A framework for integrating ChatGPT in wireless communication engineering course from basic concepts to Scilab coding

Abstract

Wireless communication is a classic engineering course in the electronics and communication undergraduate program, and this course also has the potential to create the next generation of industry-ready communication engineers, provided they have a clean understanding of complex communication concepts. That is why this framework aimed to integrate ChatGPT into a wireless communication engineering course to make a new path of innovative and flexible learning through ChatGPT. The prompt developments in generative artificial intelligence have paved the way for innovative teaching methodologies and practices, particularly in higher education. This framework for integrating ChatGPT in wireless communications investigates the potential of utilising generative artificial intelligence tools, specifically ChatGPT, to enhance the teaching and learning experience of the wireless communication course. The proposed framework focuses on improving student engagement, facilitating personalised learning, and making it easier to comprehend complex concepts in wireless communications by integrating ChatGPT. The results indicate that ChatGPT’s responses to user queries are prompt and mostly align with standard textbook concepts of wireless communications. The key focus of this framework is Scilab open-source code generation through ChatGPT, and this leads to effective understanding of wireless concepts easily. The generative artificial intelligence tools heightened the productivity of the learning procedure and empowered educators to concentrate on advanced pedagogical activities. Unlike general discussions on ChatGPT adoption in STEM education, the proposed framework introduces a structured wireless communication laboratory design workflow that integrates artificial intelligence assisted coding with instructor validation checkpoints. A 16.6% improvement in the mean has been observed from pre- to post-translation of the framework.The recommended framework entitlements are that generative artificial intelligence technologies have excessive potential to revolutionise how wireless communication engineering is taught, making the learning skill further interactive, personalised, and competent.

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