Volume: 2 Issue: 3
Year: 2025, Page: 155-167, Doi: https://doi.org/10.70372/jeltp.v2.i3.23
Engineering Education 4.0 (EE-4) represents the latest paradigm in engineering pedagogy, uniting time-honored instructional methods with cutting-edge technologies most notably artificial intelligence (AI). As AI underpins the Fourth Industrial Revolution, it is imperative that engineering curricula inculcate both theoretical understanding and practical proficiency in AI concepts and applications. This responsibility falls squarely on educational institutions, which must ensure that graduates emerge not only conversant with AI but capable of leveraging its capabilities to address complex, real-world challenges. In this study, we first establish a robust framework for evaluating engineering knowledge within AI-enhanced instruction by adapting the Technological, Pedagogical, and Content Knowledge (TPACK) model. This framework guides the design and deployment of AI-based instructional tools and provides a metric for assessing their pedagogical effectiveness. We then implement an AI-driven platform utilizing the conversational agent ChatGPT as a testbed for facilitating student engagement with authentic engineering problems. A cohort of undergraduate students at RK University, Rajkot, was invited to interact with the platform over the course of a semester, applying AI-guided insights to laboratory exercises, design projects, and collaborative assignments. Quantitative and qualitative analyses were performed to compare the framework’s predicted levels of tool efficacy against observed student outcomes. Results indicate that, while theoretical evaluations of the AI tool forecast high pedagogical value, empirical evidence demonstrates that student performance improved commensurately, fulfilling the core objectives of Engineering Education 4.0. These findings underscore the obligation of engineering programs to integrate AI tools systematically, thereby preparing graduates to navigate and shape the rapidly evolving technological landscape.
Keywords: TPKS, Artificial Intelligence, ChatGPT, Engineering Education System, Industrial revolution 4.0
Awati, J. S., Desai, S. S., & Tope, S. (2020). Mind Mapping: An Effective Teaching Learning Evaluation Tool in Engineering Education. Journal of Engineering Education Transformations, 33(Special Issue).
Lathigara, A., Gupta, L., Binu, K. G., & Kumar, V. (2021). Sustaining motivation of engineering students in india by managing their academic & affective needs. Journal of Engineering Education Transformations, 34.
Richter, O. Z., Juarros, V. I. M., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: where are the educators? International Journal of Educational Technology in Higher Education, (16), 6.
Mirzana, I. M., & Lal, M. R. (2020). Engineers for the Future. Journal of Engineering Education Transformations, 33(Special Issue).
Tanna, P., Bhatt, N., & Patel, S. (2020). An Innovative Approach for Learning and Evaluating Programming-Oriented Courses. Journal of Engineering Education Transformations, 33(3), 62-74.
Jiao, P., Ouyang, F., Zhang, Q., & Alavi, A. H. (2022). Artificial intelligence-enabled prediction model of student academic performance in online engineering education. Artificial Intelligence Review, 55(8), 6321-6344.
Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism & Mass Communication Educator, 78(1), 84-93.
Rahaman, M. S., Ahsan, M. T., Anjum, N., Terano, H. J. R., & Rahman, M. M. (2023). From ChatGPT-3 to GPT-4: a significant advancement in ai-driven NLP tools. Journal of Engineering and Emerging Technologies, 2(1), 1-11.
Topsakal, O., & Topsakal, E. (2022). Framework for a foreign language teaching software for children utilizing AR, voicebots and ChatGPT (Large Language Models). The Journal of Cognitive Systems, 7(2), 33-38.
Alghazo, M., Ahmed, V., & Bahroun, Z. (2025, February). Exploring the applications of artificial intelligence in mechanical engineering education. In Frontiers in Education (Vol. 9, p. 1492308). Frontiers Media SA.
Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468.
Valtonen, T., Sointu, E., Kukkonen, J., Kontkanen, S., Lambert, M. C., & Mäkitalo-Siegl, K. (2017). TPACK updated to measure pre-service teachers’ twenty-first century skills. Australasian Journal of Educational Technology, 33(3).
Sumathi, R., Savithramma, R. M., & Ashwini, B. P. (2023). Curriculum Compliance Improvement Model for Addressing Program Outcomes in Engineering Education. Journal of Engineering Education Transformations, 37(1).
Nuthanapati, F. A. A. K., Cherukuri, S. B. K., & Dukkipati, T. C. N. R. (2022). Education Process Re-engineering through Spectral Pyramid Framework to Achieve Excellence in Engineering Education. Journal of Engineering Education Transformations, 35 (Special Issue 1).
Riaz Kurbanali Israni. (2024). Inter-retaliatory Student Projects as Part of Evaluating Practical Knowledge with the Assist of Novel Assessment Technique. Journal of Effective Teaching and Learning Practices, 1(1):31-40
Fuchs, K. (2023, May). Exploring the opportunities and challenges of NLP models in higher education: is ChatGPT a blessing or a curse? In Frontiers in Education (Vol. 8, p. 1166682). Frontiers.
Bosman, L., Kotla, B., Madamanchi, A., Bartholomew, S., & Byrd, V. (2023). Preparing the future entrepreneurial engineering workforce using web-based AI-enabled tools. European Journal of Engineering Education, 48(5), 972-989.
Rithvik, M., & Haritha, D. (2020). Student Learning Centric Methodology: An aid to Innovative Teaching and Learning Process. Journal of Engineering Education Transformations, 33 (Special Issue).
Baalsrud Hauge, J., & Jeong, Y. (2024). Does the improvement in AI tools necessitate a different approach to engineering education? In Sustainable Production through Advanced Manufacturing, Intelligent Automation and Work Integrated Learning (pp. 709-718). IOS Press.
Fitria, T. N. (2021, December). Artificial intelligence (AI) in education: Using AI tools for teaching and learning process. In Prosiding seminar nasional & call for paper STIE AAS (pp. 134-147).
Pham, T., Nguyen, T. B., Ha, S., & Ngoc, N. T. N. (2023). Digital transformation in engineering education: Exploring the potential of AI-assisted learning. Australasian Journal of Educational Technology, 39(5), 1-19.
Dr. Riaz Kurbanali Israni, Yuvraj Kaushik Jani. The Obligation of Artificial Intelligence (AI) Tools in Engineering Education 4.0. Journal of Effective Teaching and Learning Practices. 2025;2(3):155-167