Converging Technologies for Inclusive Education

Springer, 2026

This book offers a forward-looking exploration of how converging technologies are reshaping inclusive education across diverse learning contexts. Bringing together interdisciplinary research at the intersection of engineering, artificial intelligence, pedagogy, and social sciences, the volume examines how digital innovation, generative AI, deep learning, and data-driven methodologies can enhance teaching effectiveness, learner engagement, and equitable access to education. The chapters address topics such as AI-enabled interventions for aging learners, early detection of abnormal behaviors in children through biological signal analysis, innovative teaching models for mechanical design based on TRIZ, and new pathways for industry–education integration in cultivating digital innovation talent. Ethical, cultural, and social dimensions are also critically discussed, including challenges of AI in language education, youth discourse in social media, and the evolving role of educators in AI-supported mathematics learning environments. Additional studies explore learner autonomy in second-language academic writing, faculty development in engineering education, children’s programming education supported by generative AI, and data-informed decision-making in overseas study trends. By combining technological innovation with pedagogical reflection, this collection highlights practical strategies and theoretical insights for advancing inclusive and adaptive learning ecosystems. This book is intended for researchers, educators, policymakers, instructional designers, and graduate students interested in educational technology, engineering education, artificial intelligence in learning, and inclusive educational development.

• Curatore: Michele Biasutti, Harrison Hao Yang
• Anno edizione: 2026
• Editore: Springer
• Formato: EPUB3 con Adobe DRM
• Lingua: Inglese
• Compatibilità: Tutti i dispositivi (eccetto Kindle)
• Layout: Reflowable
• EAN: 9789819594184