Conference: Integrating Large Language Models into Solid State Materials Curriculum: Enhancing Laboratory Skills through AI

当有效使用时，人工智能（AI）平台有可能促进个性化，自定进度的学习和实时反馈，使教育更加公平，并满足多样化的学习方式和需求。这个为期两天的研讨会由NSF材料研究部的固态和材料化学项目支持，鼓励参与者开发创新方法和最佳实践，将广泛可用的大型语言模型（如ChatGPT和Bard）纳入本科和研究生阶段的固态材料化学教育。这个研讨会在科罗拉多矿业学院促进科学家和教育工作者，包括研究生，博士后研究人员和教师的不同群体之间的互动和合作。参与者合作（1）开发在课堂上使用人工智能工具的创新方法，（2）确定潜在的限制并讨论在教育环境中使用这些工具的道德考虑因素。NSF资助支持研讨会参与者的旅行和住宿，以确保与会者的多样化。技术总结学生和教育工作者越来越容易使用人工智能（AI）驱动的工具，如ChatGPT和Bard，这需要教育实践的发展。该研讨会由NSF材料研究部的固态和材料化学项目支持，汇集了教师，博士后研究人员和学生，讨论将AI驱动的大型语言模型（LLM）纳入固态材料化学实验室课程的可能性，有可能显着提高学生的学习和参与度。参与者分享和协作开发在实验室环境中使用LLM的创新方式，包括设计实验前活动，评估学生准备情况，促进完整的虚拟实验室体验，并帮助实验后分析和反思。研讨会还强调了理解人工智能的局限性和潜在陷阱的重要性，特别是在实验室安全性，技术准确性和道德使用的背景下。参与者共同努力，在固态材料化学实验室中开发LLM的创新演示和应用，确定安全性和有效性考虑因素，并建立新的伙伴关系。在研讨会期间发起的讨论和合作项目预计将有助于教学实践的发展，并加深我们对人工智能工具在教育环境中有效，安全和负责任的整合的理解。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Non-Technical SummaryWhen used effectively, artificial intelligence (AI) platforms have the potential to facilitate personalized, self-paced learning and real-time feedback, making education more equitable and catering to diverse learning styles and needs. This 2-day workshop, supported by the Solid State and Materials Chemistry program in NSF’s Division of Materials Research, encourages participants to develop innovative approaches and best practices to incorporating widely-available large language models – such as ChatGPT and Bard – into solid state materials chemistry education at both undergraduate and graduate levels. This workshop at Colorado School of Mines fosters interactions and collaborations among a diverse group of scientists and educators, including graduate students, postdoctoral researchers, and faculty. Participants work collaboratively to (1) develop innovative approaches to using AI-powered tools in the classroom, and (2) identify potential limitations and discuss ethical considerations for the use of these tools in an educational setting. NSF funding supports travel and accommodations for workshop participants to ensure a diverse cohort of attendees.Technical SummaryThe growing accessibility of artificial intelligence (AI)-powered tools, such as ChatGPT and Bard, to both students and educators requires evolution of educational practices. This workshop, supported by the Solid State and Materials Chemistry program in NSF’s Division of Materials Research, brings together faculty, postdoctoral researchers and students to discuss possibilities to incorporate AI-powered Large Language Models (LLMs) into solid state materials chemistry laboratory courses, with the potential to significantly enhance student learning and engagement. Participants share and collaboratively develop innovative ways of using LLMs in laboratory settings, including designing pre-lab activities, assessing student preparedness, facilitating full virtual lab experiences, and aiding in post-lab analysis and reflection. The workshop also emphasizes the importance of understanding the limitations and potential pitfalls of AI, particularly in the context of laboratory safety, technical veracity, and ethical use. Participants work together to develop innovative demonstrations and applications of LLMs in solid-state materials chemistry labs, identify safety and effectiveness considerations, and foster new partnerships. The discussions and collaborative projects initiated during the workshop are expected to contribute to the evolution of pedagogical practices and deepen our understanding of the effective, safe, and responsible integration of AI tools in educational settings.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.