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

TL;DR

This study examines Sri Lankan ICT teachers' self-efficacy in teaching AI, revealing low confidence levels influenced mainly by emotional states and highlighting the need for targeted professional development.

Contribution

It applies a self-efficacy theoretical framework to assess teachers' readiness for AI instruction, providing insights into key influencing factors and gaps in current training.

Findings

Teachers' self-efficacy in AI teaching is low.

Emotional and physiological states significantly impact self-efficacy.

Mastery experiences have a lesser effect on self-efficacy.

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.