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Advancing AI Applications in Education and Engineering: A Multidisciplinary Perspective
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Advancing AI Applications in Education and Engineering: A Multidisciplinary Perspective

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1053

Special Issue Editors

Prof. Dr. Chin-Wen Liao

E-Mail Website
Guest Editor
Department of Industrial Education and Technology, National Changhua University of Education, Changhua City 50007, Taiwan
Interests: technical and vocational education; applied science, organizational learning; artificial intelligence; augmented reality
Dr. Wei-Sho Ho

E-Mail Website
Guest Editor
Department of Industrial Education and Technology, National Changhua University of Education, Changhua City 50007, Taiwan
Interests: creativity and invention; case studies; cyber ethics; machine learning; interdisciplinary exploration; artificial intelligence; TRIZ and quality engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of artificial intelligence (AI) and interdisciplinary research is transforming education and engineering. This Special Issue explores the role of AI in technical and vocational education, applied sciences, and organizational learning, integrating cutting-edge technologies, such as machine learning, big data analytics, augmented reality (AR), virtual reality (VR), and embedded systems, to drive innovation.

In education, AI-powered adaptive learning and AR/VR-based training are revolutionizing skill development and workforce training. We welcome research on AI-enhanced learning environments and case studies highlighting the best practices in vocational education.

In engineering and intelligent systems, AI is advancing system-on-chip (SoC) applications, microprocessor designs, and embedded system control. The rise in electric and hybrid vehicles, intelligent control systems, AI-driven optimization, and big data analytics in industrial applications underscores the need for interdisciplinary collaboration. Research on AI-enhanced cybersecurity, ethical AI applications, and sustainable automation is highly encouraged.

This Special Issue invites original research, case studies, and empirical studies that push the boundaries of AI applications in education and engineering. By fostering interdisciplinary collaboration, this Special Issue aims to provide novel insights into the evolving role of AI in shaping the future of learning and industrial innovation.

Prof. Dr. Chin-Wen Liao
Dr. Wei-Sho Ho
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI)
  • machine learning and big data analytics
  • augmented reality (AR) and virtual reality (VR)
  • technical and vocational education innovation
  • embedded systems and intelligent control
  • electric and hybrid vehicle technologies
  • cybersecurity and ethical AI applications

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

23 pages, 2693 KB  
Article
Deep Learning for Student Behavior Detection in Smart Classroom Environments
by Jue Wang, Yuchen Sun and Shasha Tian
Information 2025, 16(11), 949; https://doi.org/10.3390/info16110949 - 3 Nov 2025
Viewed by 743
Abstract
The ongoing integration of information technology in education has rendered the monitoring of student behavior in smart classrooms essential for improving teaching quality and student engagement. Classroom environments frequently provide many problems, such as heterogeneous student behaviors, significant obstructions, loss of intricate details, [...] Read more.
The ongoing integration of information technology in education has rendered the monitoring of student behavior in smart classrooms essential for improving teaching quality and student engagement. Classroom environments frequently provide many problems, such as heterogeneous student behaviors, significant obstructions, loss of intricate details, and complications in recognizing diminutive targets. These limitations lead to current approaches remaining inadequate in accuracy and stability. This paper enhances YOLOv11 with the following improvements: developed the CSP-PMSA module to enhance contextual modeling in complex backgrounds, developed a scale-aware head (SAH) to improve the perception and localization of small targets via channel unification and scale adaptation, and introduced a Multi-Head Self-Attention (MHSA) mechanism to model global dependencies and positional bias across various subspaces, thereby enhancing the discrimination of visually analogous behaviors. The experimental findings indicate that in intricate classroom settings, the model attains mAP@50 and mAP@50–95 scores of 91.6% and 75.7%, respectively. This indicates enhancements of 2.7% and 2.6% compared to YOLOv11, and 4.6% and 3.6% relative to DETR, demonstrating remarkable detection precision and dependability. Additionally, the model was implemented on the Jetson Orin Nano platform, confirming its viability for real-time detection on edge devices and offering substantial assistance for practical implementations in smart classrooms. Full article
(This article belongs to the Special Issue Advancing AI Applications in Education and Engineering: A Multidisciplinary Perspective)
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