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<p>Artificial intelligence (AI) is rapidly reshaping engineering education, yet evidence on its impact and best practices remains fragmented across disciplines and approaches. To consolidate current knowledge, this review applies a PRISMA-ScR scoping methodology, surveying over 3,000 records from Scopus, Web of Science, IEEE Xplore, ERIC, and Google Scholar (2000–2024) and synthesizing 200 relevant studies. The analysis highlights dominant applications such as intelligent tutoring, adaptive assessment, and VR/AR simulation, with emerging interest in large language model–based feedback and generative AI. Reported outcomes suggest moderate improvements in student performance and engagement, though gaps remain in areas such as ethics, equity, and long-term evaluation. Expert perspectives further underscore opportunities in micro-credentialing and AI-driven design studios, alongside challenges of data privacy, transparency, and faculty readiness. Overall, AI-enhanced strategies show strong potential to support personalization and innovation in engineering education, but sustainable adoption will require longitudinal studies, ethical safeguards, and targeted professional development.</p>

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Artificial intelligence innovations challenges and emerging trends in engineering education

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月5日

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  • Ahasanur Rahman,
  • Amith Khandakar,
  • Mohamed Arselene Ayari,
  • Khalid Naji,
  • Abdulla Khalid Al-Ali,
  • Abdel Latif Sellami,
  • Saleh Mohammed Ali Alhazbi

摘要

Artificial intelligence (AI) is rapidly reshaping engineering education, yet evidence on its impact and best practices remains fragmented across disciplines and approaches. To consolidate current knowledge, this review applies a PRISMA-ScR scoping methodology, surveying over 3,000 records from Scopus, Web of Science, IEEE Xplore, ERIC, and Google Scholar (2000–2024) and synthesizing 200 relevant studies. The analysis highlights dominant applications such as intelligent tutoring, adaptive assessment, and VR/AR simulation, with emerging interest in large language model–based feedback and generative AI. Reported outcomes suggest moderate improvements in student performance and engagement, though gaps remain in areas such as ethics, equity, and long-term evaluation. Expert perspectives further underscore opportunities in micro-credentialing and AI-driven design studios, alongside challenges of data privacy, transparency, and faculty readiness. Overall, AI-enhanced strategies show strong potential to support personalization and innovation in engineering education, but sustainable adoption will require longitudinal studies, ethical safeguards, and targeted professional development.