Volume: 3 Issue: Special Issue 1
Year: 2026, Page: 133-142, Doi: https://doi.org/10.70372/jeltp.v3.sp1.19
Advances in artificial intelligence now enable virtual assistants that enhance laboratory-based learning through conceptual diagnostics and adaptive support. This study presents an Artificial Intelligence–based Virtual Lab Assistant (AI-VLA) designed to improve learner readiness and performance in engineering laboratory environments. The system uses natural language understanding to conduct pre-lab conceptual assessments that identify gaps in understanding before students begin experimental work, and during laboratory activities it delivers context-aware guidance aligned with course outcomes to support procedural accuracy, error correction, and stronger integration of theory and practice. A design-based research methodology was adopted to iteratively refine the system and examine its practical value, with descriptive findings from a pilot implementation involving 120 undergraduate students indicating improvements in conceptual preparedness, procedural correctness, task completion, and learner confidence. Students engaging with the AI-VLA entered the laboratory better equipped, made fewer execution errors, and demonstrated deeper engagement with experimental activities. The framework incorporates responsible AI principles through data minimization, transparency, and fairness-aware mechanisms. Overall, the study illustrates how AI-driven virtual assistants can strengthen laboratory instruction by embedding real-time conceptual assessment and adaptive scaffolding into a learner-centered, outcome-oriented support ecosystem.
Keywords: Adaptive Feedback; Artificial Intelligence; Engineering Education; Natural Language Processing (NLP); Personalized Learning; Virtual Labs
Abdullah, M., Nageshwara Rao, G., Sowell, F. L., Nirmal, V., & Deb, S. (2024, June). Optimizing Virtual Learning: Advanced Recommendations for an AI Teaching Assistant. Paper presented at the 2024 ASEE Annual Conference & Exposition.
Ayre, D., Thomas, K., & Singh, R. (2023). Implementation of an artificial intelligence (AI) instructional support system in a virtual reality (VR) thermal-fluids laboratory. Engineering Education Journal, 45(2), 67–79.
Chheang, V., et al. (2024). Towards anatomy education with generative AI-based virtual assistants in immersive virtual reality environments. In Proceedings of the 2024 IEEE International Conference on Artificial Intelligence and Extended and Virtual Reality (AIxVR) (pp. 21–30).
Elmesalawy, M. M., Al-Khatib, A., & Hassan, F. (2022). AI virtual assistant for online laboratory experiments based on multi-threshold technique and genetic algorithm for analyzing student interaction activities. In Proceedings of the International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 278–284).
Glick, D., Miedijensky, S., & Zhang, H. (2024). Examining the effect of AI-powered virtual-human training on STEM majors’ self-regulated learning behavior. Frontiers in Education, 9, Article 1465207.
Groenewald, E. S., Naidoo, K., & Pillay, M. (2024). Virtual laboratories enhanced by AI for hands-on informatics learning. Journal of Informatics Education and Research, 12(1), 45–58.
Josphineleela, R., Rajalakshmi, P., & Kumar, S. (2023). Intelligent virtual laboratory development and implementation using the RASA framework. In Proceedings of the International Conference on Computing Methodologies and Communication (ICCMC) (pp. 152–159).
Lizano-Sánchez, L., Idoyaga, M., & Orduña, R. (2025). Students’ interactions with an artificial intelligence assistant in a remote chemistry laboratory. Frontiers in Education, 10, Article 1712743.
Liu, M., Wang, J., & Patel, A. (2024). Beyond traditional teaching: Large language models as simulated teaching assistants in computer science. In Proceedings of the ACM SIGCSE Technical Symposium on Computer Science Education (pp. 124–132).
Munawar, S., Hussain, A., & Khan, R. (2018). Move to smart learning environment: Exploratory research of challenges in computer laboratories and design of an intelligent virtual laboratory for eLearning technology. International Journal of Smart Learning Environments, 5(3), 201–215.
Murali, R., Sharma, P., & Iyer, S. (2024). Augmenting virtual labs with artificial intelligence for hybrid learning. In Proceedings of the IEEE Global Engineering Education Conference (EDUCON) (pp. 346–352).
Pérez-Lizano-Sánchez, L., et al. (2025). Teachers’ perspective on the use of artificial intelligence on remote experimentation. Frontiers in Education, 10, Article 1518896.
Ramasamy, V., Joseph, P., & Krishnan, A. (2024). Enhancing computer science education with learning assistants using the AI-empowered AIELA program. In Proceedings of the Frontiers in Education Conference (FIE) (pp. 1–6).
Zhang, X., et al. (2025). Adaptive intelligent tutoring systems for STEM education: Analysis of the learning impact and effectiveness of personalized feedback. Smart Learning Environments, 12(1).
S Nithya.AI-Based Virtual Lab Assistants for Personalized Engineering Education. Journal of Effective Teaching and Learning Practices. 2026;3(Sp1):133-142