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

会议：将大型语言模型融入固态材料课程：通过人工智能增强实验室技能

• 批准号: 2333654
• 负责人: Annalise Maughan
• 金额: $ 3.51万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333654&HistoricalAwards=false
• 关键词: Conference; Integrating; Large; Language; Models

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.

非技术摘要有效使用，人工智能（AI）平台有可能促进个性化的，自定进度的学习和实时反馈，从而使教育更加公平，可以满足不同的学习方式和需求。这项为期2天的研讨会在NSF材料研究部的固态和材料化学计划的支持下，鼓励参与者开发创新的方法和最佳实践，以将广泛可用的大语言模型（例如Chatgpt和Bard）纳入本科和研究生水平的固态材料化学教育。科罗拉多州矿业学院的研讨会促进了科学家和教育工作者的互动和合作，包括研究生，博士后研究人员和教职员工。参与者与（1）共同努力（1）开发了在课堂上使用AI驱动工具的创新方法，以及（2）确定潜在的局限性和讨论在教育环境中使用这些工具的道德考虑。 NSF资金支持车间参与者的旅行和住宿，以确保参与者的多样性。总结，人工智能（AI）具有能力的工具（例如Chatgpt和Bard）对学生和教育工作者都需要发展教育实践。该研讨会得到了NSF材料研究部的固态和材料化学计划的支持，召集了教职员工，博士后研究人员和学生，讨论将AI驱动的大型语言模型（LLMS）纳入固态材料化学实验室课程的可能性，并具有显着增强学生学习和参与的潜力。参与者在实验室环境中分享并共享使用LLM的创新方法，包括设计前LAB活动，评估学生的准备，支持完整的虚拟实验室体验以及帮助实验室后分析和反思。研讨会还强调了了解AI的局限性和潜在陷阱的重要性，尤其是在实验室安全，技术真实性和道德使用的背景下。参与者共同努力开发LLM在固态材料化学实验室中的创新演示和应用，确定安全性和有效性考虑因素，并促进新的合作伙伴关系。预计在研讨会期间启动的讨论和协作项目将有助于教学实践的发展，并加深我们对在教育环境中对AI工具的有效，安全和负责任的整合的理解。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和广泛的影响来评估NSF的法定任务，并被认为是宝贵的支持。