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Antonenko, P., & Abramowitz, B. (2023). In-service teachers’ (mis)conceptions of artificial intelligence in K-12 science education. Journal of Research on Technology in Education, 55(1), 64–78. https://doi.org/10.1080/15391523.2022.2119450
This quantitative research study conducted by Antonenko and Abramowitz (2023) from the University of Florida focuses on science teachers’ misconceptions of Artificial Intelligence (AI) in science education. The authors state that society’s future relies on informed AI perspectives and skills, and to effectively integrate these AI tools, skills, and lessons into K-12 education, teachers must be appropriately educated on AI and its possibilities in education. This study highlights K-12 science teachers’ conceptions and misconceptions about AI in education. This is a survey-based research study, like the study conducted by Hays et al. (2023), and 53 teachers from the Southeastern United States participated in the (Mis)conceptions of AI survey (MAIS). The participants included 21 elementary school educators, 14 middle school educators, and 18 high school educators. Through the authors’ data analysis, common participant misconceptions regarding AI were that it inherently possesses bias, struggles to process complex data, and programs can self-learn. These are valuable professional development opportunities for clarifying AI misconceptions, as the authors state that these misconceptions affect educators’ willingness to implement AI into their teaching practices. In addition to the misconceptions identified by Antonenko and Abramowitz (2023), the authors state that teachers are enthusiastic about the potential of AI for K-12 education, believe it is essential for their students to understand the basics of AI, and are overall not concerned or unsure about the ethics of AI in K-12 education. This study provides valuable insights and information for minimizing AI misconceptions as they negatively affect the effective implementation of AI in K-12 education. While this study focuses on States in the Southeastern USA, the data and its analysis are more generalizable than Chiu (2021), as Antonenko and Abramowitz (2023) incorporate a larger population and diversity of educators. This study demonstrates the importance of accurate, informative, and practical teacher education on AI, and the data, analysis, and information in this study are essential for this research question as it provides an awareness of educators’ misconceptions, readiness, and ability for implementing AI in K-12 education. The study’s findings demonstrate that professional development workshops would be valuable in minimizing teachers’ AI misconceptions and preparing teachers for effectively implementing AI in education, which would increase teacher efficiency, reduce workload, and improve student learning outcomes. (371 words)