Psychological Traits of Academic Leaders toward Artificial Intelligence Integration in Inclusive Education

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Vercelle A. Docdoc

Abstract

The integration of Artificial Intelligence (AI) has transformed educational leadership by introducing new opportunities and challenges in promoting inclusive education. As educational institutions increasingly adopt AI-enabled technologies, academic leaders are expected to demonstrate psychological traits that support ethical decision-making, innovation, collaboration, and learner-centered leadership. This study explored the psychological traits of academic leaders toward Artificial Intelligence integration in inclusive education. A qualitative interpretive descriptive research design was employed involving 14 purposively selected academic leaders from higher education institutions in the Philippines. Data were collected through semi-structured interviews and analyzed using reflexive thematic analysis. The findings generated six major themes: Adaptive Leadership, Empathetic Decision-Making, Ethical Responsibility, Collaborative Leadership, Continuous Learning, and Strategic Innovation. Participants emphasized that successful AI integration extends beyond technological adoption and depends on leaders who possess adaptability, empathy, ethical responsibility, and a commitment to continuous professional growth while fostering collaborative and inclusive educational environments. The findings further revealed that responsible AI implementation requires institutional strategies that balance technological innovation with equity, accessibility, and learner-centered educational practices. The study contributes to the growing literature on educational leadership by demonstrating that psychological traits serve as critical drivers of responsible Artificial Intelligence integration in inclusive education. The findings provide practical implications for leadership development, institutional AI governance, and policy initiatives that support the ethical and sustainable adoption of AI in higher education...

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How to Cite
A. Docdoc, V. (2026). Psychological Traits of Academic Leaders toward Artificial Intelligence Integration in Inclusive Education. CINEFORUM, 66(S2), 291–298. Retrieved from https://revistadecineforum.com/index.php/cf/article/view/986
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Original Articles

References

Avolio, B. J., Sosik, J. J., Kahai, S. S., & Baker, B. (2014). E-leadership: Re-examining transformations in leadership source and transmission. The Leadership Quarterly, 25(1), 105–131. https://doi.org/10.1016/j.leaqua.2013.11.003

Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. SAGE Publications.

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

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), 22. https://doi.org/10.1186/s41239-023-00392-8

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M., Al-Busaidi, K. A., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., ... Wright, R. (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. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1

Fullan, M., Quinn, J., Drummy, M., & Gardner, M. (2020). Education reimagined: The future of learning. Corwin.

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

Holmes, W., & Miao, F. (2023). Guidance for generative AI in education and research. UNESCO Publishing.

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeiffer, F., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50(6), 2824–2838. https://doi.org/10.1111/bjet.12861

OECD. (2023). OECD Digital Education Outlook 2023: Towards an effective digital education ecosystem. OECD Publishing. https://doi.org/10.1787/b14bef59-en

Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533–544. https://doi.org/10.1007/s10488-013-0528-y

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

Thorne, S. (2016). Interpretive description: Qualitative research for applied practice (2nd ed.). Routledge.

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), 39. https://doi.org/10.1186/s41239-019-0171-0

.