Volume: 3 Issue: Special Issue 1
Year: 2026, Page: 89-96, Doi: https://doi.org/10.70372/jeltp.v3.sp1.13
In the Industry 4.0 era, the challenges to be overcome by engineering education are twofold: covering increasingly complex theoretical concepts and developing practical, hands-on skills. Analog and Digital communication courses are characterized by abstract mathematical foundations and intricate signal processing requirements,, hence often presenting significant learning barriers for undergraduate students. This paper aims to present a novel pedagogical framework using MATLAB AI tools and Large Language Models (in particular MATLAB GPT) in order to bridge the gap between theoretical abstraction and practical application. Implementation of this framework was performed on two-core courses of the Electronics and Telecommunication engineering programme. A methodology that used MALAB AI for developing interactive simulations of modulation schemes (AM, FM, QPSK, M-ary PSK) and MATLAB GPT for drafting customized lecture notes, problem sets, and real-time contextually aware student support, was developed. Results from the pilot implementation with 37 students show a 15-20% increase in attendance and participation along with a 30-40% reduction in faculty preparation time. Survey data indicated that 92.2% of the students feel there is significant improvement in conceptual understanding because of real-time AI feedback. The following study validates a "human-in-the-loop" AI teaching methodology and presents a scalable model to modernize engineering curricula.
Keywords: Active learning, Digital communication, Engineering Education, Generative AI, Hybrid Intelligence
Alnaqbi, A. & Fouda M. (2023). Deep Learning for Predictive Hybrid Beamforming in V2X Communications, IEEE Global Communication Conference (GLOBECOM), 1-6.
Chan, C. K.Y., & Lee, K.K. W. (2023). The AI revolution in education: Will AI replace or assist teachers in higher education? Applied Sciences, 13(15), 8806.
EDUCAUSE. (2024). Exploring the opportunities and challenges with generative AI. EDUCAUSE Review.
Feuerriegla, S., Hartmann, J., Janiesch, C., & Zschech, P. (2023). Generative AI, Business & Information System Engineering, 66(1), 111-126.
Hwang, G.J., & Tu, Y.F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), 584.
IndiaAI. (2024). Generative AI and engineering education in India- An analysis. IndiaAI Portal.
MathWorks (2024). Communication System Toolbox- MATLAB & Simulink Solutions.
Qadir, J. (2023). The potential of generative AI in shaping engineering education: Opportunities and challenges. Journal of Engineering Education Transformations, 37(IS2), 439-445.
Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hicky, D.T., Huang, R., Agyemang, B., (2023). What if the devil is indeed in the details? Current trends, challenges, and opportunities of using generative AI in education. Smart Learning Environments, 10(1), 1-27.
Xu, S., Ouyang, F. (2022). A systematic review of AI role in the educational system based on a proposed conceptual framework. International Journal of Educational Technology in Higher Education, 19(1), 1-27.
Kartik Ramesh Patel. Use of MATLAB AI and MATLAB GPT for Teaching Material Preparation and Classroom Engagement Tools in Analog and Digital Communication Courses. Journal of Effective Teaching and Learning Practices. 2026;3(Sp1):89-96