Enterprise AI Analysis
PERCEPTIONS, STRATEGIES, AND CHALLENGES OF TEACHERS IN THE INTEGRATION OF ARTIFICIAL INTELLIGENCE IN PRIMARY EDUCATION: A SYSTEMATIC REVIEW
Authors: Olga Arranz-García, María del Carmen Romero García, Vidal Alonso-Secades
Publication: Journal of Information Technology Education: Research, Volume 24, 2025, Article 6
DOI: 10.28945/5458
Executive Impact Summary
This systematic review, adhering to PRISMA 2020 guidelines, explores the transformative impact of AI on K-12 education from 2014–2024. It evaluates teachers' perceptions, strategies, and challenges in integrating AI, uncovering key trends and breakthroughs. The study highlights AI's potential to enhance critical thinking, problem-solving, and student engagement, while acknowledging limitations due to insufficient training, resources, and institutional support. Teachers express confidence in designing AI-integrated curricula despite infrastructure challenges, stressing the need for continuous professional development, AI literacy frameworks, and systemic approaches. Ensuring safe learning environments by addressing data privacy and AI biases is critical for ethical and effective integration, maximizing benefits while safeguarding equity and security.
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Studies Analyzed
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Recent Studies (2022-2024)
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Qualitative Research
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Quantitative Research
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Mixed-Methods Research
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Teacher Perceptions & AI Literacy
Teachers generally hold positive attitudes toward AI integration, viewing it as a tool to enhance critical thinking, problem-solving, and student engagement. Digital literacy and self-efficacy significantly reinforce their confidence in teaching AI, though concerns persist regarding its long-term impact and the need for ethical guidelines.
Systematic Review Process (PRISMA 2020)
Identification (514,919 articles)
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Screening (23,967 peer-reviewed)
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Inclusion (28 final studies)
92.8%
of studies published in the last 3 years (2022-2024)
AI Integration Strategies & Skill Development
Effective AI integration requires curricula fostering AI literacy and experiential learning, with updated teaching approaches leveraging hands-on activities. Computational thinking, programming, and coding are crucial. Pedagogical strategies like collaborative and project-based learning enhance AI integration, fostering 21st-century skills.
| Key AI Integration Strategies | Critical Skills Fostered by AI |
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Teacher Training & Effectiveness
Professional development and training programs are crucial, focusing on AI core competencies, practical applications, and fostering human agency. Training should go beyond basic digital literacy to include stability, IT, communication, and emotional intelligence skills. Confidence-building and relevance-centered training strongly predict AI adoption.
Real-World Impact: Teacher Professional Development Program
Nazaretsky et al. (2022) in Israel
A professional development program for secondary school biology teachers in Israel included AI literacy sessions, efforts to debunk misconceptions, and strategies for integrating AI recommendations into pedagogy. Teachers used an AI-powered assessment tool, AI-Grader, which led to increased confidence in AI technology and greater willingness to incorporate AI tools into their classrooms. This case highlights the potential of structured professional development programs in addressing barriers to AI adoption in education and improving practical integration.
Challenges & Limitations of AI in Education
Internal barriers for teachers include negative perceptions, lack of training, and inadequate resources. Challenges also stem from data privacy, algorithmic biases, and academic integrity concerns. Disparities in infrastructure and resource access between urban and rural schools further hinder equitable AI adoption, underscoring the need for robust ethical and regulatory frameworks.
| Benefits of AI in Education | Key Challenges of AI Integration |
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Your AI Implementation Roadmap
A phased approach ensures successful, ethical, and effective integration of AI into your enterprise operations.
Phase 1: Strategic Planning & Infrastructure Audit
Develop a comprehensive AI integration strategy aligned with educational goals, assess existing technological infrastructure, and identify resource gaps. Establish a dedicated AI task force comprising educators, IT specialists, and administrators. Define clear policies for data privacy and ethical AI use. (Estimated: 6-12 months)
Phase 2: Pilot Programs & Targeted Teacher Training
Implement pilot AI programs in selected classrooms to test tools and strategies. Launch tailored professional development programs focusing on AI literacy, pedagogical integration, and ethical considerations. Gather feedback from pilot participants to refine strategies and address unforeseen challenges. (Estimated: 12-18 months)
Phase 3: Full-Scale Rollout & Continuous Optimization
Expand successful pilot programs across the institution, ensuring equitable access to AI tools and training. Establish continuous support systems for teachers and students. Regularly evaluate the impact of AI on learning outcomes and adapt policies and training based on performance data and emerging AI advancements. (Estimated: Ongoing)
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