Home / Current Issue / Paper 1714887
Factors Influencing Students’ Intention to Adopt Gemini in Education
Subject area: Science,Engineering and Technology · Area of research: AI Assistant
Abstract
The growing development of artificial intelligence technologies is significantly transforming conventional educational practices by introducing intelligent academic support systems. Google Gemini, a generative AI system, offers capabilities including automated content generation, conceptual clarification, academic problem solving, and interactive learning support. However, the adoption of AI tools by students in the formal learning environment is still varied and affected by various behavioral and environmental factors. This research study aims to explore the determinants that affect the academic adoption behaviour of students towards Google Gemini in higher education institutions. An integrated research framework is designed by expanding the modern technology adoption theories to AI-supported learning environments. The proposed framework will assess the impact of Learning Performance Expectancy, Interaction Simplicity, Cognitive Engagement, AI Reliability Perception, Ethical Transparency Awareness, and Academic Environment Influence on Academic Adoption behavior. A quantitative methodological approach was used, and the data was gathered through a structured survey among university students who are acquainted with AI-assisted learning tools. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used as the research method to explore the relationships among the constructs. The results show that perceived academic value, engagement experience, and reliability perception are significant contributors to the willingness to adopt Gemini in academic contexts. Encouragement and awareness of ethics also enhance adoption behavior. The paper adds to the growing body of literature on the adoption of generative AI in academic contexts by underlining the importance of trust, engagement, and readiness in the academic ecosystem for successful implementation, aside from technological efficiency.
Keywords
Google Gemini, Generative Artificial Intelligence, Academic Adoption Behavior, Higher Education, AI-Assisted Learning.
References
[1] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
[2] Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
[3] George, B., & Wooden, O. (2023). Managing strategic transformation of higher education through artificial intelligence. Administrative Sciences, 13(9), 196.
[4] Shin, D. (2021). The effects of explainability and castability on perception, trust, and acceptance. International Journal of Human–Computer Studies, 146, 102551.
[5] Wu, W., Zhang, B., Li, S., & Liu, H. (2022). Exploring factors of willingness to accept AI-assisted learning environments. Frontiers in Psychology, 13, 870777.
[6] Chatterjee, S., & Bhattacharjee, K. (2020). Adoption of AI in higher education. Education and Information Technologies, 25(5), 3443–3463.
[7] Wang, Y., Liu, C., & Tu, Y. F. (2021). AI applications in higher education. Educational Technology & Society, 24(3), 116–129.
[8] Cepeda-Carrion, G., Cegarra-Navarro, J., & Cillo, V. (2019). Tips to use PLS-SEM in knowledge management. Journal of Knowledge Management, 23(1), 67–89.
[9] E. H. Miller, ―A note on reflector arrays (Periodical style—Accepted for publication), ‖ IEEE Trans. Antennas Propagate., to be published.
[10] J. Wang, ―Fundamentals of erbium-doped fiber amplifiers arrays (Periodical style—Submitted for publication), ‖ IEEE J. Quantum Electron., submitted for publication.
How to cite this paper
@article{1714887,
author = {Utkarsh Kaul, Prof Dr. Sandeep Raghuwanshi},
title = {Factors Influencing Students’ Intention to Adopt Gemini in Education},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {277-283},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1714887.pdf},
abstract = {The growing development of artificial intelligence technologies is significantly transforming conventional educational practices by introducing intelligent academic support systems. Google Gemini, a generative AI system, offers capabilities including automated content generation, conceptual clarification, academic problem solving, and interactive learning support. However, the adoption of AI tools by students in the formal learning environment is still varied and affected by various behavioral and environmental factors. This research study aims to explore the determinants that affect the academic adoption behaviour of students towards Google Gemini in higher education institutions. An integrated research framework is designed by expanding the modern technology adoption theories to AI-supported learning environments. The proposed framework will assess the impact of Learning Performance Expectancy, Interaction Simplicity, Cognitive Engagement, AI Reliability Perception, Ethical Transparency Awareness, and Academic Environment Influence on Academic Adoption behavior. A quantitative methodological approach was used, and the data was gathered through a structured survey among university students who are acquainted with AI-assisted learning tools. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used as the research method to explore the relationships among the constructs. The results show that perceived academic value, engagement experience, and reliability perception are significant contributors to the willingness to adopt Gemini in academic contexts. Encouragement and awareness of ethics also enhance adoption behavior. The paper adds to the growing body of literature on the adoption of generative AI in academic contexts by underlining the importance of trust, engagement, and readiness in the academic ecosystem for successful implementation, aside from technological efficiency.},
keywords = {Google Gemini, Generative Artificial Intelligence, Academic Adoption Behavior, Higher Education, AI-Assisted Learning.},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1714887}
}