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.

如果使用得当，人工智能（AI）平台有可能促进个性化、自定节奏的学习和实时反馈，使教育更加公平，满足不同的学习方式和需求。这个为期两天的研讨会由美国国家科学基金会材料研究部固态和材料化学项目支持，鼓励参与者开发创新的方法和最佳实践，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。科罗拉多矿业学院的这个研讨会促进了不同群体的科学家和教育工作者之间的互动和合作，包括研究生、博士后研究人员和教师。参与者协同工作(1)开发在课堂上使用人工智能工具的创新方法，以及(2)确定在教育环境中使用这些工具的潜在限制并讨论伦理考虑。NSF资金支持旅行和住宿的研讨会参与者，以确保与会者的多样化队列。随着人工智能（AI）驱动的工具（如ChatGPT和Bard）对学生和教育工作者的日益普及，教育实践也需要不断发展。本次研讨会由美国国家科学基金会材料研究部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，讨论将人工智能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，有可能显著提高学生的学习和参与度。参与者分享并合作开发在实验室环境中使用法学硕士的创新方法，包括设计实验室前活动，评估学生的准备情况，促进全虚拟实验室体验，并协助实验室后分析和反思。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、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创新的llm演示和应用，确定安全性和有效性考虑因素，并培养新的合作伙伴关系。研讨会期间发起的讨论和合作项目有望促进教学实践的发展，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。该奖项反映了美国国家科学基金会的法定使命，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。