The main goal of the study was to explore the conceptions and misconceptions about AI held by K-12 science teachers in the U.S. and to examine how these perceptions influence their intent to integrate AI into their teaching practices.
Methods: The study involved a survey called the (Mis)conceptions of AI Survey (MAIS), completed by 53 K-12 science teachers from the Southeastern U.S. The survey included 36 Likert-scale items and seven open-ended questions. Data were analyzed using descriptive statistics, Spearman Rho correlation matrices, and thematic analysis.
Key Findings:
- Teachers generally had accurate conceptions about AI, such as recognizing that AI algorithms can differ in purpose and structure.
- Common misconceptions included beliefs that AI is expensive, can learn on its own, and is not biased.
- Older teachers were more likely to view AI as dangerous, while more experienced teachers correctly believed AI cannot function independently of humans.
- Teachers' intent to use AI was associated with both correct and incorrect conceptions about AI.
Implications: The findings highlight the need for comprehensive professional development for teachers to address misconceptions about AI, which is crucial for effective integration of AI tools in K-12 education. This can enhance teachers' preparedness to educate students about AI, contributing to a more informed future workforce.
Limitations: The study's limitations include a small sample size and a regional focus, which may not be representative of all U.S. K-12 teachers. Additionally, the reliance on self-reported data could introduce bias.
Future Directions: Future research should explore AI misconceptions among teachers in diverse educational settings and develop targeted professional development programs to address these misconceptions. Expanding the study to include a larger and more diverse sample of teachers could provide more comprehensive insights.
Title and Authors: "In-service teachers' (mis)conceptions of artificial intelligence in K-12 science education" by Pavlo Antonenko and Brian Abramowitz.
Published On: September 9, 2022
Published By: Journal of Research on Technology in Education