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A Structural equation modeling analysis of generative AI chatbots adoption among students and educators in higher education
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In an era where artificial intelligence (AI) is reshaping educational paradigms, this study explores AI-based chatbot adoption in higher education among students and educators. Employing a Structural Equation Modeling (SEM) approach, the research focuses on developing and validating a comprehensive model to understand the multifaceted factors impacting the acceptance and use of these chatbots. The methodology integrates an extensive literature review, construction of a theoretical model, administration of a detailed questionnaire to a representative sample from the higher education sector, coupled with advanced SEM techniques for data analysis and interpretation. The SEM analysis validates the model's robustness and highlights the relationships between several key factors affecting users' perspectives and chatbots adoption. Results reveal a predominantly positive perception towards AI-chatbots among both students and educators, underscoring the potential to substantially enrich their educational journey. However, it also uncovers critical concerns pertaining to trust, privacy, response bias, and information accuracy. Moreover, the study offers valuable insights into how moderators such as technological proficiency, user roles, and gender influence the adoption model relationships. This emphasizes the need for customizing AI-chatbots deployment to meet the diverse needs of users effectively. Contributing a robust framework for understanding users' perceptions towards AI-chatbots and their adoption patterns, this study offers actionable insights for educational leaders, policymakers, and technology developers. It also lays the groundwork for future research, including longitudinal studies to evaluate the long-term impact of these technologies, investigations into their effect on learning outcomes, and explorations of the ethical and privacy considerations involved.
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“AI use regulations are discussed in relation to data privacy, ethical chatbot usage, and responsible implementation, highlighting the need for clear, enforceable institutional frameworks (Ichikawa et al., 2025; Saihi et al., 2024).”
From Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review (2026) · cites this paper as Saihi et al., 2024
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“Lastly, predictive modelling of GenAI learning outcomes in the literature is dominated by regression and structural equation approaches [1, 52, 67]”
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Sorted by citations
Nazaretsky, T., Mejia‐Domenzain, P., Swamy, V., Frej, J., & Käser, T. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100368 · Cited by 164
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“Similarly, Saihi et al. (2024) emphasized that PU and perceived ef fi cacy factors have strong positive effects on the intention to adopt AI chatbots, but concerns about “ trust and privacy, ” “ interaction, ” and “ information quality ” are evident.”
Influential
Metadata
- Authors
- Saihi, A., Ben-Daya, M., Hariga, M., & As'ad, R.
- Affiliations
- American University of Sharjah (AE)
- Corresponding author
- Saihi, Afef
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2024-12
- Volume
- 7
- Pages
- 100274
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4401295314
- Cited by
- 75
- References
- 242
- Citations by year
- 2026: 29 · 2025: 41 · 2024: 5
- FWCI
- 28.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- generative AI chatbots, technology adoption, higher education, students, educators, trust in AI, privacy concerns, response bias, information accuracy, technological competence, gender differences, user roles, artificial intelligence in education
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