A Multidimensional Conceptual Framework of Generative AI As a Co-Instructor: Measurement Formulation for Personalized and Sustainable Higher Education
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Generative artificial intelligence (GenAI) is transforming higher education by evolving from a content automation tool into a collaborative instructional partner. However, its conceptualization as a co-instructor remains fragmented and lacks a validated measurement structure. This study develops a multidimensional conceptual framework and measurement formulation that positions GenAI as a structured pedagogical collaborator supporting personalization, sustainability, and ethical integrity. Using a theory-building approach grounded in interdisciplinary literature, the study defines key constructs, specifies relationships, and proposes indicators for future structural equation modeling. The framework identifies three instructional domains co-planning, co-instruction, and co-assessment mediated by pedagogical implementation effectiveness and moderated by ethical governance readiness. An eight-construct model and 37-item instrument are proposed for empirical validation. The study contributes conceptual clarity by advancing a systematic, ethically grounded approach to human–AI collaboration in higher education and recommends institutionally guided, teacher-in-the-loop implementation strategies.
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Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. https://doi.org/10.1080/0969595980050102
Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., ... & Wamba, S. F. (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, 102642.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Schafer, B. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707.
Franco, M., Pereira, R., & Silva, J. (2024). Responsible AI governance in higher education: Ethical frameworks and implementation guidelines. Computers & Education: Artificial Intelligence, 5, 100169.
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059
Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.
Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10(1–2), 18–26. https://doi.org/10.1007/s13162-020-00161-0
Jamie, K., Lee, S., & Park, J. (2024). Hybrid AI-human instructional models in computer science education: Exam performance and engagement outcomes. Education and Information Technologies, 29, 11245–11268.
Joon, H., Kim, T., & Alvarez, L. (2024). Fairness, accountability, and transparency in generative AI for education. AI & Society. Advance online publication.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
Kotsis, I. (2025). Constructivist alignment in AI-supported higher education environments. Educational Technology Research and Development.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
MacInnis, D. J. (2011). A framework for conceptual contributions in marketing. Journal of Marketing, 75(4), 136–154. https://doi.org/10.1509/jmkg.75.4.136
Magrill, D., Santos, R., & Ibrahim, N. (2024). Generative AI applications in curriculum mapping and feedback systems. British Journal of Educational Technology.
Neupane, B., Sharma, P., & Gautam, R. (2024). Personalized adaptive learning using large language models. Computers & Education: Artificial Intelligence, 4, 100130.
OECD. (2019). OECD principles on artificial intelligence. OECD Publishing. https://doi.org/10.1787/974a9f69-en
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Rose Luckin et al. (2016), Intelligence Unleashed: An Argument for AI in Education.
Shubhanshi, S. (2025). Teacher-in-the-Loop framework for responsible AI integration in higher education. Journal of Educational Technology & Society.
Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189. https://doi.org/10.3102/0034654307313795
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15.
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO Publishing.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Whetten, D. A. (1989). What constitutes a theoretical contribution? Academy of Management Review, 14(4), 490–495. https://doi.org/10.5465/amr.1989.4308371
Xiao, Y., Li, Z., & Chen, M. (2024). Human–AI co-grading and automated essay scoring using large language models. Computers & Education, 196, 104720.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(39). https://doi.org/10.1186/s41239-019-0171-0
Zhang, L. (2025). Retrieval-augmented generative AI for course-specific instructional support: Evidence from DS-ASST implementation. IEEE Transactions on Learning Technologies.

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