Personalized Learning

Background: The integration of artificial intelligence (AI) with augmented reality (AR) has significantly revolutionized educational practices. By blending digital content with the physical environment, AR enhances student engagement, while AI-driven tools personalize learning experiences. Objective: This article aims to explore the future of AI-powered AR in education, analyzing its potential to transform traditional learning environments by improving student interaction, knowledge retention, and personalized learning. Methods: A comprehensive literature review was conducted, examining current AI-AR applications in educational settings. Additionally, case studies from early adopters of this technology in classrooms were analyzed. Interviews with educators and experts were conducted to gain insights into the challenges and opportunities associated with AI-enhanced AR. Results: The findings indicate that AI-AR systems significantly enhance student engagement, promote interactive learning experiences, and offer personalized feedback based on individual learning styles. However, challenges such as high implementation costs, technical expertise requirements, and the need for curriculum alignment were identified. Conclusion: AI-AR has the potential to reshape educational practices by fostering a more interactive, engaging, and tailored learning experience. Future efforts should focus on addressing the technical and pedagogical challenges to ensure successful adoption across various educational contexts.

AI in Language Teaching, Learning, and Assessment (2024), edited by Fang Pan, offers a comprehensive and insightful exploration of how artificial intelligence (AI) is reshaping language education. The book is thoughtfully divided into three well-structured sections Preview, Perspectives, and Practice, each addressing essential dimensions of AI integration. In the Preview section, Pan examines AI's transformative role in enhancing educational delivery and personalizing learning experiences, paving the way for greater efficiency and accessibility. Subsequently, the Perspectives section collates diverse global viewpoints that critically address challenges such as algorithmic bias, data privacy, and equity concerns. Finally, the Practice section offers evidence-based case studies, showcasing tangible innovations such as adaptive tutoring systems and AI-powered assessment tools, which have demonstrated real-world impact. More importantly, Pan strikes an impressive balance between highlighting AI's vast potential and scrutinizing its ethical and practical complexities. By combining theoretical perspectives, empirical findings, and practical applications, this book stands as an essential resource for educators, researchers, and policymakers, aiming to navigate AI-driven developments in language education with both responsibility and clarity.