Transforming Higher Education: The Collaborative Potential of AI for Students and Faculty

Authors

DOI:

https://doi.org/10.46328/ijtes.7685

Keywords:

Artificial Intelligence in Education, Student-Faculty Collaboration, Higher Education Technology, AI-Powered Learning Tools, Personalized Learning

Abstract

Artificial Intelligence (AI) is rapidly transforming higher education by enhancing teaching, learning, and academic collaboration. This study examines the collaborative potential of AI in fostering effective interactions between students and faculty. Using a quantitative research design, data were collected from 100 purposively selected participants, including IT students and faculty members, through a structured online survey. The findings reveal a high level of AI adoption, with 82.5% of respondents utilizing AI-powered tools such as ChatGPT, content generation platforms, and collaboration systems. Results indicate that AI significantly improves communication, enables faster feedback, and supports personalized learning experiences, contributing to enhanced academic engagement. However, challenges such as limited training, privacy concerns, resistance to adoption, and accessibility issues remain significant barriers. Thematic analysis further highlights AI’s role in bridging communication gaps, promoting adaptive learning, and reshaping traditional educational dynamics into a more interactive and collaborative model. Despite ethical and technical concerns, participants expressed optimism regarding AI’s future integration in higher education. The study concludes that AI holds substantial potential to transform student-faculty collaboration, provided that institutions address issues related to digital literacy, infrastructure, and ethical governance.

References

Alfalah, A. A. (2023). Drivers and consequences of ChatGPT use in higher education: Key stakeholder perspectives. Healthcare (Basel, Switzerland), 11(22), 2948. https://doi.org/10.3390/healthcare11222948

Chiu, T. K. F. (2023). Under what conditions do teachers use artificial intelligence? A technological pedagogical content knowledge (TPACK) perspective. Interactive Learning Environments, 1–14. https://doi.org/10.1080/10494820.2021.1917547

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

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

Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to design and evaluate research in education (10th ed.). McGraw-Hill Education.

Grunwald, D., Boese, E., Hoenigman, R., Sayler, A., & Stafford, J. (2015). Personalized attention @ scale: Talk isn't cheap, but it's effective. Proceedings of the 46th ACM Technical Symposium on Computer Science Education, 610–615. https://doi.org/10.1145/2676723.2677283

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

Holmes, W., Porayska-Pomsta, K., Digumarti, K. M., Anderson, D. G., Bernardini, S., Grcar, J., Lücke, M., & Selwyn, N. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504–526. https://doi.org/10.1007/s40593-021-00239-1

Johnson, R. B., & Christensen, L. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). SAGE Publications.

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michalczak, T., Nerdel, C., Pfeffer, J., 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

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2023). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100031. https://doi.org/10.1016/j.caeai.2021.100031

Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020. https://doi.org/10.1016/j.caeai.2021.100020

Perrotta, C., & Selwyn, N. (2020). Deep learning goes to school: Toward a relational understanding of AI in education. Learning, Media and Technology, 45(3), 251–269. https://doi.org/10.1080/17439884.2020.1686017

Saaida, M. B. E. (2023). AI-driven transformations in higher education: Opportunities and challenges. Zenodo. https://doi.org/10.5281/zenodo.8164415

Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill-building approach (7th ed.). Wiley.

UNESCO. (2023). Guidance for generative AI in education and research. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000386693

U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. https://tech.ed.gov/ai/

Wang, J., Zhang, X., & Li, L. (2023). The impact of ChatGPT on university teacher-student interactions. In Proceedings of the 2023 5th International Conference on Literature, Art and Human Development (ICLAHD 2023). Atlantis Press. https://doi.org/10.2991/978-2-38476-170-8_67

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2022). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 19(1), 1–27. https://doi.org/10.1186/s41239-022-00312-5

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Published

2026-05-12

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How to Cite

Transforming Higher Education: The Collaborative Potential of AI for Students and Faculty . (2026). International Journal of Technology in Education and Science, 10(3), 664-675. https://doi.org/10.46328/ijtes.7685