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

Non-Technical Summary When 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 Summary The 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.