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Abstract Enhancing the engagement and boosting the learning experience of students in foundational engineering courses have always been among the top priorities of professors. The rapid recent technological advancements of artificial intelligence (AI) have brought about promising pedagogical opportunities for engineering students with more efficient learning tools. This manuscript aims to study the prospects and obstacles in using a state-of-the-art natural language processing (NLP) model for obtaining a deeper understanding of core engineering courses. In this regard, several engineering examples were explored for analyzing the accuracy of quantitative results obtained from ChatGPT. In-class surveys were also conducted to assess the enthusiasm of students and enhanced interactivity of implementing ChatGPT-powered educational platform in solving engineering problems. We discovered that students can noticeably benefit from the key beneficial features offered by artificial intelligence including, but not limited to, real-time assistance, personalized feedback, and dynamic content generation. Survey results highlight the positive impact of implementation of ChatGPT on engineering students' scholarly performance and their broader learning experience. Despite all the undeniable advantages AI offers, it is essential to exercise caution and thorough analysis when evaluating the final results as the final outcomes are not always correct. Not only can incorrect results be discouraging, but they can also mislead students and hinder their potential ability to engage in deep, critical thinking. Regardless of the accuracy of the results, it is beyond doubt that ChatGPT is a valuable tool for educators in the field of mechanical engineering who are enthusiastic in offering an innovative approach to foster deep understanding and interest in fundamental engineering concepts. It is strongly believed that the limitations of the current ChatGPT model can be addressed and rectified in future iterations of the model, making the future of AI-driven education more promising, and establishing the generative models as flawless and reliable resources for both students and educators in STEM fields.
Rad et al. (Sun,) studied this question.
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