Researchers from Near East University and collaborating institutions analyse how theoretical and practical AI knowledge influences primary school teachers’ sustainable integration of AI in Northern Cyprus. Using a structured survey and Structural Equation Modeling, they demonstrate that teachers’ beliefs and attitudes critically mediate the impact of AI knowledge on classroom adoption. These findings underscore the importance of professional development that fosters both AI competence and positive perceptions to secure lasting educational innovation.
Key points
Teachers’ beliefs and attitudes mediate the impact of both theoretical and practical AI knowledge on sustainable AI integration.
Structural Equation Modeling on 340 primary teachers’ survey data demonstrates that positive perceptions explain 58% of variance in integration ability.
Both theoretical and practical AI knowledge contribute indirectly to classroom AI adoption, highlighting the need for integrated conceptual and hands-on training.
Why it matters:
By revealing that teachers’ perceptions mediate AI adoption, this research reshapes training strategies for enduring, scalable educational technology integration.
Q&A
What is Structural Equation Modeling?
How do beliefs and attitudes mediate AI integration?
What distinguishes theoretical vs practical AI knowledge?
What is sustainable AI integration?
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Academy
Artificial Intelligence in Education
Artificial Intelligence (AI) refers to computational systems designed to perform tasks that typically require human intelligence, such as pattern recognition, decision-making, and problem-solving. In educational settings, AI encompasses tools like adaptive learning platforms, automated assessment systems, and intelligent tutoring applications. These technologies analyse student data—such as quiz responses, reading times, or interaction patterns—to deliver personalized recommendations, real-time feedback, and differentiated instruction.
Key Concepts
Theoretical AI Knowledge involves understanding core AI principles—machine learning algorithms, data ethics, and the pedagogical rationale behind tool selection. This knowledge is usually acquired through formal training, courses, or workshops that cover AI terminology, system architectures, and conceptual frameworks.
Practical AI Knowledge emerges from hands-on experience with AI tools in real classrooms. Teachers gain practical competence by setting up software, customising learning modules, and troubleshooting predictive analytics dashboards. This experience builds technical skills and confidence.
Benefits of AI Integration
- Personalized Learning: AI algorithms adapt content to each student’s pace and level, helping underperforming learners catch up and advanced learners stay engaged.
- Efficient Assessment: Automated grading and analytics provide immediate feedback on student performance, freeing teachers to focus on targeted interventions.
- Data-Driven Insights: AI tools track metrics like response accuracy and engagement time, enabling educators to refine lesson plans based on real-time evidence.
Challenges and Best Practices
- Addressing Teacher Perceptions: Positive beliefs and attitudes are critical. Professional development should blend theory and practice with confidence-building activities, such as peer mentoring and low-stakes pilot programs.
- Ensuring Ethical Use: Educators must understand data privacy, algorithmic bias, and student consent protocols. Clear guidelines and institutional policies help maintain trust.
- Building Support Infrastructure: Reliable internet access, technical support teams, and integration with existing learning-management systems ensure smooth implementation.
- Continuous Evaluation: Ongoing monitoring of tool effectiveness—through surveys, performance metrics, and teacher feedback—allows for adaptive course corrections and sustainable adoption.
Future Directions include deeper integration of AI with curriculum design, collaborative teacher networks for sharing best practices, and research on long-term student outcomes. By combining robust theoretical foundations with immersive hands-on training and attitudinal support, schools can unlock the full potential of AI to transform teaching and learning.