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

arXiv: https://arxiv.org/abs/2412.19425 · HTML: https://mlsanity.com/paper/2412.19425/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 76 references that are in the map.
- First posted on arXiv 27 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [ML-Quest: A Game for Introducing Machine Learning Concepts to K-12 Students](https://mlsanity.com/paper/2107.06206/) (2021)

## Built on by (most cited first, 1 of 1)

- [Google, AI Literacy, and the Learning Sciences: Multiple Modes of Research, Industry, and Practice Partnerships](https://mlsanity.com/paper/2604.07601/) (2026)

## 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.