Investigating pre-service teachers’ artificial intelligence perception from the perspective of planned behavior theory

• Published in: Computers and education. Artificial intelligence 2024-06, Vol.6, p.100202, Article 100202
• Main Authors: Sanusi, Ismaila Temitayo, Ayanwale, Musa Adekunle, Tolorunleke, Adebayo Emmanuel
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Basic knowledge; Behavioral intention; Personal characteristics; Pre-service teachers; Teacher education
• ISSN: 2666-920X; 2666-920X
• DOI: 10.1016/j.caeai.2024.100202

There is a need for teachers who are prepared to teach Artificial Intelligence (AI) across the K-12 learning contexts. Owing to the dearth of teacher education programmes on AI, it is helpful to explore factors to be considered in designing an effective AI programme for future teachers. We posit that understanding how to encourage pre-service teachers to learn AI is thus critical for practitioners and policymakers while designing effective instructional AI teacher education programmes. This exploratory study examined the perceptions of pre-service teachers and their behavioral intention to learn AI, by identifying factors that might affect learning and promoting AI in teacher preparation programmes. This study proposed a research model supported by the theory of planned behavior and expanded with other constructs. The factors that were examined include basic knowledge of AI, subjective norm, AI for social good, perceived self-efficacy, self-transcendent goals, personal relevance, AI anxiety, behavioral intention to learn AI, and actual learning of AI. Using a duly validated questionnaire, we surveyed 796 pre-service teachers in Nigerian Universities. Through structural equation modeling approach analyses, our proposed model explains about 79% of the variance in pre-service teachers' intention to learn AI. Basic knowledge and subjective norm were found to be the most important determinant in pre-service teachers’ intention to learn AI. All our hypotheses were supported except for self-efficacy and personal relevance, personal relevance and social good, and behavioral intention and actual learning behavior. The findings provide practitioners, researchers, and policymakers with valuable information to consider in designing effective AI teacher education programmes.