标题： 学生对大型语言模型生成的论证作文反馈和分数的感知 显示英文标题

标题： Students' Perceptions to a Large Language Model's Generated Feedback and Scores of Argumentation Essays

摘要： 在入门物理课程中的学生常常依赖无效的学习策略，关注最终答案而非理解基本原理。 将科学论证融入问题解决过程可以培养批判性思维，并将概念知识与实际应用联系起来。 通过帮助学习者阐述他们为解决问题而提出的科学论证，并为学生的学习策略提供实时反馈，我们的目标是使学生能够发展出更优秀的问题解决技能。 在大班额物理课程中，向学生提供及时的个性化反馈仍然是一个挑战。 人工智能（AI）的最新进展提供了有希望的解决方案。 本研究探讨了人工智能生成的反馈在入门物理课堂中对学生书面科学论证的潜在作用。 使用Open AI的GPT-4o，我们对学生书面的科学论证提供了延迟反馈，并调查了他们对这种反馈的有用性和准确性的感知。 我们的研究结果为在大班额教室中实施实时人工智能反馈以增强学生的问题解决能力和元认知技能提供了见解。 显示英文摘要

摘要： Students in introductory physics courses often rely on ineffective strategies, focusing on final answers rather than understanding underlying principles. Integrating scientific argumentation into problem-solving fosters critical thinking and links conceptual knowledge with practical application. By facilitating learners to articulate their scientific arguments for solving problems, and by providing real-time feedback on students' strategies, we aim to enable students to develop superior problem-solving skills. Providing timely, individualized feedback to students in large-enrollment physics courses remains a challenge. Recent advances in Artificial Intelligence (AI) offer promising solutions. This study investigates the potential of AI-generated feedback on students' written scientific arguments in an introductory physics class. Using Open AI's GPT-4o, we provided delayed feedback on student written scientific arguments and surveyed them about the perceived usefulness and accuracy of this feedback. Our findings offer insights into the viability of implementing real-time AI feedback to enhance students' problem-solving and metacognitive skills in large-enrollment classrooms.