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<p>This study investigates the transformative impact of artificial intelligence (AI) on transnational higher education, focusing on innovative teaching methods that address challenges in a globalized and digital learning environment. The purpose of the study is to examine how AI-driven tools, including adaptive learning systems, intelligent tutoring systems, AI-enhanced language translation, and immersive virtual and augmented reality experiences, influence the effectiveness of teaching across diverse educational contexts. Data were collected from 294 students across five private universities in Bangladesh using an online questionnaire and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the effects of AI tool type, institutional support, teacher readiness, student demographics, integration depth, and feedback mechanisms on teaching effectiveness. Findings reveal that AI tool type, integration depth, and teacher readiness have the strongest positive effects on teaching effectiveness, while institutional support, feedback mechanisms, and student demographics also contribute significantly. The study further highlights ethical considerations, including data privacy, algorithmic bias, and the importance of balancing AI with human interaction. The findings have practical implications for educators, administrators, and policymakers seeking to enhance teaching quality, promote inclusive learning, and strategically integrate AI technologies in higher education.</p>

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Factors affecting innovative teaching methods with AI transnational higher education in Bangladesh

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月13日

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  • Md Mominur Rahman,
  • Md. Moniruzzaman,
  • Abdur Rahim Khan,
  • Mohammad Ekramol Islam,
  • Hasibul Islam,
  • Foday Joof,
  • Abu Bakkar Siddik

摘要

This study investigates the transformative impact of artificial intelligence (AI) on transnational higher education, focusing on innovative teaching methods that address challenges in a globalized and digital learning environment. The purpose of the study is to examine how AI-driven tools, including adaptive learning systems, intelligent tutoring systems, AI-enhanced language translation, and immersive virtual and augmented reality experiences, influence the effectiveness of teaching across diverse educational contexts. Data were collected from 294 students across five private universities in Bangladesh using an online questionnaire and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the effects of AI tool type, institutional support, teacher readiness, student demographics, integration depth, and feedback mechanisms on teaching effectiveness. Findings reveal that AI tool type, integration depth, and teacher readiness have the strongest positive effects on teaching effectiveness, while institutional support, feedback mechanisms, and student demographics also contribute significantly. The study further highlights ethical considerations, including data privacy, algorithmic bias, and the importance of balancing AI with human interaction. The findings have practical implications for educators, administrators, and policymakers seeking to enhance teaching quality, promote inclusive learning, and strategically integrate AI technologies in higher education.