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AI from Industry 4.0 to Industry 5.0: Engineering for Social Change

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 March 2026) | Viewed by 29519

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Editors

Dr. Ching-Hung Lee

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Guest Editor

School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an 710049, China
Interests: AI in healthcare; service design; smart retail; knowledge-based systems; digital transformation; e-government; sectoral systems of innovation
Special Issues, Collections and Topics in MDPI journals

Dr. Lingguo Bu

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Guest Editor

School of Software, Shandong University, Jinan, China
Interests: human factors; design methods; multimodal data fusion

Dr. Danni Chang

E-Mail Website
Guest Editor

School of Design, Shanghai Jiao Tong University, Shanghai, China
Interests: industrial design; smart product service system; design and human factors

Special Issue Information

Dear Colleagues,

The rapid evolution of Industry 4.0 has ushered in unprecedented advancements in automation, data exchange, and manufacturing technologies, primarily driven by artificial intelligence (AI). As we transition to Industry 5.0, the emphasis shifts from mere automation to a harmonious collaboration between humans and intelligent systems, focusing on sustainability, resilience, and societal wellbeing. This Special Issue aims to explore how AI and the upcoming Generative AI (GenAI) can be harnessed to engineer social change, fostering an inclusive, ethical, and human-centric industrial paradigm.

We are excited to announce a call for papers to be submitted to our upcoming Special Issue, dedicated to "AI From Industry 4.0 to Industry 5.0: Engineering for Social Change". This Special Issue will delve into the applications of AI technologies in Industry 4.0 and their evolution into the socially responsible frameworks of Industry 5.0, encompassing smart manufacturing and smart cities.

Scenes and topics of interest include, but are not limited to, the following:

AI-Driven Sustainability in Smart Manufacturing

Waste Reduction: Using AI for predictive analytics and process optimization to minimize material waste and enhance recycling.
Supply Chain Sustainability: Integrating AI to develop sustainable supply chain practices, such as reducing carbon footprints and improving resource allocation.

AI-driven Order Fulfillment and Smart Commercialization

Optimized Inventory Management: Using AI to predict demand and manage inventory levels efficiently.
Automated Order Processing: Implementing AI for real-time order processing and error reduction.
Personalized Customer Experience: Leveraging AI to analyze customer data and provide personalized shopping experiences and recommendations.

Human–AI Collaboration for Enhanced Workplace Safety and Productivity

Training and Skill Development: Developing AI-driven training programs that adapt to individual learning needs and enhance worker skills in a dynamic industrial setting.
Augmented Workforce: Utilizing AI-powered wearable technology and robotics to assist workers in hazardous environments.
Ergonomics and Health Monitoring: Implementing AI systems to monitor worker health, ergonomics, and fatigue to prevent workplace injuries.

Ethical AI and Governance Frameworks for Industry 5.0

AI for Resilience: Enhancing community and industrial resilience in the face of global challenges.
AI in Education and Skills Development: Advancing education and training to prepare the workforce for Industry 5.0.
Policy and Governance: Developing policies and governance models for ethical AI deployment.
Bias and Fairness: Developing methods to detect and mitigate biases in AI systems to ensure fair treatment of all stakeholders.
Transparency and Accountability: Creating frameworks that enhance the transparency of AI decision-making processes and establish accountability mechanisms.
Regulatory and Policy Recommendations: Proposing policy guidelines and regulatory measures that support ethical AI development and deployment in industrial applications.

We welcome original research articles, review papers, case studies, and perspectives that offer valuable insights into the application of AI technologies in the Industry 5.0 era and smart manufacturing and smart cities.

Dr. Ching-Hung Lee
Dr. Lingguo Bu
Dr. Danni Chang
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

Industry 4.0
Industry 5.0
digital product–service systems
smart manufacturing
human–AI collaboration
generative AI

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Published Papers (8 papers)

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Research

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38 pages, 2880 KB

Open AccessArticle

An Integrated Pipeline for Intent-Based Zero-Touch Networks: From Intent Translation to Minimal-Modification Reconfiguration

by DongJun Seo and KeeCheon Kim

Appl. Sci. 2026, 16(12), 5811; https://doi.org/10.3390/app16125811 - 9 Jun 2026

Viewed by 132

Abstract

To support Industry 5.0 smart factories that require ultra-low latency and high reliability, this paper proposes a three-layer Intent-Based Zero-Touch Networking (IBZTN) pipeline. Existing Intent-Based Networking (IBN)/Zero-Touch Networking (ZTN) studies often remain conceptual, while Graph Neural Network (GNN)-based Quality of Service (QoS) prediction and Deep Reinforcement Learning (DRL)-based reconfiguration are usually developed as separate modules. The proposed pipeline connects natural-language intent translation, feasibility prediction, and minimal-modification reconfiguration through a validated QoS contract and feasibility-aware closed-loop structure. Layer 1 converts intents into quantitative QoS profiles by combining Retrieval-Augmented Generation (RAG) with schema- and rule-based validation. Layer 2 evaluates feasibility using a Graph Isomorphism Network with Edge features (GINE)-based binary classifier. Layer 3 recovers infeasible states using a Behavior Cloning (BC) Proximal Policy Optimization (PPO) agent with Smart Traffic Engineering (TE) masking. In experiments with 300 natural language intents, RAG+Validator reduced Layer 1 constraint violations to 0.0% for most evaluated cloud and local Large Language Models (LLMs). The Layer 2 predictor achieved a 93.9% F1-score, and Layer 3 achieved an 87.8% recovery success rate with 9.8 average modifications and 5.56 ms inference latency. These results demonstrate the simulation-level potential of IBZTN and motivate future hardware-in-the-loop validation in industrial networks. Full article

(This article belongs to the Special Issue AI from Industry 4.0 to Industry 5.0: Engineering for Social Change)

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26 pages, 2820 KB

Open AccessArticle

Forensic Analysis of Manipulated Images and Videos

by Sergio A. Falcón-López, Llanos Tobarra, Antonio Robles-Gómez and Rafael Pastor-Vargas

Appl. Sci. 2025, 15(23), 12664; https://doi.org/10.3390/app152312664 - 29 Nov 2025

Cited by 1 | Viewed by 2513

Abstract

The transition from Industry 4.0 to Industry 5.0 emphasizes the need for ethical, transparent, and human-centric artificial intelligence systems. In this context, ensuring the authenticity of digital information has become crucial for maintaining societal trust. This study addresses the challenge of detecting manipulated multimedia content, including synthetic images, videos, and audio generated by artificial intelligence, commonly known as Deepfakes. We analyze and compare general-purpose and Deepfake-specific detection methods to assess their effectiveness in real-world scenarios. This work introduces a refined reference model that integrates both application-oriented and methodological criteria, grouping tools into Blind Forensic, Handcrafted Machine Learning, Deep Learning-based methods, and Toolkits. This structured taxonomy provides a clearer comparative framework than existing works, which typically classify detectors using only one of these dimensions. To ensure reproducible evaluation, all experiments were performed using the SAFL dataset, which consolidates real and synthetic multimedia content generated with publicly available tools under a unified protocol. Among the tested tools, Forensically achieved the highest accuracy in image forgery detection 86.9%, while Autopsy reached 69.5% among Deepfake-specific image detectors. In video analysis, Forensically obtained 98.6% accuracy, whereas Deepware Scanner achieved 91.2% as the most effective Deepfake-focused tool. These results highlight that general-purpose methods remain robust for images, while specialized detectors perform competitively in videos. Overall, the proposed model and dataset establish a consistent foundation for advancing hybrid detection strategies aligned with the ethical and transparent AI principles envisioned in Industry 5.0. Full article

(This article belongs to the Special Issue AI from Industry 4.0 to Industry 5.0: Engineering for Social Change)

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18 pages, 5280 KB

Open AccessArticle

A Drilling Debris Tracking and Velocity Measurement Method Based on Fine Target Feature Fusion Optimization

by Jinteng Yang, Yu Bao, Zumao Xie, Haojie Zhang, Zhongnian Li and Yonggang Li

Appl. Sci. 2025, 15(15), 8662; https://doi.org/10.3390/app15158662 - 5 Aug 2025

Viewed by 1254

Abstract

During unmanned drilling operations, the velocity of drill cuttings serves as an important indicator of drilling conditions, which necessitates real-time and accurate measurements. To address challenges such as the small size of cuttings, weak feature representations, and complex motion trajectories, we propose a novel velocity measurement method integrating small-object detection and tracking. Specifically, we enhance the multi-scale feature fusion capability of the YOLOv11 detection head by incorporating a lightweight feature extraction module, Ghost Conv, and a feature-aligned fusion module, FA-Concat, resulting in an improved model named YOLOv11-Dd (drilling debris). Furthermore, considering the robustness of the ByteTrack algorithm in retaining low-confidence targets and handling occlusions, we integrate ByteTrack into the tracking phase to enhance tracking stability. A velocity estimation module is introduced to achieve high-precision measurement by mapping the pixel displacement of detection box centers across consecutive frames to physical space. To facilitate model training and performance evaluation, we establish a drill-cutting splash simulation dataset comprising 3787 images, covering a diverse range of ejection angles, velocities, and material types. The experimental results show that the YOLOv11-Dd model achieves a 4.65% improvement in mAP@80 over YOLOv11, reaching 76.04%. For mAP@75–95, it improves by 0.79%, reaching 41.73%. The proposed velocity estimation method achieves an average accuracy of 92.12% in speed measurement tasks, representing a 0.42% improvement compared to the original YOLOv11. Full article

(This article belongs to the Special Issue AI from Industry 4.0 to Industry 5.0: Engineering for Social Change)

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30 pages, 6427 KB

Open AccessArticle

Artificial Intelligence of Things Infrastructure for Quality Control in Cast Manufacturing Environments Shedding Light on Industry Changes

by Cosmina-Mihaela Rosca, Gabriel Rădulescu and Adrian Stancu

Appl. Sci. 2025, 15(4), 2068; https://doi.org/10.3390/app15042068 - 16 Feb 2025

Cited by 21 | Viewed by 4446

Abstract

The transition from Industry 4.0 to 5.0 raises concerns about integrating advanced quality control measures by replacing humans. The biggest challenge of this transition is infrastructure compatibility. This paper proposes a remote collaboration solution via the Internet of Things (IoT) infrastructure. The study identifies challenges in implementing such strategies and highlights the importance of AI–human collaboration, aligning with Industry 5.0 concepts. This research integrates data from multiple visual sensors (cameras) and devices into an IoT framework to create a monitoring system. This system’s application focuses on ensuring cast quality control standards. The proposed artificial AI method provides compatibility for the entire infrastructure. The Nonconformity Indicator Algorithm (NIA) was designed for defect detection. NIA, developed using Azure Custom Vision Service, identified and classified manufactured product defects based on image analysis with an Accuracy of 98.18%, Precision of 98.44%, Recall of 96.56%, and F₁-Score of 97.50%. Furthermore, an IoT-based monitoring system was designed that employs real-time sensor fusion techniques for quality control in cast manufacturing environments. The system integrates data from multiple devices, including visual sensors like the ESP32-CAM, within an IoT framework powered by Azure IoT Hub and Azure Custom Vision Service. This infrastructure enables the compatibility of devices by facilitating communication via an Azure Event Grid Trigger integrated into an Azure Function through Azure IoT Hub. Full article

(This article belongs to the Special Issue AI from Industry 4.0 to Industry 5.0: Engineering for Social Change)

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28 pages, 2394 KB

Open AccessArticle

Industry 5.0: Are We Going to Accept Robots as Co-Workers in Office Environments? An Empirical Analysis

by Gozde Doven, Bulent Sezen, Kadir Alpaslan Demir and Yavuz Selim Balcioglu

Appl. Sci. 2025, 15(3), 1591; https://doi.org/10.3390/app15031591 - 5 Feb 2025

Cited by 5 | Viewed by 3709

Abstract

This research aims to assess the readiness of professionals working in offices to accept robots as co-workers, and to provide insight for robot developers and organizations in promoting robot acceptance. This study investigates the acceptance of robots in office environments using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, extended with a specific focus on perceived sociability. A two-country comparative approach was employed. The research involved participants from the United Kingdom and Turkey to explore differences on robot acceptance. Data were collected via a structured questionnaire with demographics, robot usage or intention to use, and robot appearance preferences, targeting working professionals in office environments. The findings highlight key factors influencing behavioral intentions to use robots, including performance expectancy, effort expectancy, social influence, and perceived sociability. Our research results indicate that robots will likely to be accepted in our future office work environments. The results provide actionable insights for designing socially interactive robots and utilizing them in diverse workplace environments. Future research directions include expanding the cultural scope and utilizing qualitative methods for the additional investigation of factors that may enhance our understanding of robot acceptance. Full article

(This article belongs to the Special Issue AI from Industry 4.0 to Industry 5.0: Engineering for Social Change)

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22 pages, 2251 KB

Open AccessArticle

Humanoid Robots in Tourism and Hospitality—Exploring Managerial, Ethical, and Societal Challenges

by Ida Skubis, Agata Mesjasz-Lech and Joanna Nowakowska-Grunt

Appl. Sci. 2024, 14(24), 11823; https://doi.org/10.3390/app142411823 - 18 Dec 2024

Cited by 19 | Viewed by 12147

Abstract

The paper evaluates the benefits and challenges of employing humanoid robots in tourism and hospitality, examining their roles, decision-making processes, human-centric approaches, and oversight mechanisms. Data will be collected from a variety of sources, including academic journals, websites of the companies where the robots operate, case studies, and news articles. Specific attention will be given to concrete examples of humanoid robots deployed in the tourism and hospitality sector, such as Connie, Spencer, and Henn-na Hotel’s robots. Robots highlight the potential to assume roles traditionally occupied by humans. The presence of humanoid robots also influences cultural practices and social interactions within the hospitality context. Humanoid robots also have the potential to improve equity and accessibility in the tourism and hospitality industry. The interaction between humans and humanoid robots can have psychological and emotional effects on both guests and employees. Finally, the usage of humanoid robots intersects with broader sustainability operational efficiency and customer satisfaction across various sectors within the tourism and hospitality industry. Introducing humanoid robots represents a challenge in innovation that holds promise for revolutionizing service delivery and guest experiences. Full article

(This article belongs to the Special Issue AI from Industry 4.0 to Industry 5.0: Engineering for Social Change)

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Other

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33 pages, 997 KB

Open AccessSystematic Review

Human-Centered XR Integration for STEM Education in New Zealand: A Systematic Review and Implementation Framework

by Muhammad Faisal Buland Iqbal, Kien T. P. Tran, Wei Qi Yan, Hazel Abraham and Minh Nguyen

Appl. Sci. 2026, 16(10), 5090; https://doi.org/10.3390/app16105090 - 20 May 2026

Cited by 1 | Viewed by 505

Abstract

This systematic review comprehensively explores the integration of Extended Reality (XR) technologies, comprising Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), into New Zealand’s STEM education framework. In alignment with PRISMA 2020 guidelines, we systematically analyzed 127 peer-reviewed studies from the Web of Science (n = 48), Scopus (n = 57), and Dimensions (n = 22) and incorporated 15 grey literature sources, resulting in 142 studies included in the review. Our meta-analysis found substantial improvements in student conceptual understanding from XR-enhanced STEM modules. Specifically, we observed an average increase of 23.4% when compared to traditional instructional methods (95 percent Confidence Interval: 18.7 to 28.1 percent, p < 0.001). These gains were especially prominent in interactive learning environments where immersive XR applications supported deeper engagement and the visualization of abstract STEM concepts. The qualitative synthesis highlighted several key barriers that limit effective XR integration. These include technological infrastructure gaps reported in 68 percent of reviewed studies, a critical need for educator training cited by 82 percent of studies, and curriculum alignment issues present in 57 percent of cases. Methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT) 2018, and the qualitative component employed a deductive thematic coding approach with inter-coder reliability verification. Successful institutional implementations were also identified. At Auckland University of Technology, XR-supported courses produced a 67 percent increase in student engagement, while Wellington High School achieved a 41 percent reduction in STEM achievement gaps through targeted XR interventions. Based on the evidence, we propose a four-phase implementation framework that addresses the technological, pedagogical, and policy requirements for sustainable XR adoption. These findings highlight the role of immersive technologies in supporting human-centered digital transformation and future skills development in the transition to Industry 5.0. The review contributes evidence-based insights that support the transition from technology-driven approaches associated with Industry 4.0 to the human-centered, socially oriented priorities of Industry 5.0. It also identifies critical research gaps, particularly in long-term learning outcomes and the integration of Mātauranga Māori within XR-enabled STEM environments. Full article

(This article belongs to the Special Issue AI from Industry 4.0 to Industry 5.0: Engineering for Social Change)

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26 pages, 3229 KB

Open AccessSystematic Review

Systematic Literature Review of Human–AI Collaboration for Intelligent Construction

by Juan Du, Ruoqi Gu, Xuan Tang and Vijayan Sugumaran

Appl. Sci. 2026, 16(2), 597; https://doi.org/10.3390/app16020597 - 7 Jan 2026

Viewed by 3009

Abstract

Artificial intelligence (AI) technology, serving as an indispensable component within intelligent construction systems, has become a cornerstone for driving the digital and intelligent transformation of the construction industry. Although AI demonstrates autonomous decision-making capabilities in specific operational contexts, because of the dynamic and often unforeseeable nature of construction workflows, human–AI collaboration (HAIC) still dominates the operational paradigm. This study undertakes a systematic review of the prior research on human–AI collaboration in intelligent construction. Through a bibliometric search, scientometric analysis, and in-depth literature classification, 191 highly cited articles in the past five years, which are in the top 10% by citation count within the dataset (as of May 2025, based on Scopus, Google Scholar, and WOS), were screened, and four research streams were formed based on a co-citation analysis and clustering, namely, construction robotics, productivity and safety, intelligent algorithms and modelling, and factors related to construction workers. Finally, a three-dimensional knowledge framework covering the technical layer, application layer, and management layer was constructed. Through this comprehensive synthesis, the study developed a human–AI collaboration knowledge framework in the field of construction science that integrates technology, scenarios, and management dimensions, revealing the co-evolutionary path of artificial intelligence technology and industry digital transformation. Full article

(This article belongs to the Special Issue AI from Industry 4.0 to Industry 5.0: Engineering for Social Change)

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