Date
Publisher
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.
Study at a glance
- Question: How ready are Sri Lankan ICT teachers to teach artificial intelligence in schools?
- Setting and data: Sri Lanka public schools, June to July 2023, survey of 1,120 in-service Information and Communication Technology teachers.
- Method: Survey analyzed with partial least squares structural equation modeling (PLS-SEM)
Why it matters
Assessing teacher readiness is crucial as countries integrate artificial intelligence into school curricula, but little evidence exists on whether teachers are prepared and confident to deliver this advanced technical subject.
Key findings
- Sri Lankan ICT teachers reported low readiness and low overall self-efficacy for teaching artificial intelligence, averaging 46% on a normalized scale.
- Teachers' self-efficacy for teaching AI was most strongly influenced by emotional and physiological states and imaginary experiences, followed by mastery experience and external factors.
- Neither vicarious experiences nor verbal persuasion had a statistically significant relationship with teachers' self-efficacy to teach artificial intelligence.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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