Author(s)

Qi Chen ¹, Jianbo He ¹, Zhuo Wang ¹, Mingzhi Chen ¹

Affiliation(s)

¹ School of Mechanical Engineering, University of Shanghai for Science and Technology, Jungong Road No.516, Shanghai, China

Corresponding Author

Zhuo Wang

ABSTRACT

The convergence of Virtual Laboratories (VL) and Artificial Intelligence (AI) is fundamentally transforming PLC education paradigms, offering scalable, personalized, and sustainable learning solutions. This educational technology revolution not only aligns with the transformation trends of teaching models in the new media era, but also presents a significant "double-edged sword" effect at the practical level: On one hand, VL technology, based on 3D simulation environments, has successfully overcome three major challenges faced by traditional physical laboratories—equipment costs (saving 90% per unit), scalability, and safety control. In Germany, the integration of digital twin modules demonstrated a 40% improvement in learning efficiency. On the other hand, AI-powered intelligent diagnostic systems and adaptive learning algorithms have achieved truly personalized teaching, with empirical data from Indian educational institutions showing a 25% improvement in course completion rates. However, similar to the challenges faced by HoloLens 2 in industrial training, VL+AI technologies also face key challenges such as optimizing human-machine interaction and lowering professional thresholds. From a sustainable development perspective, this model not only promotes educational inclusivity through open-source ecosystems (such as OpenPLC), but also achieves a breakthrough in environmental benefits by reducing practical training electronic waste by 60%. To maximize the technological benefits in the future, a systematic solution needs to be developed that includes infrastructure upgrades, privacy protection mechanisms, and blended course design, thereby driving the deep integration of educational technology innovation with traditional teaching systems.

KEYWORDS

Virtual Laboratory (VL), Artificial Intelligence (AI), PLC Education, Industry4.0, Cost Efficiency, Adaptive Learning Systems

CITE THIS PAPER

Qi Chen, Jianbo He, Zhuo Wang, Mingzhi Chen, Application of Virtual Laboratory Technology and Artificial Intelligence in PLC Education: Opportunities, Challenges, and Future Directions. Transactions on Comparative Education (2025) Vol. 7: 33-38. DOI: http://dx.doi.org/10.23977/trance.2025.070205.

REFERENCES

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[3] Müller, A., et al. (2023). Open-Source Platforms for PLC Simulation: A Case Study of OpenPLC. International Conference on Engineering Education, 78-85.
[4] Gupta, R., & Wang, L. (2021). AI-Driven Adaptive Learning Systems for Industrial Automation Training. IEEE Transactions on Education, 64(4), 512-520.
[5] UNESCO. (2023). Digital Education Initiatives in Sub-Saharan Africa. Paris: UNESCO Publishing.
[6] IEEE. (2023). Ethical Guidelines for AI in Education. Piscataway: IEEE Standards Association.
[7] Festo Didactic. (2022). Impact Report on Virtual Labs in Vocational Training. Retrieved from https://www.festo-didactic.com

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