Integrating AI in Academia: A SEM Evaluation of Research Scholars' Usage Intentions on ChatGPT
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| Title: | Integrating AI in Academia: A SEM Evaluation of Research Scholars' Usage Intentions on ChatGPT |
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| Language: | English |
| Authors: | Sonal Sharma (ORCID |
| Source: | Higher Learning Research Communications. 2025 15(2). |
| Availability: | Walden University, LLC. 100 Washington Avenue South Suite 900, Minneapolis, MN 55401. Tel: 800-925-3368; Fax: 612-338-5092; e-mail: HLRCeditor@mail.waldenu.edu; Web site: https://scholarworks.waldenu.edu/hlrc/ |
| Peer Reviewed: | Y |
| Page Count: | 30 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Technology Uses in Education, Technology Integration, Educational Technology, Intention, Higher Education, Educational Research, Educational Researchers, Scholarship, Foreign Countries, Predictor Variables, Expectation, Social Influences, Ethics, Writing Research, Research Methodology, Research Needs, Educational Quality, Motivation |
| Geographic Terms: | India |
| ISSN: | 2157-6254 |
| Abstract: | Objectives: In this study, we investigate the intentions of research scholars to use ChatGPT in their academic research endeavors by employing an extended Unified Theory of Acceptance and Use of Technology model, the UTAUT2, which examines factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, habit, ethical concerns, and research excellence. By incorporating "research excellence" into the model, we aim to provide insights into artificial intelligence (AI) adoption in academic research settings and contribute to the broader discourse on the future of academic practices. Method: We conducted a survey among 400 research scholars in Indian higher education institutions to collect data. We then analyzed the data using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results: The analysis revealed that performance expectancy, facilitating conditions, hedonic motivation, habit, and research excellence significantly influence the intention to use ChatGPT, with research excellence emerging as the strongest predictor. Effort expectancy, social influence, and ethical concerns, however, did not significantly impact adoption intentions. Conclusions: The findings indicate that several key factors, particularly research excellence, play a critical role in influencing research scholars' intentions to integrate ChatGPT into their academic activities. The study demonstrates the importance of these factors in fostering AI adoption within the context of academic research. Implications for Practice: The results of this study offer valuable insights for researchers, administrators, and policymakers who aim to create environments conducive to effective AI utilization in research academia. By understanding the factors that influence AI adoption, stakeholders can better support research scholars in integrating ChatGPT into their work, enhancing academic practices, and advancing the use of AI in research. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1483989 |
| Database: | ERIC |
| Abstract: | Objectives: In this study, we investigate the intentions of research scholars to use ChatGPT in their academic research endeavors by employing an extended Unified Theory of Acceptance and Use of Technology model, the UTAUT2, which examines factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, habit, ethical concerns, and research excellence. By incorporating "research excellence" into the model, we aim to provide insights into artificial intelligence (AI) adoption in academic research settings and contribute to the broader discourse on the future of academic practices. Method: We conducted a survey among 400 research scholars in Indian higher education institutions to collect data. We then analyzed the data using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results: The analysis revealed that performance expectancy, facilitating conditions, hedonic motivation, habit, and research excellence significantly influence the intention to use ChatGPT, with research excellence emerging as the strongest predictor. Effort expectancy, social influence, and ethical concerns, however, did not significantly impact adoption intentions. Conclusions: The findings indicate that several key factors, particularly research excellence, play a critical role in influencing research scholars' intentions to integrate ChatGPT into their academic activities. The study demonstrates the importance of these factors in fostering AI adoption within the context of academic research. Implications for Practice: The results of this study offer valuable insights for researchers, administrators, and policymakers who aim to create environments conducive to effective AI utilization in research academia. By understanding the factors that influence AI adoption, stakeholders can better support research scholars in integrating ChatGPT into their work, enhancing academic practices, and advancing the use of AI in research. |
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| ISSN: | 2157-6254 |
