Division/Committee: [CINF: Division of Chemical Information]

With the significant growth of AI-powered tools for learning and research in Chemistry, assessing their effectiveness and efficiency from an end users perspective has become increasingly challenging. These AI-powered tools encompass a wide range of applications, including data and information discovery, insight extraction, and content generation powered by Large Language Models (LLMs), as well as predictive tools for materials properties, drug effects, and chemical reactions, among others. This symposium invites students, bench chemists, educators, librarians, chemical information professionals, providers of AI-powered tools and services, and other scientists to share their explorations and insights on the evaluation of these tools from the end users perspective. Topics of focus for evaluating these tools include, but are not limited to, their intended purpose, training data sources and algorithms, costs and other access barriers, model accuracy and precision, result usefulness and comprehensiveness, transparency in policies and technical details, as well as considerations regarding user privacy and ethics. Particular attention should be given to evaluating how these tools impact student learning and research efficiency in practice, both positively and negatively.

Dr. Ye Li

Librarian for Chemistry and Chemical Engineering, Massachusetts Institute of Technology

Appearances