AI-DRIVEN RESEARCH ENHANCEMENT IN COMMERCE EDUCATION: TOOLS, TRENDS, AND ETHICAL DIMENSIONS IN HIGHER ACADEMIA
DOI:
https://doi.org/10.62737/nh4n7716Keywords:
Artificial Intelligence, Commerce Education, Research Productivity, AI Tools, Ethical Challenges, Data Analysis, Higher Education, Academic Integrity, Faculty Perception, Technology AdoptionAbstract
The advent of Artificial Intelligence (AI) has significantly reshaped the research dynamics in higher education, with a notable impact on commerce education. This paper investigates how AI tools are leveraged to improve research efficiency among academicians and students by streamlining tasks such as literature synthesis, data interpretation, manuscript preparation, and scholarly collaboration (Zawacki-Richter et al., 2019; Chen et al., 2020). Employing a mixed-methods approach that includes secondary literature and synthesized dummy primary data, the study analyzes the current scope of AI usage, highlights frequently employed platforms, and outlines the advantages and challenges associated with their implementation. Ethical dilemmas, skill deficits, and technological inequality are explored as primary barriers to adoption (Holmes et al., 2019). The research offers practical recommendations for the responsible and ethical application of AI in academic settings and outlines future areas for investigation. Overall, the study affirms AI's transformative potential in advancing research practices while calling for inclusive policies and robust ethical frameworks to support sustainable use in commerce education.
References
[1] Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149–159. https://doi.org/10.1145/3287560.3287583
[2] Broussard, M. (2018). Artificial unintelligence: How computers misunderstand the world. MIT Press.
[3] Floridi, L., et al. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
[4] Gurumurthy, A., & Chami, N. (2019). Data governance, AI and the need for regulatory frameworks. IT for Change. https://itforchange.net/research
[5] Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://curriculumredesign.org/wp-content/uploads/AI-in-Education-Promises-and-Implications-for-Teaching-and-Learning-Holmes-Bialik-Fadel.pdf
[6] Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
[7] Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education. https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/open-ideas/Intelligence-Unleashed-Publication.pdf
[8] Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0
[9] Dwivedi, Y. K., Hughes, D. L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
[10] Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/s11023-020-09548-1
[11] Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., ... & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), 233. https://doi.org/10.1038/s41467-019-14108-y
[12] Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
[13] Wang, X., Xu, H., & Wang, H. (2023). Artificial Intelligence–enabled scientific collaboration and research impact: Evidence from machine learning and social science. Journal of Informetrics, 17(1), 101358. https://doi.org/10.1016/j.joi.2022.101358
[14] HolonIQ. (2023). The state of AI in global business schools: A report on AI adoption in business education. HolonIQ. https://www.holoniq.com
[15] Gulati, R. (2024). A study on the faculty perception regarding the use of artificial intelligence in commerce education. International Journal of Advance Research and Innovation, 12(2), 7–12. https://doi.org/10.69996/ijari.2024007
[16] EDUCAUSE. (2025). AI procurement in higher education: Benefits and risks of emerging tools. https://er.educause.edu/articles/2025/3/ai-procurement-in-higher-education-benefits-and-risks-of-emerging-tools
