Portland, Oregon
June 23, 2024
June 23, 2024
June 26, 2024
Educational Research and Methods Division (ERM) Technical Session 7
Educational Research and Methods Division (ERM)
14
10.18260/1-2--47453
https://peer.asee.org/47453
91
Dr. Kittur is an Assistant Professor in the Gallogly College of Engineering at The University of Oklahoma. He completed his Ph.D. in Engineering Education Systems and Design program from Arizona State University, 2022. He received a bachelor’s degree in Electrical and Electronics Engineering and a Master’s in Power Systems from India in 2011 and 2014, respectively. He has worked with Tata Consultancy Services as an Assistant Systems Engineer from 2011–2012 in India. He has worked as an Assistant Professor (2014–2018) in the department of Electrical and Electronics Engineering, KLE Technological University, India. He is a certified IUCEE International Engineering Educator. He was awarded the ’Ing.Paed.IGIP’ title at ICTIEE, 2018. He is serving as an Associate Editor of the Journal of Engineering Education Transformations (JEET).
He is interested in conducting engineering education research, and his interests include student retention in online and in-person engineering courses/programs, data mining and learning analytics in engineering education, broadening student participation in engineering, faculty preparedness in cognitive, affective, and psychomotor domains of learning, and faculty experiences in teaching online courses. He has published papers at several engineering education research conferences and journals. Particularly, his work is published in the International Conference on Transformations in Engineering Education (ICTIEE), American Society for Engineering Education (ASEE), Computer Applications in Engineering Education (CAEE), International Journal of Engineering Education (IJEE), Journal of Engineering Education Transformations (JEET), and IEEE Transactions on Education. He is also serving as a reviewer for a number of conferences and journals focused on engineering education research.
The language model known as Chat Generative Pre-Trained Transformer (ChatGPT) was developed by Open Artificial Intelligence engineers. It's a kind of AI system that can produce text responses to a variety of questions and prompts that seem human. ChatGPT provides a number of benefits, such as round-the-clock assistance, prompt question answering, research-related information discovery, coding program writing, etc. Notwithstanding these benefits, ChatGPT's limited contextual knowledge of a given subject may result in inaccurate or irrelevant responses. Additionally, the feedback may be unfair or erroneous due to bias in the data used to train the program. Sadly, ChatGPT has the potential to pose security risks, which could result in data breaches and the leakage of private student information.
This research project aims at understanding the factors influencing engineering students’ perceptions on the use of ChatGPT. This topic is relevant, timely, and important as ChatGPT as created sufficient stir in education. By exploring factors influencing students’ experiences and perspectives, we aim to shed light on different aspects of the usage of ChatGPT and glean critical insights. This research study answers the following research question, ‘What factors influence the engineering students’ perceptions on the use of ChatGPT?’ A survey instrument was designed which included five dimensions: learning tool (10 items), trustworthiness (5 items), ethical considerations (5 items), ease of access (6 items), and concerns with ChatGPT (6 items). Additionally, the survey instrument also included demographic questions such as gender identity, race/ethnicity, current engineering academic department, and class standing. Four factors were identified by exploratory factor analysis (EFA): learning tool, trustworthiness, ease of access, and concerns with ChatGPT. Following the EFA, it was suggested that the dimension "ethical considerations" be eliminated. The range of Cronbach's alpha was 0.62 to 0.82, indicating a high degree of internal consistency reliability among the items. The statistical analysis reveals that males reported higher self-efficacy in using ChatGPT as a learning tool in comparison with other gender identities. Furthermore, Freshmen engineering students tend to have high perceptions on using ChatGPT as a learning tool, while junior engineering students have the lowest. Finally, freshmen engineering students tend to have high perceptions on ease of accessing ChatGPT, while sophomore engineering students have the lowest.
Sajawal, M. F., & Kittur, J. (2024, June), Factors Influencing Engineering Students’ Perceptions on the Use of ChatGPT Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--47453
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