Integrating Large Language Models into the Chemistry and Materials Science Laboratory Curricula
Abstract
Recommendations F rom robotic teachers to interactive tomes, personalized, artificial intelligence (AI)-based education has been a recurring theme in science fiction since the mid-20th century.With the recent rise of large language models (LLMs) such as ChatGPT and Gemini, these once-fantastical visions are rapidly shifting from fiction to fact.In the realm of chemistry and materials science education�and especially in courses with a laboratory component�the potential for revolutionizing education is immense.Laboratory experiences are meant to foster hands-on skills, reinforce core concepts, and develop students' identity as scientists.[1][2][3][4] Yet research consistently finds that traditional lab courses underperform in achieving deeper conceptual understanding and gains in critical thinking.2,3,[5][6][7] In an era of LLMs, one can envision improving these student outcomes using AI-based feedback or personalized guidance to reshape laboratory courses.Alongside cautious optimism, however, higher education faces unsettling questions surrounding ethical usage and the penchant of LLMs to deliver hallucinations and misinformation.In lab-based courses, these risks become particularly fraught.Presently, the intersection of education and LLMs is a "wild west", where individual educators are experimenting with LLMs in classrooms and forging new approaches as they go.Best practices remain ad hoc, and new strategies�whether brilliant or flawed�are rarely shared widely.The result is…
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