The Integration of Artificial Intelligence (AI) in the Work of Educators
Abstract
The integration of Artificial Intelligence (AI) into teacher education is transforming pedagogical practices by promoting personalized learning, supporting continuous professional development, and improving instructional effectiveness. Core AI technologies—such as Intelligent Tutoring Systems (ITS), learning analytics, and automated assessment tools—enable adaptive and data-informed teaching strategies that enhance engagement and learning outcomes. This study employs a qualitative thematic analysis of peer-reviewed literature to identify key themes, opportunities, and challenges related to the use of AI in teacher education. The findings demonstrate that AI contributes to the design of personalized learning pathways, fosters critical and reflective thinking, and strengthens teachers’ motivation and engagement. Technologies such as ITS, Virtual Reality (VR), and AI-based analytics have shown measurable benefits for both teacher performance and learner outcomes. The integration of AI also raises significant ethical and professional issues, including algorithmic bias, data privacy, and the transparency of AI processes. A persistent gap in teacher preparation remains, particularly in digital literacy and the pedagogically sound application of AI tools. The study highlights the necessity of comprehensive training programs that integrate ethical awareness, digital competence, and practical skills for AI implementation in educational contexts. The research concludes that effective AI integration requires not only technological adoption but also strong ethical frameworks, institutional support, and sustained professional development. It offers practical recommendations for educators, policymakers, and researchers aiming to leverage AI responsibly to enhance educational quality, inclusiveness, and innovation.
Fields of Study & Subjects
Artificial Intelligence in Education
Artificial Intelligence in Healthcare and Education
Intelligent Tutoring Systems and Adaptive Learning
Publication details
- Journal / source
- Anuarul Universitatii Petre Andrei din Iasi Fascicula Asistenta sociala sociologie psihologie
- Volume
- 33
- Pages
- 283–289
- Publication date
- 18 January 2026