions of feedback generated by an LLM. We find substantial disagreement between LLMs and TAs on grading decisions but that LLM-generated feedback can still be useful to TAs for submissions with major errors. We conclude by discussing design implications for human-AI grading and feedback systems in proof-based courses.
非洲的学生与教师的比例很高，限制了学生获得教师的机会。因此，学生们很难得到他们问题的答案。在这项工作中，我们扩展了我们以前的人工智能教学助手Kwame，将其调整为科学教育，并将其部署为Web应用程序。Kwame for Science基于西非高中证书考试（WASSCE）的综合科学科目回答学生的问题。Kwame for Science是一个基于句子BERT的问答网络应用程序，它显示3个段落作为答案，并在回答科学问题时显示信心分数。此外，它显示了前5个相关的过去考试的问题和他们的答案，除了3段。我们对Kwame for Science进行了为期2.5周的实际部署的初步评估，结果显示前3名的准确率为87.5%（n=56），共有11个国家的190名用户。Kwame for Science将为非洲数百万人提供可扩展的、具有成本效益的高质量远程教育。
Generative AI as a Learning Buddy and Teaching Assistant: Pre-service Teachers' Uses and Attitudes
University of Georgia（佐治亚大学）；AI4STEM Education Center, University of Georgia（佐治亚大学 AI4STEM 教育中心）；Generative AI for STEM+C (GeNIUS+ C) Project, University of Georgia（佐治亚大学 STEM+C 生成式人工智能项目）；Department of Educational Theory and Practice, University of Georgia（佐治亚大学教育理论与实践系）；Department of Workforce Education and Instructional Technology, University of Georgia（佐治亚大学劳动力教育与教学技术系）；Bagabaga College of Education（巴加巴加教育学院）；St. Joseph’s College of Education（圣约瑟夫教育学院）；Department of Mathematics, Science, and Social Studies Education, University of Georgia（佐治亚大学数学、科学和社会研究教育学系）；University for Development Studies（发展研究大学）；Gambaga College of Education（甘巴加教育学院）
Evaluating AI-Powered Learning Assistants in Engineering Higher Education: Student Engagement, Ethical Challenges, and Policy Implications
ByWater Institute, Tulane University（杜兰大学拜沃特研究所）；Office of Teaching, Learning, and Technology, University of Iowa（爱荷华大学教学、学习与技术办公室）；River-Coastal Science and Engineering, Tulane University（杜兰大学河流与海岸科学与工程系）
本研究评估了在R1公立大学工程本科课程中应用的AI学习框架Educational AI Hub，通过混合方法发现学生认可其便利性但存在伦理顾虑，强调需提升可用性、伦理透明度并辅以教师指导以促进有效AI学习参与。
