Bridging the Gap: How Students Perceive AI Tools in Management Education
Keywords:
Artificial Intelligence (AI), ChatGPT, Management Education, Students Perception, Ethical AI Usage, Personalized Learning, Technology IntegrationAbstract
The use of AI tools particularly ChatGPT, has significantly altered management education by enhancing accessibility, customizing instruction, and raising student interest. This study shows how management students view and using AI tools, emphasizing their benefits, drawbacks, and implications for academic performance. Even while ChatGPT and other AI technologies facilitate self directed learning, goal setting and problem solving, concerns about academic integrity, technological dependence, data privacy, and fragmented learning experiences still exist. The study examines usage patterns, satisfaction levels, ethical concerns, and how AI competency affects boosting benefits by analyzing inputs from students. The findings emphasize how crucial it is to integrate AI tools in education in a balanced way to address ethical concerns and foster critical thinking and all encompassing learning outcomes. In order to prepare students for professions in a technologically advanced world, recommendations are made to educators and legislators for creating curricula that successfully use AI tools.
References
Bozkurt, A., Jung, I., Xiao, J., Vladimirschi, V., Schuwer, R., Egorov, G., Lambert, S., Al-Freih, M., Pete, J., Olcott Jr, D. and Rodes, V., 2020. A global outlook to the interruption of education due to COVID-19 pandemic: Navigating in a time of uncertainty and crisis. Asian journal of distanceeducation, 15(1),pp.1-126. https://www.asianjde.com/ojs/index.php/AsianJDE/article/view/462
Muthukrishnan, R. and Datta, R., 2023. Indigenous practice and community-led climate change solutions: The relevance of traditional cosmic knowledge systems. Taylor & Francis. DOI: 10.4324/9781003389064
Mayembe, E. and Nsabata, S., 2020. Print-based learning media. Journal Educational Verkenning, 1(1), pp.1-7. https://doi.org/10.48173/jev.v1i1.23
Nelson, R., 2024. Academic Identity in the Age of AI: Higher Education and the Digital Revolution. Emerald Group Publishing.
Dahake, P.S., Mohare, R.V. and Dahake, N.S., 2024. Enhancing Management Education Through ChatGPT: A Novel Method for Ease and Efficacy. In Entrepreneurship and Creativity in the Metaverse (pp. 161-178). IGI Global.
Milner, H.R., 2021. Start where you are, but don't stay there: Understanding diversity, opportunity gaps, and teaching in today's classroom
Vashista, N., Gugnani, P., Bala, M. and Kumar, A., 2023. The Educator's Lens: Understanding the Impact of AI on Management Education. International Journal of Education and Development using Information and Communication Technology, 19(3), pp.9-27.
Alqahtani, T., Badreldin, H.A., Alrashed, M., Alshaya, A.I., Alghamdi, S.S., Bin Saleh, K., Alowais, S.A., Alshaya, O.A., Rahman, I., Al Yami, M.S. and Albekairy, A.M., 2023. The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in social and administrative pharmacy, 19(8), pp.1236-1242. https://doi.org/10.1016/j.sapharm.2023.05.016
Ma, Y., Hu, S., Li, X., Wang, Y., Liu, S. and Cheong, K.H., 2024. Students rather than experts: A new ai for education pipeline to model more human-like and personalised early adolescences. arXiv preprint arXiv:2410.15701. https://doi.org/10.48550/arXiv.2410.15701
Roumeliotis, K.I. and Tselikas, N.D., 2023. Chatgpt and open-ai models: A preliminary review. Future Internet, 15(6), p.192. https://doi.org/10.3390/fi15060192
Atlas, S., 2023. ChatGPT for higher education and professional development: A guide to conversational AI. https://digitalcommons.uri.edu/cba_facpubs/548
Yu, H., 2024. The application and challenges of ChatGPT in educational transformation: New demands for teachers' roles. Heliyon, 10(2). https://doi.org/10.1016/j.heliyon.2024.e24289
Wanner, T. and Palmer, E., 2015. Personalising learning: Exploring student and teacher perceptions about flexible learning and assessment in a flipped university course. Computers & Education, 88, pp.354-369. https://doi.org/10.1016/j.compedu.2015.07.008
Mohammadkarimi, E., 2023. Teachers’ reflections on academic dishonesty in EFL students’ writings in the era of artificial intelligence. Journal of Applied Learning and Teaching, 6(2), pp.105-113. DOI: https://doi.org/10.37074/jalt.2023.6.2.10
Xiao, Y., Zhang, T. and He, J., 2024. A review of promises and challenges of AI-based chatbots in language education through the lens of learner emotions. Heliyon. https://doi.org/10.1016/j.heliyon.2024.e37238
Masalaci, Z.S., 2024. Exploring the Impact of Large Language Models on Conceptual Learning in Higher Education: An Analysis of AI-Driven C https://hdl.handle.net/11250/3178094
Kitsantas, A., Baylor, A.L. and Hiller, S.E., 2019. Intelligent technologies to optimize performance: Augmenting cognitive capacity and supporting self-regulation of critical thinking skills in decision-making. Cognitive Systems Research, 58, pp.387-397. https://doi.org/10.1016/j.cogsys.2019.09.003
Ng, D.T.K., Tan, C.W. and Leung, J.K.L., 2024. Empowering student self‐regulated learning and science education through ChatGPT: A pioneering pilot study. British Journal of Educational Technology, 55(4), pp.1328-1353. https://doi.org/10.1111/bjet.13454
Çakiroglu, Ü. and Öztürk, M., 2021. Cultivating Self-Regulated Learning in Flipped EFL Courses: A Model for Course Design. European Journal of Open, Distance and E-Learning, 23(2), pp.20-36. DOI: 10.2478/eurodl-2020-0008
Van den Beemt, A., MacLeod, M., Van der Veen, J., Van de Ven, A., Van Baalen, S., Klaassen, R. and Boon, M., 2020. Interdisciplinary engineering education: A review of vision, teaching, and support. Journal of engineering education, 109(3), pp.508-555. https://doi.org/10.1002/jee.20347
Adiguzel, T., Kaya, M.H. and Cansu, F.K., 2023. Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3). https://doi.org/10.30935/cedtech/13152
Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W.J., Sun, M., Day, I., Rather, R.A. and Heathcote, L., 2023. The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6(1), pp.41-56. https://doi.org/10.37074/jalt.2023.6.1.29
Jung, E., Kim, D., Yoon, M., Park, S. and Oakley, B., 2019. The influence of instructional design on learner control, sense of achievement, and perceived effectiveness in a supersize MOOC course. Computers & Education, 128, pp.377-388. https://doi.org/10.1016/j.compedu.2018.10.001
Alam, A. and Mohanty, A., 2023. Educational technology: Exploring the convergence of technology and pedagogy through mobility, interactivity, AI, and learning tools. Cogent Engineering, 10(2), p.2283282. https://doi.org/10.1080/23311916.2023.2283282
Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D. and Pietrantoni, L., 2023. The impact of artificial intelligence on workers’ skills: Upskilling and reskilling in organisations. Informing Science, 26, pp.39-68. https://dx.doi.org/10.28945/5078
Tomaskinova, J. and Tomaskin, J., 2024. UNLOCKING THE FUTURE OF EDUCATION: EMPOWERING EDUCATORS WITH AI BY OVERCOMING PROFESSIONAL DEVELOPMENT CHALLENGES. In ICERI2024 Proceedings (pp. 10633-10642). IATED. https://doi.org/10.21125/iceri.2024.2759
Weizenbaum, J. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45.
Colby, K. M. (1981). PARRYing. Behavioral and Brain Sciences, 4(4), 550–560. doi:10.1017/S0140525X00000224
Wallace, R. (1995). Artificial linguistic internet computer entity (alice). City.
Hoffer, R., Kay, T., Levitan, P., & Klein, S. (2001). Smarterchild. ActiveBuddy.
Aron, J. (2011). How innovative is Apple’s new voice assistant. Siri, NewScientist, 212(2836), 24
Lally, A., & Fodor, P. (2011). Natural language processing with prolog in the ibm watson system. The Association for Logic Programming (ALP) Newsletter, 9, 2011.
Holotescu, C. (2016). MOOCBuddy: A Chatbot for personalized learning with MOOCs. RoCHI, 91–94.
Dinh, T. N., & Thai, M. T. (2018). AI and blockchain: A disruptive integration. Computer, 51(9), 48–53. https://doi.org/10.1109/MC.2018.3620971
Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), 263–267. https://doi.org/10.2501/JAR-2018-035
AlZubi, S., Mughaid, A., Quiam, F., & Hendawi, S. (2022). Exploring the Capabilities and Limitations of ChatGPT and Alternative Big Language Models. Artificial Intelligence and Applications. https://doi.org/10.47852/bonviewAIA3202820
Rahaman, M. S., Ahsan, M. M., Anjum, N., Rahman, M. M., & Rahman, M. N. (2023). The AI race is on! Google’s Bard and OpenAI’s ChatGPT head to head: An opinion article. Mizanur and Rahman, Md Nafizur, The AI Race Is On. https://dx.doi.org/10.2139/ssrn.4351785
Rudolph, J., Tan, S., & Tan, S. (2023). War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education. Journal of Applied Learning and Teaching, 6(1). https://doi.org/10.37074/jalt.2023.6.1.23
Downloads
Published
Issue
Section
License
Copyright (c) 2025 R. Keerthana , Dr. N. Manikandan (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
