Artificial Intelligence Chatbots in Chemical Information Seeking: Narrative Educational Insights via a SWOT Analysis

Abstract

Artificial intelligence (AI) chatbots are next-word predictors built on large language models (LLMs). There is great interest within the educational field for this new technology because AI chatbots can be used to generate information. In this theoretical article, we provide educational insights into the possibilities and challenges of using AI chatbots. These insights were produced by designing chemical information-seeking activities for chemistry teacher education which were analyzed via the SWOT approach. The analysis revealed several internal and external possibilities and challenges. The key insight is that AI chatbots will change the way learners interact with information. For example, they enable the building of personal learning environments with ubiquitous access to information and AI tutors. Their ability to support chemistry learning is impressive. However, the processing of chemical information reveals the limitations of current AI chatbots not being able to process multimodal chemical information. There are also ethical issues to address. Despite the benefits, wider educational adoption will take time. The diffusion can be supported by integrating LLMs into curricula, relying on open-source solutions, and training teachers with modern information literacy skills. This research presents theory-grounded examples of how to support the development of modern information literacy skills in the context of chemistry teacher education.