ARTIFICIAL INTELLIGENCE IN BUSINESS EDUCATION: A THREAT TO HUMAN INSTRUCTORS OR AN OPPORTUNITY FOR ENHANCED LEARNING EXPERIENCE.

Babafemi Timothy Tolulope, Emmanuel Tunde Ilori

Abstract


 

 

The integration of Artificial Intelligence (AI) in business education is rapidly transforming traditional learning environments, raising critical questions about its implications for human instructors. This paper explores whether AI poses a threat to the role of educators or offers an opportunity for enhancing business education. While some critics argue that AI could replace human teachers and diminish the personal touch in teaching, others view it as a valuable tool to enrich the learning experience. Through an analysis of AI applications in business education, including personalized learning, automated grading, and predictive analytics, this study presents a balanced perspective. It highlights AI’s potential to support business instructors by handling administrative tasks, providing personalized learning experiences, and offering data-driven insights to improve teaching effectiveness. However, concerns about job displacement, loss of human interaction, and ethical implications such as bias and data privacy remain significant. As a way forward, the paper emphasizes the need for a collaborative approach, where AI complements rather than replaces human educators. In this context, instructors can focus on fostering critical thinking, creativity, and mentorship- skills that AI cannot replicate. The conclusion argues that AI, when integrated thoughtfully and ethically, represents an opportunity for business education to evolve and meet the needs of modern learners. Recommendations are provided for educators and institutions to embrace AI while maintaining the vital human elements of teaching and learning. 

Keywords: Artificial Intelligence, Human instructors, Business Education, Enhanced Learning, Teaching and Learning, Enhanced Learning.


Full Text:

PDF

References


Floridi, L., et al. (2018). AI4People—An Ethical Framework for a Good AI Society. Minds and Machines, 28(4), 689 - 707.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

Luckin, R., et al. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson Education.

Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. Polity Press.

Zawacki-Richter, O., et al. (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(39).

Dede, C., Richards, J., Saxberg, B., & Shapiro, H. J. (2019). Learning engineering for online education: Theoretical contexts and design-based examples. Routledge.

Goel, A., & Polepeddi, L. (2016). Jill Watson: A virtual teaching assistant for online education. Georgia Institute of Technology.

Huang, R. H., Liu, D. J., Tlili, A., Yang, J. F., & Wang, H. H. (2020). Handbook on facilitating flexible learning during educational disruption: The Chinese experience in maintaining undisrupted learning in COVID-19 outbreak. Beijing: Smart Learning Institute of Beijing Normal University.

Jordan, S., & Mitchell, T. (2020). Automated feedback and assessment: Developments and challenges. Assessment & Evaluation in Higher Education, 45(1), 1–11. https://doi.org/10.1080/02602938.2019.1624766.

Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 1–13. https://doi.org/10.1186/s41039-017-0062-8.

Baker, R. S. (2020). Educational data mining and learning analytics: Applications to education and business. Springer.

McMahan, D., & Cech, T. (2019). The human element in AI-assisted education: Why teachers matter. Journal of Educational Technology, 17(2), 44–58.

Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–12. https://doi.org/10.1145/3173574.3174104.

Zhou, Z. (2020). Data privacy concerns and security in artificial intelligence applications in education. Educational Technology Research and Development, 68(3), 1919–1938. https://doi.org/10.1007/s11423-020-09731-2.


Refbacks

  • There are currently no refbacks.


Copyright © 2022-2025. Department of Business Education, Kwara State University, Malete, Nigeria. All Rights Reserved.
ISSN: 2408-5367

Powered by Myrasoft Systems Ltd.(http://www.myrasoft.com.ng)