ChatGPT in Engineering Teaching & Learning: Student and Faculty Perspective

Abstract

The rise of Artificial Intelligence (AI) in education is reshaping traditional learning environments, with AI-powered tools increasingly being integrated into teaching and learning processes. AI chatbots are being used in engineering education for personalized concept explanations, generating practice problems, and assisting students in formulating their thoughts on complex engineering topics [1]. AI tools offer opportunities to enhance learning outcomes and teaching strategies, yet they also present challenges. This study focuses on the integration of AI-driven learning in engineering education at an English-speaking university, examining both student and faculty perspectives across various engineering disciplines, including, but not limited to chemical, civil, electrical, mechanical engineering and engineering management. While AI tools can enhance educational experiences by providing personalized feedback, generating content, and assisting with research tasks, they also pose risks of plagiarism and misuse, as students may rely on AI-generated outputs without proper oversight or attribution [2]. Recent studies [3] discuss that the rapid adoption of AI tools in education presents a significant challenge in maintaining academic integrity, with concerns over the fabrication of information and the ethical use of AI-generated content, prompting the need for clearer guidelines and frameworks to support responsible usage. Previous studies have demonstrated the efficacy of AI tools in enhancing language skills and supporting English for Specific Purposes (ESP) courses. Our research extends this focus to the engineering field, where both technical and communication skills are critical. This research investigates the use of AI tools for educators and learners and how they affect the learning outcomes, their critical thinking, the student engagement, and how this affects the offering of personalized (or the lack thereof) learning experiences. By focusing on the perspectives of both students and faculty, the study seeks to understand how these tools impact educational outcomes and shape teaching methodologies. In particular, this research investigates the following question: How do students and faculty perceive and use AI chatbots in academic settings? A survey was used to gather data from both groups where descriptive and inferential statistics were performed to evaluate the broader implications of AI integration in educational contexts. The study was conducted in the context of the university's commitment to integrating AI tools into higher education, focusing on developing skills for effective and ethical AI use. In conclusion, this study provides valuable insights into both the benefits and limitations of AI-driven tools in engineering education. It highlights the opportunities these technologies present, while also addressing the need for much needed ethical considerations and digital literacy training for both students and educators. However, it's important to note that the survey method may not fully capture the complex impact of AI, and the focus on a single institution may limit the generalizability of the findings. Despite this, the findings contribute to ongoing discussions on how AI tools can be effectively integrated into higher education to foster innovation, enhance learning, and maintain academic standards.

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