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A Self-Efficacy Theory-based Study on the Teachers Readiness to Teach Artificial Intelligence in Public Schools in Sri Lanka

arXiv 2412.19425 ↗First posted 27 Dec 2024cs.AIcited by 1

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Abstract (arXiv)

This study investigates Sri Lankan ICT teachers' readiness to teach AI in schools, focusing on self-efficacy. A survey of over 1,300 teachers assessed their self-efficacy using a scale developed based on Bandura's theory. PLS-SEM analysis revealed that teachers' self-efficacy was low, primarily influenced by emotional and physiological states and imaginary experiences related to AI instruction. Mastery experiences had a lesser impact, and vicarious experiences and verbal persuasion showed no significant effect. The study highlights the need for a systemic approach to teacher professional development, considering the limitations in teachers' AI expertise and social capital. Further research is recommended to explore a socio-technical systems perspective for effective AI teacher training.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 1 of its 76 references that are in the map. First posted on arXiv 27 Dec 2024.

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  1. 2021ML-Quest: A Game for Introducing Machine Learning Concepts to K-12 Studentscited by 1

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