Generative artificial intelligence in teacher education: systematic literature review

• Autor(a) principal: Marques, M.
• Data de Publicação: 2024
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10773/42297
• topic: Artificial intelligence; Science teacher education; Systematic literature review

Generative artificial intelligence (AI) employs sophisticated algorithms, such as deep learning and reinforcement learning, to create a wide variety of content, including text, images, and audio, with minimal human input. Its ability to generate authentic outputs is being applied in various sectors as diverse as healthcare, finance, and entertainment. In science, generative AI can boost productivity by assisting scientists with idea generation, data analysis, experimental design, and even research writing, despite concerns about accuracy and concerns over authorship and ownership. In education, generative AI promises personalised learning experiences tailored to the needs of individual students, potentially improving educational outcomes. For example, in computer education natural language processing (NLP) can personalise learning experiences, aid student recruitment, and provide solutions to programming exercises. However, generative AI implementation in education also presents unique challenges, such as the risk of being perceived by students as an infallible authority, without sufficient evidence or qualification. This is an exploratory work on the opportunities for incorporating generative AI into science teacher education according to the literature. The research question is: What are the potential opportunities of incorporating generative AI into science teacher education reported in the literature? The Preferred Reporting Items for Systematic Reviews (PRISMA) statement was used to answer this question. Therefore, a search in the Scopus database in March 2024 retrieved 34 relevant registers, published between 1987 and 2023. This literature review includes peer-reviewed empirical studies, published as articles, conference papers or book chapters, focusing on the potential of exploring (generative) AI in (science) teacher education. Due to the novelty of the topic, studies related to non-generative AI in non-science teacher education where also included. Hence, after applying the inclusion/exclusion criteria, 18 registers were excluded from this study. From the remaining 16 registers, 2 publications were not retrieved, so 14 publications integrate the analysis corpus. The analysis revealed teachers' positive attitudes towards AI education, future use of AI in their practice, and opportunities for teacher candidates to practice teaching skills. Despite the promising outlook, this study reveals a gap in the literature on science teacher education. Moreover, the scarcity of AI-learning opportunities in teacher education programmes, makes it crucial to prioritise the development of pedagogically and scientifically sound AI-supported education. Exploring AI in teacher education programmes empowers teacher candidates to make informed decisions about incorporating AI into their future teaching practices, and highlights the need for further research on, for example, the risks, barriers, and limitations of generative AI use in science teacher education, to enhance a broad understanding and to guide best practices, potentially advancing science education and the effective integration of AI in science teaching.