Enhancing Foreign Language Learning Through AI-Powered Personalized Instruction: Opportunities and Challenges

This qualitative research investigates the effects of AI-driven adaptive learning systems on foreign language education, drawing on the frameworks of Constructivist Learning Theory and Innovation Diffusion Theory. Data collected through interviews, classroom observations, and analysis of instructional materials revealed both advantages and limitations associated with these personalized learning technologies. On the one hand, learners indicated higher levels of engagement, motivation, and skill acquisition as a result of tailored content, flexible pacing, and individualized support, which correspond with Constructivist ideas of learner-centered scaffolding. On the other hand, concerns were raised regarding possible algorithmic bias, as well as the necessity for stronger human supervision and cooperation between educators and AI systems, reflecting Innovation Diffusion Theory’s emphasis on complexity and compatibility challenges. The social dimension of AI-supported learning environments also proved significant, as some participants felt that increased personalization reduced opportunities for peer interaction. Overall, the results highlight the importance of designing and implementing AI-based adaptive systems with attention to inclusivity, transparency, and effective human–AI collaboration in order to maximize learning experiences and outcomes.