The integration of Artificial Intelligence (AI) in primary education is reshaping teaching methodologies, yet many educators face challenges in adopting AI tools. This study examines teacher readiness for AI adoption by analyzing self-reported and training data on AI familiarity, confidence levels, challenges, and strategies. The findings indicate a significant gap in AI-specific knowledge, despite strong pedagogical and content competencies. 44 teachers reported low AI familiarity, 41 had some knowledge, and 10 were entirely unfamiliar. Confidence levels varied, with many expressing the need for structured training and professional development. The study connects these findings to Vygotsky’s Zone of Proximal Development (ZPD) and emphasizes the need for scaffolding and mentorship in AI adoption. Challenges such as infrastructure limitations, digital divides, and ethical concerns align with Sweller’s Cognitive Load Theory and Maslow’s Hierarchy of Needs, which highlight the necessity of proper infrastructure for AI implementation. To bridge this gap, the study recommends AI training programs, institutional investment, adaptive AI learning platforms, and ethical AI guidelines. Further research should explore AI-specific pedagogical frameworks to enhance teacher readiness and optimize AI-driven learning.

Authors:
Heramb Kulkarni, University of Jyväskylä, Finland
Tommi Kärkkäinen, University of Jyväskylä, Finland

About the Presenter(s)
Heramb Kulkarni is Director at Council for Creative Education (CCE) Finland and a doctoral researcher at Jyväskylä University. His interests include AI in education, teacher training, and global curriculum reform.

Connect on Linkedin
https://www.linkedin.com/in/herambkulkarni/

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