journal article·2026-02-18Open access

Industrial Robotics and Adaptive Control Systems in STEM Education: Systematic Review of Technology Transfer from Industry to Classroom and Competency Development Framework

Claudio Urrea

Applied Sciences· Volume 16· Issue 4

Abstract

The Fourth Industrial Revolution reshapes manufacturing and workforce demands, yet a persistent gap remains between industry needs and engineering education. While proficiency in industrial robotics, adaptive control, and automation becomes critical, traditional education struggles to bridge the theory–practice divide. This systematic review examines technology transfer from factory to classroom to develop authentic Industry 4.0 competencies. Following PRISMA 2020 guidelines, we synthesized 52 empirical studies (2019–2025) focusing on technology complexity, pedagogical approaches, and learning outcomes. Random-effects meta-analysis of 12 representative studies reveals large positive effects: Hedges’ g of 0.786 (95% CI: 0.726–0.846, p < 0.001) with homogeneous effects (I2 = 0.00%, p = 0.464), indicating robust generalizability. However, critical gaps emerged: only 7.7% employ actual industrial manipulators versus educational kits, adaptive control pedagogy remains limited, and fault-tolerant systems teaching receives minimal attention. Technology complexity analysis reveals clear progression from educational kits through semi-industrial platforms to industrial systems, with significant differential effects on transferable skills (r = 0.68, p < 0.001). This study proposes the ARC Framework integrating technology taxonomy, competency progression, pedagogical strategies, and assessment rubrics. Cost–effectiveness analysis demonstrates remote labs optimize impact-per-investment ratios ($45 vs. $280 per student), providing an evidence-based framework for technology transfer in engineering education.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Digital Transformation in Industry · Mechatronics Education and Applications · Teaching and Learning Programming

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Electrical Engineering, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estación Central, Santiago 9170020, Chile

References (100+)

Show source-provided references
1. Bonet, C.T., Abrisqueta, F.A.L., and González, A.G. (2025). Integration of Industry 5.0 Technologies in the Concrete Industry: An Analysis of the Impact of AI-Based Virtual Assistants. Appl. Sci., 15.10.3390/app151810147 ↗
2. Wu, Y., Xiao, L., and Du, X. (2025). Contrastive Geometric Cross-Entropy: A Unified Explicit-Margin Loss for Classification in Network Automation. Network, 5.10.3390/network5040045 ↗
3. Antunes, R., Aguiar, M.L., and Gaspar, P.D. (2023). A Dynamic STEM-Driven Approach through Mobile Robotics to Enhance Critical Thinking and Interdisciplinary Skills for Empowering Industry 4.0 Competencies. Technologies, 11.10.3390/technologies11060170 ↗
4. Competencies of a holistic engineer: Towards the development of a competency model and curriculum framework for future-proof engineers10.1080/03043797.2025.2584067 ↗
5. Onyia, U. (2025). Industry 5.0 and offsite construction in Nigeria: Required skills and competencies for effective construction management. Constr. Innov. Engl., 1–24.10.1108/CI-02-2025-0078 ↗
6. Capolupo, N., Maione, G., and Supino, S. (2025). The anatomy of human resource management (HRM) 5.0. A bibliometric review of practices and workforce skills. TQM J., 1–23.10.1108/TQM-07-2025-0402 ↗
7. Papacharalampopoulos, A., Karagianni, O.M., Fedeli, M., Lackner, P., Aleksandraviciene, G., Ippolito, M., Elorza, U., Schröder, A.J., and Stavropoulos, P. (2025). Training for Industry 5.0: Evaluating Effectiveness and Mapping Emerging Competences. Machines, 13.10.3390/machines13090825 ↗
8. Intelligent robotic systems in Industry 4.0: A review
9. Abdullah, M.N.L.Y., Chun, T.C., Ping, T.A., and Omar, A.F. (2025). Influences of Heutagogical Activities and Students’ Psychological Needs on Self-Determined Learning of Postgraduate Students. Sage Open, 15.10.1177/21582440251378421 ↗
10. Olszak, C.M., and Sączewska-Piotrowska, A. (2025). The Role of Higher Education Institutions in Shaping Sustainability and Digital Ethics in the Era of Industry 5.0: Universities as Incubators of Future Skills. Sustainability, 17.10.3390/su17198530 ↗
11. Integrating QMS model and balanced scorecard in higher education in the era of industry 5.0
12. Lu, Q., and Li, M. (2025). Fault Prediction Method Towards Rolling Element Bearing Based on Digital Twin and Deep Transfer Learning. Appl. Sci., 15.10.3390/app152312509 ↗
13. Cao, X., and Shi, K. (2025). A Health Status Identification Method for Rotating Machinery Based on Multimodal Joint Representation Learning and a Residual Neural Network. Appl. Sci., 15.10.3390/app15074049 ↗
14. Villegas-Ch, W., Gutierrez, R., and Govea, J. (2025). Digital Twin Integration in Metalworking: Enhancing Efficiency and Predictive Maintenance. Front. Mech. Eng., 11.10.3389/fmech.2025.1655565 ↗
15. Smart Watchdog for RISC-V: A Novel Spiking Neural Network Approach to Fault Detection10.1109/TCSII.2025.3583042 ↗
16. Wang, Z., Li, H., Du, Y., and Chen, Z. (2025). Learning-Based Fault Identification and Fault-Tolerant Control for Quadruped Robots with Small Samples. IEEE Trans. Ind. Electron., 1–11.10.1109/TIE.2025.3618921 ↗
17. Intelligent fault diagnosis of rotating machinery driven by physical information and contrastive learning under extreme sample imbalance conditions10.1088/1361-6501/ae1159 ↗
18. Prior-Informed Transductive Inference Framework: A chiller multi-source faulty sensor detection and diagnosis under information incompleteness conditions10.1016/j.jobe.2025.114210 ↗
19. Data-Driven Propulsion System Fault Diagnosis for Deep-Sea Submersible10.1109/JIOT.2025.3598106 ↗
20. Yang, Y., Wang, H., Nie, X., Wen, S., and Li, G. (2025). A Fault Diagnosis Method for the Train Communication Network Based on Active Learning and Stacked Consistent Autoencoder. Symmetry, 17.10.3390/sym17101622 ↗
21. Unsupervised Root Cause Analysis Using Twin Attentional-Generative Adversarial and Walrus-Based Deep Coupling Recurrent Autoencoder10.1002/dac.70241 ↗
22. Integrating sustainability into STEM education and career development: A scientometric and narrative review10.1186/s40594-025-00582-y ↗
23. The effects of educational robotics in STEM education: A multilevel meta-analysis10.1186/s40594-024-00469-4 ↗
24. A systematic review of studies on educational robotics
25. Sensor-Enabled Safety Systems for Human-Robot Collaboration: A Review10.1109/JSEN.2024.3496905 ↗
26. Bridging Research Gaps in Industry 5.0: Synergizing Federated Learning, Collaborative Robotics, and Autonomous Systems for Enhanced Operational Efficiency and Sustainability10.1109/ACCESS.2025.3541822 ↗
27. Zamora, P., Lozada, A., Buele, J., and Avils-Castillo, F. (2025). Robotics in higher education and its impact on digital learning. Front. Comput. Sci., 7.10.3389/fcomp.2025.1607766 ↗
28. Human-Robot collaboration in construction: Robot design, perception and Interaction, and task allocation and execution10.1016/j.aei.2025.103109 ↗
29. Learning and planning for optimal synergistic human-robot coordination in manufacturing contexts10.1016/j.rcim.2025.103006 ↗
30. Control of robot manipulators with uncertain closed architecture using neural networks10.1007/s11370-023-00507-0 ↗
31. Fault Joint Detection and Adaptive Fault-Tolerant Control of Legged Robots Under Joint Partial Failures10.1109/LRA.2025.3598620 ↗
32. Ensemble Deep Bidirectional GRU Network with Grey Wolf Optimizer for Spacecraft Fault Diagnosis10.1109/TIE.2025.3605461 ↗
33. Adaptive safety-critical control using a variable task energy tank for collaborative robot tasks under dynamic environments10.1016/j.rcim.2025.102964 ↗
34. Learning Fast, Tool-Aware Collision Avoidance for Collaborative Robots10.1109/LRA.2025.3579207 ↗
35. PRO-MIND: Proximity and Reactivity Optimization of Robot Motion to Tune Safety Limits, Human Stress, and Productivity in Industrial Settings10.1109/TRO.2025.3547270 ↗
36. AI and Digital Twin Federation-Based Flexible Safety Control for Human-Robot Collaborative Work Cell
37. Kazemiesfahani, M., Dilfanian, E., Monsarrat, B., and Hajzargarbashi, S. (2025). Design, Analysis, and Prototyping of a Multifunctional Digital Twin-Enabled Aerospace Drilling End-Effector Deployable by a Collaborative Robot. Sensors, 25.10.3390/s25247504 ↗
38. Zhao, B., Wu, C.D., Chang, L.J., Jiang, Y., and Sun, R.H. (2025). Research on Zero-Force control and collision detection of deep learning methods in collaborative robots. Displays, 102969.10.1016/j.displa.2025.102969 ↗
39. Advancing Industry 5.0: Collaborative Welding Robotics with Precision, Safety, and Flexibility10.1109/ACCESS.2025.3588515 ↗
40. Design of an Inductive Torque Sensor for Robotic Applications
41. Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas, Basic Books.
42. Kolb, D.A. (1984). Experiential Learning: Experience as the Source of Learning and Development, Prentice Hall.
43. Crawley, E.F., Malmqvist, J., Östlund, S., and Brodeur, D.R. (2007). Rethinking Engineering Education: The CDIO Approach, Springer.
44. Malisa, V., and Crnoki, B. (2026). Robot Applications in Theoretical and Practical University Education. Digital Transformation in Education and Artificial Intelligence Application. MoStart 2025; Communications in Computer and Information Science, Springer.10.1007/978-3-032-02801-3_6 ↗
45. Technological pedagogical content knowledge: A framework for teacher knowledge10.1111/j.1467-9620.2006.00684.x ↗
46. Calzada-Garcia, A., Victores, J.G., Naranjo-Campos, F.J., and Balaguer, C. (2025). Inverse Kinematics for Robotic Manipulators via Deep Neural Networks: Experiments and Results. Appl. Sci., 15.10.3390/app15137226 ↗
47. Zhang, X., Wu, J., and Zhao, C. (2025). Vision-Based Adaptive Control of Robotic Arm Using MN-MD3+BC. Appl. Sci., 15.10.20944/preprints202508.1715.v1 ↗
48. Makulavičius, M., Petronienė, J.J., Šutinys, E., Bučinskas, V., and Dzedzickis, A. (2025). Industrial Robotic Setups: Tools and Technologies for Tracking and Analysis in Industrial Processes. Appl. Sci., 15.10.3390/app151810249 ↗
49. Yan, Y., Yi, D., Lu, H., and Gao, L. (2025). Control and Applications of Intelligent Unmanned Aerial Vehicles. Electronics, 14.10.3390/electronics14020375 ↗
50. Programmable Logic Controller (PLC) Trainer Kit: Design and Implementation
51. Al-Najari, M.A., Hassan, M.K., and Ibrahim, R. (2025). Dynamic PLC Parameter Tuning Using Hybrid PSO-MANFIS Algorithm for Industrial Control. Automation, 6.10.3390/automation6040083 ↗
52. Antonelli, M.G., Zobel, P.B., Mattei, E., and Stampone, N. (2024). A Methodology for the Mechanical Design of Pneumatic Joints Using Artificial Neural Networks. Appl. Sci., 14.10.3390/app14188324 ↗
53. Chen, H.-C., Li, S.-A., Chang, T.-H., Feng, H.-M., and Chen, Y.-C. (2024). Hybrid Centralized Training and Decentralized Execution Reinforcement Learning in Multi-Agent Path-Finding Simulations. Appl. Sci., 14.10.3390/app14103960 ↗
54. RBF Neural Network-Based Adaptive Control for Robotic Systems with Input Saturation
55. Li, J., Jiang, H., Gao, M., Li, S., Wang, Z., Pang, Z., Zhang, Y., and Jiao, Y. (2025). Research on Iterative Learning Method for Lower Limb Exoskeleton Rehabilitation Robot Based on RBF Neural Network. Appl. Sci., 15.10.3390/app15116053 ↗
56. Neuromorphic Controller Design for Adaptive Control of Robotic Manipulators
57. Fixed-Time Reinforcement Learning Control for Robotic Manipulators with Model Uncertainties
58. Yao, K.-C., Hsu, L.-C., Fang, J.-S., Chen, Y.-J., and Guo, Z.-K. (2025). The Establishment and Evaluation Model of the Thematic Deep-Learning Teaching Module. Appl. Sci., 15.10.3390/app15052335 ↗
59. Lawpanom, R., Songpan, W., and Kaewyotha, J. (2024). Advancing Facial Expression Recognition in Online Learning Education Using a Homogeneous Ensemble Convolutional Neural Network Approach. Appl. Sci., 14.10.3390/app14031156 ↗
60. Zhu, X., Bi, L., Wu, T., Zhang, C., and Wu, J. (2025). Causal Correction and Compensation Network for Robotics: Applications and Validation in Continuous Control. Appl. Sci., 15.10.3390/app15179628 ↗
61. Simon, J., Gogolák, L., and Sárosi, J. (2024). Deep Reinforcement Learning-Assisted Teaching Strategy for Industrial Robot Manipulator. Appl. Sci., 14.10.3390/app142310929 ↗
62. Online Reinforcement Learning-Based Adaptive Control for Nonlinear Systems with Unknown Dynamics
63. Alginahi, Y.M., Alsulami, M.H., and Rifai, K. (2025). Reinforcement Learning Applications in Industrial Automation: A Comprehensive Review. Machines, 13.10.3390/machines13121140 ↗
64. Urrea, C. (2025). Adaptive Multi-Objective Reinforcement Learning for Real-Time Manufacturing Robot Control. Machines, 13.10.3390/machines13121148 ↗
65. Adaptive Fuzzy Finite-Time Control for Uncertain Nonlinear Systems with Asymmetric Time-Varying Constraints
66. Command filter-based adaptive fuzzy control for constrained robotic systems
67. Teaching Model Predictive Control: What, When, Where, Why, Who, and How?10.1109/MCS.2024.3402908 ↗
68. Adaptive Robust Backstepping Control Based on Radial Basis Neural Network for Linear Motor Drives10.1080/00207179.2025.2495124 ↗
69. Mao, Y., Chen, Z., and Wang, X. (2025). Liquid-Augmented Model Predictive Control for Adaptive Trajectory Tracking. Electronics, 14.
70. Safety-Critical Neural Model Predictive Control for Robotic Systems
71. Convex optimization-based adaptive control for robotic systems
72. Autsou, S. (2025). Application of Fuzzy Logic for Collaborative Robot Control. Electronics, 14.10.3390/electronics14204029 ↗
73. Bricher, D., and Müller, A. (2025). Analysis of Deep-Learning Methods in an ISO/TS 15066-Compliant Human-Robot Safety Framework. Sensors, 25.10.3390/s25237136 ↗
74. Fernandez-Vega, M., Alfaro-Viquez, D., Zamora-Hernandez, M., Garcia-Rodriguez, J., and Azorin-Lopez, J. (2025). Transforming Robots into Cobots: A Sustainable Approach to Industrial Automation. Electronics, 14.10.3390/electronics14112275 ↗
75. Halvorsen, T.S., Tyapin, I., and Jha, A. (2025). Autonomous Textile Sorting Facility and Digital Twin Utilizing an AI-Reinforced Collaborative Robot. Electronics, 14.10.3390/electronics14132706 ↗
76. Sulaiman, M., Rahman, A., and Hassan, N. (2025). Learning-Based Impedance Control for Human-Robot Collaborative Tasks. Front. Robot. AI, 12.10.3389/frobt.2025.1665267 ↗
77. Zhu, H., Wang, J., and Gao, R. (2025). EEG-Based Inverse Reinforcement Learning for Safety-Oriented Global Path Planning in Dynamic Environments. Appl. Sci., 15.10.3390/app15116163 ↗
78. Terras, N., Pereira, F., Silva, A.R., Santos, A.A., Lopes, A.M., da Silva, A.F., Cartal, L.A., Apostolescu, T.C., Badea, F., and Machado, J. (2025). Integration of Deep Learning Vision Systems in Collaborative Robotics for Real-Time Applications. Appl. Sci., 15.10.3390/app15031336 ↗
79. Ma, J., Chen, H., Liu, X., Yang, Y., and Huang, D. (2025). Adaptive Impedance Control of a Human-Robotic System Based on Motion Intention Estimation and Output Constraints. Appl. Sci., 15.10.3390/app15031271 ↗
80. Multilateral Learning-Based Adaptive Control for Affine Nonlinear Systems
81. Multi-robot Collaborative Manufacturing Driven by Digital Twins: Advancements, Challenges, and Future Directions10.1016/j.jmsy.2025.06.014 ↗
82. Enhanced Density-driven Control of Multi-agent UAV Systems for Efficient Victim Detection in Large-scale Disaster Scenarios10.1007/s12555-024-1020-z ↗
83. Sliding mode adaptive control with chattering reduction for industrial robots
84. Robust Adaptive Safety Control of Uncertain Nonlinear Systems and Its Application to Differential Robots10.1002/rnc.7464 ↗
85. Backstepping adaptive control with disturbance observer for industrial robots
86. Neural Network-Based State Observation Utilizing a History-of-Error Performance Index10.1016/j.conengprac.2025.106578 ↗
87. Prescribed performance adaptive control for robotic manipulators
88. Barrier Lyapunov function-based adaptive control for constrained robotic systems
89. Broad Learning System-Based Model-Free Adaptive Robust Control for Hypersonic Morphing Aircraft with Appointed-Time Prescribed Performance10.1016/j.engappai.2024.109962 ↗
90. Passivity-based adaptive control for underactuated robotic systems
91. Model-Free Adaptive Iterative Learning Control for Nonlinear Systems with Time-Varying Constraints10.1007/s12555-024-0349-7 ↗
92. Gaussian process-based adaptive control for uncertain robotic systems
93. Learning Safe Data-Driven Control Barrier Functions for Unknown Continuous Systems10.1109/LCSYS.2025.3584079 ↗
94. Interval type-2 fuzzy adaptive control for industrial manipulators
95. Adaptive Safety Control for Constrained Uncertain Robotic Systems: A Neural Network-Based High-Order Control Barrier Function Approach
96. Ozer, T., and Cinar, A.C. (2025). Deep Learning-Based Robotic Stacking System with Adaptive Control for Industrial Applications. Electronics, 14.
97. Ayyildiz, E., Karaca, T.K., Cari, M., Kavus, B.Y., and Aydin, N. (2025). Smart Risk Assessment and Adaptive Control Strategy Selection for Human-Robot Collaboration in Industry 5.0: An Intelligent Multi-Criteria Decision-Making Approach. Processes, 13.10.3390/pr13103206 ↗
98. Safety-Driven Optimisation of Human-Robot Collaborative Assembly Line Balancing10.1080/00207543.2025.2469288 ↗
99. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews10.1136/bmj.n71 ↗
100. Mendez, E., Ochoa, O., Olivera-Guzman, D., Soto-Herrera, V.H., Luna-Sánchez, J.A., Lucas-Dophe, C., Lugo-Del-Real, E., Ayala-Garcia, I.N., Perez, M.A., and González, A. (2024). Integration of Deep Learning and Collaborative Robot for Assembly Tasks. Appl. Sci., 14.10.3390/app14020839 ↗