Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
ENGINEERING FACULTY PERCEPTIONS OF CHATGPT:OPPORTUNITIES, CHALLENGES, AND ETHICAL CONSIDERATIONS IN EDUCATION
0
Zitationen
4
Autoren
2025
Jahr
Abstract
This study investigates engineering faculty members’ perceptions of the ethical integration of ChatGPT, a generative AI tool, in teaching practices at the University of Engineering and Technology (UET) in Balochistan, Pakistan. As AI technologies continue to shape the educational landscape, the ethical considerations surrounding their use, especially in technical fields like engineering, have become a significant focus. Using a quantitative, cross-sectional survey design, this study gathered data from 40 engineering faculty members across six departments to assess their awareness, perceived benefits, challenges, and training needs related to ChatGPT. The findings indicate high levels of awareness (85%) and usage (85%) of ChatGPT among faculty, with 75% reporting that it enhances student engagement and simplifies complex engineering concepts. Despite these positive perceptions, concerns regarding reliability, accuracy, and the potential for ChatGPT to undermine critical thinking and academic integrity were expressed by 57.5% of the respondents. These results highlight the dual nature of ChatGPT's role in education—offering significant educational benefits while presenting ethical challenges. The study underscores the need for comprehensive faculty training, clear institutional policies, and ethical guidelines to ensure responsible integration of AI tools in higher education. This research contributes to the growing discourse on AI in education, particularly in the context of engineering, by providing insights into the opportunities, risks, and strategies for promoting ethical AI use.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.436 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.311 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.753 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.523 Zit.