Encyclopedia
This research presents a disruptive business model in higher education, centered on student-customized degree programs that meet the changing demands of today’s workforce. This model leverages emerging technologies such as AI (Artificial Intelligence), ChatGPT, and Gemini to create adaptable and accessible educational pathways, allowing students to personalize their learning based on individual goals. The COVID-19 pandemic, which accelerated the shift to online and hybrid learning models, highlights the need for flexible educational models like this one that prioritize student-centered approaches and operational efficiency. This study examines both the potential benefits and the challenges of implementing such a model.
- Higher education
- business model
- Iot
- personalised learning
- customised degree
The proposed disruptive business model addresses key challenges in higher education, such as flexibility, affordability, and the need for alignment with industry demands. Emerging technologies like AI, ChatGPT, and Gemini are integral to this model. AI automates administrative tasks, enhances personalized learning, and supports research, thereby improving overall educational efficiency [1][2]. ChatGPT contributes by assisting students with writing, answering questions, and facilitating online discussions [3]. Gemini, with its multi-modal capabilities, helps generate interactive content and tailors learning experiences to individual needs, fostering a more engaging and accessible educational environment [4][5].
The COVID-19 pandemic accelerated the adoption of virtual and blended learning, highlighting the necessity for new learning models. Smart Learning Environments (SLEs) emerged as a promising solution to improve student engagement and access to education, while offering personalized learning experiences [6]. However, despite the advantages, the adoption of these technologies faces resistance, especially due to concerns about the efficacy of online learning [7][8]. Stakeholder awareness and infrastructure, including reliable internet and technology, are crucial for the successful implementation of this model [9].
Future research should focus on the long-term impact of customized degree programs and their scalability, cybersecurity concerns, and how to create a specialized accreditation framework for personalized learning pathways [8][9][10][11][12][13][14]. This will ensure that the business model is sustainable and meets the evolving needs of students and the workforce.
References
- [2] Almarzooq, Z. I., Lopes, M., & Kochar, A. (2020). Virtual learning during the COVID-19 pandemic: A disruptive technology in graduate medical education. Journal of the American College of Cardiology, 75(20), 2635–2638. https://doi.org/10.1016/j.jacc.2020.04.015
- [3] Chen, X., Zou, D., Cheng, G., & Xie, H. (2021). Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education. Computers & Education, 167, 104120. https://doi.org/10.1016/j.compedu.2021.104120
- Bernard, R. M., Borokhovski, E., Schmid, R. F., Tamim, R. M., & Abrami, P. C. (2014). A meta-analysis of blended learning and technology use in higher education: From the general to the applied. Journal of Computing in Higher Education, 26(1), 87–122. https://doi.org/10.1007/s12528-013-9077-3
- Chen, X., Zou, D., Cheng, G., & Xie, H. (2021). Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education. Computers & Education, 167, 104120. https://doi.org/10.1016/j.compedu.2021.104120
- Rolfe, V. E. (2016). Open educational resources: Staff attitudes and awareness. Research in Learning Technology, 24, 29927. https://doi.org/10.3402/rlt.v24.29927
- van der Velde, J., Sense, F., Spijker, S., Meijer, P., & Wicherts, J. M. (2022). Hybrid learning environments in higher education: A systematic review. Educational Research Review, 36, 100448. https://doi.org/10.1016/j.edurev.2022.100448
- Zhang, K., & Fei, W. (2022). Artificial intelligence in higher education: Exploring the transformative impact and potential for innovative educational practices. Educational Technology Research and Development, 70, 1–16. https://doi.org/10.1007/s11423-022-10017-5
- Casilli, A. A., & Posada, J. (2019). The platformization of labor and society. In P. Graham (Ed.), Digital labor studies handbook.
- Wang, T., & Matsushita, K. (2019). Blended learning for quality higher education: Selected case studies on implementation from Asia-Pacific. UNESCO Bangkok Office.
- Rolfe, V. E. (2016). Open educational resources: Staff attitudes and awareness. Research in Learning Technology, 24, 29927. https://doi.org/10.3402/rlt.v24.29927
- van der Velde, J., Sense, F., Spijker, S., Meijer, P., & Wicherts, J. M. (2022). Hybrid learning environments in higher education: A systematic review. Educational Research Review, 36, 100448. https://doi.org/10.1016/j.edurev.2022.100448
- Zhang, K., & Fei, W. (2022). Artificial intelligence in higher education: Exploring the transformative impact and potential for innovative educational practices. Educational Technology Research and Development, 70, 1–16. https://doi.org/10.1007/s11423-022-10017-5
- Casilli, A. A., & Posada, J. (2019). The platformization of labor and society. In P. Graham (Ed.), Digital labor studies handbook.
- Wang, T., & Matsushita, K. (2019). Blended learning for quality higher education: Selected case studies on implementation from Asia-Pacific. UNESCO Bangkok Office.
