Perspectives of Generative AI in Chemistry Education Within the TPACK Framework

Abstract

AbstractArtificial intelligence (AI) has made remarkable strides in recent years, finding applications in various fields, including chemistry research and industry. Its integration into chemistry education has gained attention more recently, particularly with the advent of generative AI (GAI) tools. However, there is a need to understand how teachers’ knowledge can impact their ability to integrate these tools into their practice. This position paper emphasizes two central points. First, teachers technological pedagogical content knowledge (TPACK) is essential for more accurate and responsible use of GAI. Second, prompt engineering—the practice of delivering instructions to GAI tools—requires knowledge that falls partially under the technological dimension of TPACK but also includes AI-related competencies that do not fit into any aspect of the framework, for example, the awareness of GAI-related issues such as bias, discrimination, and hallucinations. These points are demonstrated using ChatGPT on three examples drawn from chemistry education. This position paper extends the discussion about the types of knowledge teachers need to apply GAI effectively, highlights the need to further develop theoretical frameworks for teachers’ knowledge in the age of GAI, and, to address that, suggests ways to extend existing frameworks such as TPACK with AI-related dimensions.