Strategic Stakeholder Integration in the AI Era: A Bibliometric Mapping of the Shift from Intelligent Tutoring to Generative Educational Systems

Ramzi Talmoudi; Jamel Choukir

doi:10.48161/qaj.v6n2a2550

Authors

Ramzi Talmoudi Department of Business Administration, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Kingdom of Saudi Arabia. https://orcid.org/0000-0003-3908-8567
Jamel Choukir Department of Business Administration, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Kingdom of Saudi Arabia. https://orcid.org/0000-0003-4247-5760

DOI:

https://doi.org/10.48161/qaj.v6n2a2550

Keywords:

Generative AI disruption, Intelligent tutoring systems, Strategic stakeholder integration, Technology acceptance model, Adaptive educational ecosystems.

Abstract

Higher education faces social and technological challenges driven by the democratization of the generative artificial intelligence (GenAI). The generative shockwave has marked a major shift in traditional learning from centralized and controlled Intelligent Tutoring Systems (ITS) to decentralized and unmanaged Shadow IT. To rigorously map higher education institution transition during the generative shockwave, the current study used a pre-defined protocol for an exploratory PRISMA-based bibliometric analysis of 459 academic documents retrieved from the Web of Science Core Collection. Its main findings highlight the magnitude of this shift, as 82% of the considered articles were published in 2024 and 2025, confirming the generative AI shockwave. Thematic mapping shows that while the key problems with artificial intelligence in education are universally recognized, higher education institutions lack adequately adapted policies to manage them. Furthermore, important psychological factors that influence technology acceptance, such as students' self-efficacy and mental strain, require special academic interest. By considering the dual theoretical lenses, using a scalar analysis approach, of the micro-level Technology Acceptance Model (TAM) and the macro-level Institutional Theory, the current study highlights the relationship between AI-based learning evolution and the inefficient management of AI risks through top-down IT systems. Instead, a collaborative approach that integrates stakeholders must be built. By working with localized institutional entrepreneurs and providing appropriate socio-technical support, universities can bridge gaps, meet the expectations of modern students, sustain their legitimacy, and adapt to the inevitable challenges posed by artificial intelligence, especially in an adaptive personalized learning ecosystem.

Downloads

Download data is not yet available.

References

Perez-Encinas, A., Martinez-Vazquez, A., Souto-Otero, M., & Kéri, A. (2025). Can student satisfaction rise as education quality declines? A comparative analysis of higher education students’ satisfaction in times of disruption. Journal of Marketing for Higher Education.

Panagiotakis, S., et al. (2022). Remote Arduino labs for teaching microcontrollers and Internet of Things programming. Proceedings of the 2022 31st Annual Conference of the European Association for Education in Electrical and Information Engineering (EAEEIE 2022).

Altalhi, A., Bonke, C., & Alblowi, K. (2025). Adaptive vs. Traditional Learning: Long-Term Knowledge Retention—A Literature Review. Qubahan Academic Journal, 5(4), 322–331.

Revesai, Z. (2025). Generative AI dependency: The emerging academic crisis and its impact on student performance—A case study of a university in Zimbabwe. Cogent Education, 12(1).

Huisman, J., & van der Wende, M. (2022). A research agenda for global higher education. Edward Elgar Publishing.

Tleuov, A. (2024). Understanding shifts in higher education professional development: Evidence from Kazakhstani universities’ pandemic experience. Journal of Higher Education Policy and Leadership Studies, 5(4), 68–80.

van Beusekom-Thoolen, P., Holmes, P., Jansen, W., Vos, B., & de Boer, A. (2024). Interdisciplinary challenges associated with rapid response in the food supply chain. Supply Chain Management, 29(3), 444–459.

Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1), 38.

Crawford, J., Cowling, M., & Allen, K. A. (2023). Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching and Learning Practice, 20(3).

Bearman, M., & Ajjawi, R. (2023). Learning to work with the black box: Pedagogy for a world with artificial intelligence. British Journal of Educational Technology, 54(5), 1160–1173.

Ali, M., Ma, M., Muneeb, M., & Wong, G. K. W. (2025). Mapping contemporary AI-education intersections and developing an integrated convergence framework: A bibliometric-driven and inductive content analysis. Metrics, 2(4), 23.

Tsai, S. C., Chen, C. H., Shiao, Y. T., Ciou, J. S., & Wu, T. N. (2020). Precision education with statistical learning and deep learning: A case study in Taiwan. International Journal of Educational Technology in Higher Education, 17(1), 12.

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).

Lee, K. (2018). Everyone already has their community beyond the screen: Reconceptualizing online learning and expanding boundaries. Educational Technology Research and Development, 66(5), 1255–1268.

Papadokostaki, K., Panagiotakis, S., Vassilakis, K., & Malamos, A. (2018). Implementing an adaptive learning system with the use of experience API. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (LNICST), 229, 393–402.

Peled, Y., Eshet, Y., Barczyk, C., & Grinautski, K. (2019). Predictors of academic dishonesty among undergraduate students in online and face-to-face courses. Computers & Education, 131, 49–59.

Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429.

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 1–22.

Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460–474.

Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410.

Nguyen, T. N. T., Lai, N. V., & Nguyen, Q. T. (2024). Artificial intelligence (AI) in education: A case study on ChatGPT’s influence on student learning behaviors. Educational Process: International Journal, 13(2), 105–121.

Ilieva, G., et al. (2026). Navigating opportunities and challenges of generative AI in higher education: Towards responsible, equitable, and human-centered integration. Frontiers in Artificial Intelligence.

Qu, Y., & Wang, J. (2025). The impact of AI guilt on students’ use of ChatGPT for academic tasks: Examining disciplinary differences. Journal of Academic Ethics, 23(4), 2087–2110.

Chen, X., Xie, H., Qin, S. J., Wang, F. L., & Hou, Y. (2025). Artificial intelligence-supported student engagement research: Text mining and systematic analysis. European Journal of Education, 60(1), e70008.

Khosravi, H., et al. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100074.

Pan, Y., & Nehm, R. H. (2025). Large language model and traditional machine learning scoring of evolutionary explanations: Benefits and drawbacks. Education Sciences, 15(6), 676.

Mundlamuri, R., Gunnam, G. R., Mysari, N. K., & Pujuri, J. (2025). The evolution of AI: From classical machine learning to modern large language models. IEEE Access, 13, 178302–178341.

Shahzad, M. F., Xu, S., An, X., & Asif, M. (2025). Are generative AI technologies transforming education for the 21st century? Research trends, challenges, and benefits. SAGE Open, 15(3).

Wang, X., Xu, X., Zhang, Y., Hao, S., & Jie, W. (2024). Exploring the impact of artificial intelligence application in personalized learning environments: Thematic analysis of undergraduates’ perceptions in China. Humanities and Social Sciences Communications, 11(1), 1644.

Xu, X. S., Wang, X. B., Zhang, Y. F., & Zheng, R. (2024). Applying ChatGPT to tackle the side effects of personal learning environments from learner and learning perspective: An interview of experts in higher education. PLoS ONE, 19(1).

Diaconescu, R. (2025). Institution-independent model theory (2nd ed.). Springer.

Langseth, I., Jacobsen, D. Y., & Haugsbakken, H. (2022). The role of support units in digital transformation: How institutional entrepreneurs build capacity for online learning in higher education. Technology, Knowledge and Learning, 28(4), 1745–1782.

Naseer, F., Khan, M. N., Addas, A., Awais, Q., & Ayub, N. (2025). Game mechanics and artificial intelligence personalization: A framework for adaptive learning systems. Education Sciences, 15(3), 301.

Zhao, Z., An, Q., & Liu, J. (2025). Exploring AI tool adoption in higher education: Evidence from a PLS-SEM model integrating multimodal literacy, self-efficacy, and university support. Frontiers in Psychology, 16, 1619391.

Klimova, B., & Pikhart, M. (2025). Exploring the effects of artificial intelligence on student and academic well-being in higher education: A mini-review. Frontiers in Psychology, 16, 1498132.

Pikhart, M., & Al-Obaydi, L. H. (2025). Reporting the potential risk of using AI in higher education: Subjective perspectives of educators. Computers in Human Behavior Reports, 18, 100693.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. The Center for Curriculum Redesign.

Li, R., Ouyang, J., Lin, J., & Ouyang, S. (2025). Mediating effect of AI attitudes and AI literacy on the relationship between career self-efficacy and job-seeking anxiety. BMC Psychology, 13(1), 1–17.

Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599.

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education (1st ed.). Polity Press.

Mollick, E. R., & Mollick, L. (2023). Assigning AI: Seven approaches for students, with prompts. SSRN Electronic Journal.

Waters-Lynch, J., Allen, D. W. E., Potts, J., & Berg, C. (2025). Shadow user innovation: Governing covert generative-AI use for dynamic-capability renewal. Innovation.

Meyer, J., & Rowan, B. (2011). Institutionalized organizations: Formal structure as myth and ceremony (I. Chirikov, Trans.). Journal of Economic Sociology, 12(1), 43–67.

MacKenzie, A., et al. (2021). Dissolving the dichotomies between online and campus-based teaching: A collective response to The Manifesto for Teaching Online (Bayne et al., 2020). Postdigital Science and Education, 4(2), 271–329.

Bozkurt, A., et al. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53.

Osamor, A., Ifelebuegu, P., Kulume, P., & Cherukut, P. (2023). Chatbots and AI in education (AIEd) tools: The good, the bad, and the ugly. Journal of Applied Learning and Teaching, 6(2), 332–345.

Al-Ahmadi, H. H. (2025). A proposed model for policies and procedural tasks to develop curriculum in general education stages based on Beauchamp’s model of curriculum engineering. Qubahan Academic Journal, 5(3), 436–461.

Dwivedi, Y. K., et al. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71.

Sergeeva, O. V., Zheltukhina, M. R., Shoustikova, T., Tukhvatullina, L. R., Dobrokhotov, D. A., & Kondrashev, S. V. (2025). Understanding higher education students’ adoption of generative AI technologies: An empirical investigation using UTAUT2. Contemporary Educational Technology, 17(2), ep571.

Abdullah, Z., & Zaid, N. M. (2023). Perception of generative artificial intelligence in higher education research. Innovative Teaching and Learning Journal, 7(2), 84–95.

Oc, Y., Gonsalves, C., & Quamina, L. T. (2025). Generative AI in higher education assessments: Examining risk and tech-savviness on student adoption. Journal of Marketing Education, 47(2), 138–155.

Dizon, G., Gold, J., & Barnes, R. (2025). Technostress and English language learning in the age of generative AI. The EuroCALL Review, 32(2), 88–101.

Ud Din, M., Khan, M. F., Talmoudi, R., Tanveer, M., & Ahmad, A. (2024). Spiritual leadership: A beacon of stress relief for university educators—Trust, commitment, and whole life management in focus. Pakistan Journal of Life and Social Sciences, 22(2), 20652–20668.

Lawrence, S. (2026). Artificial intelligence in higher education: Stakeholder perceptions and policy implications. Journal of Applied Research in Higher Education, 1–18.

Tanveer, M. (2026). Artificial intelligence literacy moderating the effects of artificial intelligence use on cognitive outcomes including memory retention, decision making, and cognitive laziness. Discover Psychology.

Bond, M., et al. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), 1–41.

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239.

Fu, Y., Lin, Y., Wang, Y., Tran, S., & Hiniker, A. (2026). “Everyone’s using it, but no one is allowed to talk about it”: College students’ experiences navigating the higher education environment in a generative AI world. arXiv.

Peterson, S. (2025). Addressing student use of generative AI in schools and universities through academic integrity reporting. Frontiers in Education, 10.

Dabis, A., & Csáki, C. (2024). AI and ethics: Investigating the first policy responses of higher education institutions to the challenge of generative AI. Humanities and Social Sciences Communications, 11(1), 1006.

Zhang, A., Walker, O., Nguyen, K., Dai, J., Chen, A., & Lee, M. K. (2023). Deliberating with AI: Improving decision-making for the future through participatory AI design and stakeholder deliberation. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW1).

Tadimalla, S. Y., & Maher, M. L. (2024). AI literacy for all: Adjustable interdisciplinary socio-technical curriculum. 2024 IEEE Frontiers in Education Conference (FIE), 1–9.

Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.

Sefcik, L., Striepe, M., & Yorke, J. (2020). Mapping the landscape of academic integrity education programs: What approaches are effective? Assessment & Evaluation in Higher Education, 45(1), 30–43.

Wang, C., Wang, H., Li, Y., Dai, J., Gu, X., & Yu, T. (2025). Factors influencing university students’ behavioral intention to use generative artificial intelligence: Integrating the theory of planned behavior and AI literacy. International Journal of Human–Computer Interaction, 41(11), 6649–6671.

Banihashem, S. K., Bond, M., Bergdahl, N., Khosravi, H., & Noroozi, O. (2025). A systematic mapping review at the intersection of artificial intelligence and self-regulated learning. International Journal of Educational Technology in Higher Education, 22(1), 50.

Omarsaib, M., Mitha, S. B., Vahed, A., & Mohamed, G. M. (2025). Mapping the AI surge in higher education: A bibliometric study spanning a decade (2015–2025). Informatics, 12(4), 137.