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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: 30 September 2026 | Viewed by 4650

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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Published Papers (5 papers)

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Research

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19 pages, 1310 KB  
Article
Security and Safety Education from the Polish Context to Reinforce Social Education at a Time of Global Uncertainty
by Małgorzata Gawlik-Kobylińska, José A. García-Berná, Dorota Domalewska, Andrzej Pieczywok, Peter Holowka and Juan Manuel Carrillo de Gea
Information 2026, 17(4), 358; https://doi.org/10.3390/info17040358 - 8 Apr 2026
Viewed by 357
Abstract
This study advances the conceptual and practical scope of social education by integrating Security and Safety Education (SSE) categories into its theoretical foundation. We demonstrate that SSE encompasses multidimensional areas highly relevant to social education and offer a structured competence model to guide [...] Read more.
This study advances the conceptual and practical scope of social education by integrating Security and Safety Education (SSE) categories into its theoretical foundation. We demonstrate that SSE encompasses multidimensional areas highly relevant to social education and offer a structured competence model to guide curriculum design. Using a mixed-methods approach, 2926 Web of Science publications were analysed through an NVivo Word Frequency Query to identify key domains associated with security and safety. The temporal scope of the corpus (2019–2021) provides a coherent analytical baseline, capturing intensified security and health-related discourse during the COVID-19 period while preceding geopolitical disruptions that could otherwise distort thematic patterns. The results show that security is associated with broad social and geopolitical issues, including food, political, economic, public, national, and international affairs, as well as health and information. In contrast, safety is mainly linked to transport-related concerns, although both domains converge in areas such as health, social, public, national, and information matters. These findings indicate that SSE encompasses multidimensional areas relevant to social education. To support curricular integration, we propose an eMEDIATOR-derived competence model that structures SSE content into measurable, outcomes-based components. Ultimately, this research provides actionable tools to elevate social education and promote active, informed citizenship in times of global uncertainty. Full article
(This article belongs to the Special Issue Advancing AI Applications in Education and Engineering: A Multidisciplinary Perspective)
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17 pages, 980 KB  
Article
Dual-View Sign Language Recognition via Front-View Guided Feature Fusion for Automatic Sign Language Training
by Siyuan Jing and Gaorong Yan
Information 2026, 17(2), 158; https://doi.org/10.3390/info17020158 - 5 Feb 2026
Viewed by 492
Abstract
The foundation of an automatic sign language training (ASLT) system lies in word-level sign language recognition (WSLR), which refers to the translation of captured sign language signals into sign words. However, two key issues need to be addressed in this field: (1) the [...] Read more.
The foundation of an automatic sign language training (ASLT) system lies in word-level sign language recognition (WSLR), which refers to the translation of captured sign language signals into sign words. However, two key issues need to be addressed in this field: (1) the number of sign words in all public sign language datasets is too small, and the words do not match real-world scenarios, and (2) only single-view sign videos are typically provided, which makes solving the problem of hand occlusion difficult. In this work, we design an efficient algorithm for WSLR which is trained on our recently released NationalCSL-DP dataset. The algorithm first performs frame-level alignment of dual-view sign videos. A two-stage deep neural network is then employed to extract the spatiotemporal features of the signers, including hand motions and body gestures. Furthermore, a front-view guided early fusion (FvGEF) strategy is proposed for effective fusion of features from different views. Extensive experiments were carried out to evaluate the algorithm. The results show that the proposed algorithm significantly outperformed existing dual-view sign language recognition algorithms. Compared with several state-of-the-art methods, the proposed algorithm achieves Top-1 accuracy on the NationalCSL6707 dataset that is 10.29 and 11.38 higher than MViT and CNN + Transformer, respectively. Full article
(This article belongs to the Special Issue Advancing AI Applications in Education and Engineering: A Multidisciplinary Perspective)
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18 pages, 587 KB  
Article
Bridging the Engagement–Regulation Gap: A Longitudinal Evaluation of AI-Enhanced Learning Attitudes in Social Work Education
by Duen-Huang Huang and Yu-Cheng Wang
Information 2026, 17(1), 107; https://doi.org/10.3390/info17010107 - 21 Jan 2026
Viewed by 519
Abstract
The rapid adoption of generative artificial intelligence (AI) in higher education has intensified a pedagogical dilemma: while AI tools can increase immediate classroom engagement, they do not necessarily foster the self-regulated learning (SRL) capacities required for ethical and reflective professional practice, particularly in [...] Read more.
The rapid adoption of generative artificial intelligence (AI) in higher education has intensified a pedagogical dilemma: while AI tools can increase immediate classroom engagement, they do not necessarily foster the self-regulated learning (SRL) capacities required for ethical and reflective professional practice, particularly in human-service fields. In this two-time-point, pre-post cohort-level (repeated cross-sectional) evaluation, we examined a six-week AI-integrated curriculum incorporating explicit SRL scaffolding among social work undergraduates at a Taiwanese university (pre-test N = 37; post-test N = 35). Because the surveys were administered anonymously and individual responses could not be linked across time, pre-post comparisons were conducted at the cohort level using independent samples. The participating students completed the AI-Enhanced Learning Attitude Scale (AILAS); this is a 30-item instrument grounded in the Technology Acceptance Model, Attitude Theory and SRL frameworks, assessing six dimensions of AI-related learning attitudes. Prior pilot evidence suggested an engagement regulation gap, characterized by relatively strong learning process engagement but weaker learning planning and learning habits. Accordingly, the curriculum incorporated weekly goal-setting activities, structured reflection tasks, peer accountability mechanisms, explicit instructor modeling of SRL strategies and simple progress tracking tools. The conducted psychometric analyses demonstrated excellent internal consistency for the total scale at the post-test stage (Cronbach’s α = 0.95). The independent-samples t-tests indicated that, at the post-test stage, the cohorts reported higher mean scores across most dimensions, with the largest cohort-level differences in Learning Habits (Cohen’s d = 0.75, p = 0.003) and Learning Process (Cohen’s d = 0.79, p = 0.002). After Bonferroni adjustment, improvements in the Learning Desire, Learning Habits and Learning Process dimensions and the Overall Attitude scores remained statistically robust. In contrast, the Learning Planning dimension demonstrated only marginal improvement (d = 0.46, p = 0.064), suggesting that higher-order planning skills may require longer or more sustained instructional support. No statistically significant gender differences were identified at the post-test stage. Taken together, the findings presented in this study offer preliminary, design-consistent evidence that SRL-oriented pedagogical scaffolding, rather than AI technology itself, may help narrow the engagement regulation gap, while the consolidation of autonomous planning capacities remains an ongoing instructional challenge. Full article
(This article belongs to the Special Issue Advancing AI Applications in Education and Engineering: A Multidisciplinary Perspective)
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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
Cited by 2 | Viewed by 1938
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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Review

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28 pages, 1859 KB  
Review
Fluency Illusion: A Review on Influence of ChatGPT in Classroom Settings
by Sachin Kumar, Anna Mikayelyan and Olga Vorfolomeyeva
Information 2026, 17(3), 299; https://doi.org/10.3390/info17030299 - 19 Mar 2026
Viewed by 709
Abstract
The rapid adoption of generative artificial intelligence tools such as ChatGPT in educational settings has generated both enthusiasm and concern regarding their influence on student learning. While several studies report improvements in efficiency, confidence, and perceived understanding, evidence for durable conceptual learning and [...] Read more.
The rapid adoption of generative artificial intelligence tools such as ChatGPT in educational settings has generated both enthusiasm and concern regarding their influence on student learning. While several studies report improvements in efficiency, confidence, and perceived understanding, evidence for durable conceptual learning and knowledge transfer remains mixed. This article examines these tensions through the concept of fluency illusion, a cognitive phenomenon in which information that is easy to process is mistakenly judged as being well understood. Using a narrative conceptual review approach, this study synthesizes findings from 41 publications identified through searches of Google Scholar, Scopus, Web of Science, and ERIC covering the period from 2022 to early 2026. The reviewed literature includes 28 empirical studies, nine conceptual or theoretical analyses, and four review articles addressing the use of ChatGPT in educational contexts. Across domains such as writing and language learning, STEM problem solving, feedback and tutoring, and assessment, the literature shows a recurring pattern in which fluent AI-generated responses increase learners’ confidence without consistently improving deeper conceptual understanding. Drawing on research from cognitive psychology and metacognition, this review proposes an integrative conceptual account of how fluent AI output may shape learners’ judgments of understanding and influence their engagement with learning tasks. The paper concludes by discussing implications for instructional design, assessment practices, and metacognitive scaffolding, and outlines directions for future research aimed at empirically examining the proposed framework and identifying strategies to reduce fluency-driven misjudgments while preserving the potential benefits of generative AI in education. Full article
(This article belongs to the Special Issue Advancing AI Applications in Education and Engineering: A Multidisciplinary Perspective)
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