Artificial Intelligence Chatbots in Chemical Information Seeking

Educational Insights through a SWOT analysis

Authors

  • Johannes Pernaa The Unit of Chemistry Teacher Education, Department of Chemistry, Faculty of Science, University of Helsinki, Helsinki, Finland https://orcid.org/0000-0003-1735-5767
  • Topias Ikävalko The Unit of Chemistry Teacher Education, Department of Chemistry, Faculty of Science, University of Helsinki, Helsinki, Finland
  • Aleksi Takala The Unit of Chemistry Teacher Education, Department of Chemistry, Faculty of Science, University of Helsinki, Helsinki, Finland
  • Emmi Vuorio The Unit of Chemistry Teacher Education, Department of Chemistry, Faculty of Science, University of Helsinki, Helsinki, Finland
  • Reija Pesonen The Unit of Chemistry Teacher Education, Department of Chemistry, Faculty of Science, University of Helsinki, Helsinki, Finland
  • Outi Haatainen The Unit of Chemistry Teacher Education, Department of Chemistry, Faculty of Science, University of Helsinki, Helsinki, Finland

Keywords:

artificial intelligence, chatbot, information seeking, chemistry learning, teacher education, TPACK, SWOT

Abstract

Artificial intelligence (AI) chatbots are the latest advance in information technology. They are next-word predictors built on large language models (LLM) that offer the possibility to process and generate information. In this theoretical article, we provide educational insights of the possibilities and challenges of educational usage of AI chatbots. The insights were produced in the context of chemical information-seeking activities designed for chemistry teacher education. The analysis was conducted via a SWOT approach using technological pedagogical content knowledge framework (TPACK) to improve the accuracy. The analysis revealed several internal and external possibilities and challenges. The key insight is that AI chatbots will change the way people interact with information. For example, they enable the building of personal learning environments with ubiquitous access to information and AI tutoring. Their ability to support chemistry learning is impressive. However, 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 of AI chatbots will take time. The obstacles hindering the adoption of AI chatbots can be removed, for example, through integrating LLMs to curricula, focusing 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 chemistry teacher education. Based on the conducted analysis, we predict that AI chatbots will be a major technological change agent towards inclusive and equitable quality lifelong learning for all.

How to cite: Pernaa, J.; Ikävalko, T.; Takala, A.; Vuorio, E.; Pesonen, R.; Haatainen, O. Artificial Intelligence Chatbots in Chemical Information Seeking: Educational Insights through a SWOT analysis. Preprints 2023, 2023121066. https://doi.org/10.20944/preprints202312.1066.v1

Author Biography

Johannes Pernaa, The Unit of Chemistry Teacher Education, Department of Chemistry, Faculty of Science, University of Helsinki, Helsinki, Finland

I work as a university lecturer at the Chemistry Department in University of Helsinki. My main responsibility is to develop chemistry teacher education in our university. I also work as the Editor-in-Chief in LUMAT. My work can be followed through my academic blog ajatuksia.johannespernaa.fi.

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Published

2023-12-14

Issue

Vol. 4 No. 1 (2023): Chatbots in Chemistry Education – Preprints in 2023

Section

Articles

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License

Copyright (c) 2023 Johannes Pernaa, Topias Ikävalko, Aleksi Takala, Emmi Vuorio, Reija Pesonen, Outi Haatainen

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.