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

December 27, 2024 Β· Declared Dead Β· πŸ› ACM Transactions on Computing Education

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Authors Chathura Rajapakse, Wathsala Ariyarathna, Shanmugalingam Selvakan arXiv ID 2412.19425 Category cs.AI: Artificial Intelligence Citations 18 Venue ACM Transactions on Computing Education Last Checked 4 months ago
Abstract
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.
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