A Structural equation modeling analysis of generative AI chatbots adoption among students and educators in higher education
Open accessAbstract
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.
“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
Predictive modelling of GenAI learning outcomes in the literature is dominated by regression and structural equation approaches.
“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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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 146 · see citing works
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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.”
InfluentialJayasinghe, S., Gamage, K. A. A., Yang, D., Cheng, C., Disanayake, C., & Apeji, U. D. (2026) · Education Sciences · doi:10.3390/educsci16010137 · Cited by 23 · see citing works
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“However, Saihi et al. stated that there are challenges related to trust, response bias, privacy, and information accuracy that are major problems [16], although recently this has begun to be unproven due to the perceived benefits and relatively acceptable content [22, 23].”
InfluentialPhuong, D. T. T., Silalahi, A. D. K., Chang, W., Tedjakusuma, A. P., & Eunike, I. J. (2025) · Business Strategy & Development · doi:10.1002/bsd2.70122 · Cited by 11 · see citing works
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“These studies have expanded TAM to include additional constructs relevant to GenAI, such as facilitating conditions (Cabero-Almenara et al., 2024; Saihi et al., 2024) and teachers’ intentions to use the technology (Hu et al., 2025), both of which are linked to perceived usefulness and ease of use.”
Just-in-Time News: An AI Chatbot for the Modern Information Age
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“These constructs have been previously validated through a structural equation modeling (SEM) analysis confirming acceptable reliability and both convergent and discriminant validity (Saihi et al., 2024).”
InfluentialSelf-citationZahid, H., Danish, M., Ali, S., Huo, C., & Khalid, N. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2677727 · Cited by 7 · see citing works
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Firm-Level Digitalization for Sustainability Performance: Evidence from Ningbo City of China
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“Effective adoption thus requires policies, training, and ethical frameworks that simultaneously build trust and reduce anxiety (Saihi et al., 2024).”
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“Structural model research in related fields similarly indicates that factors like technological readiness and self-efficacy influence AI tool use and attitudes (Saihi et al., 2024; Shi et al., 2025).”
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InfluentialTowards a Sustainable and Ethical Integration of AI Chatbots in Higher Education
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Uso de IA para desarrollo de habilidades blandas: revisión sistemática de la literatura
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Generating Change: AI as an Opportunity to Address Long-Standing OER Challenges
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“Explores how hallucinations shape critical thinking and memory [6], [25], [26].”
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“Past studies show students are more likely to use GenAI if they deem it helps their learning [6, 13].”
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“This aligns with the findings of Saihi et al. (2024) that the reliability of GenAI is positively related to efficiency.”
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Exploring the Integration of AI in Interactive Lesson Planning: A Study of Delhi NCR Teachers
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“In particular, AI tools such as chatbots, gamified learning platforms, augmented and virtual reality (AR/VR), adaptive learning systems, and AI-based content generators are increasingly used in interactive lesson planning and instructional delivery[2].”
Mapping AI in Social Sciences: A Bibliometric Study of Its Influence in Higher Education
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AI-Assisted Language Assessment: Basis for Senior High School Language Arts Instruction Assessment
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Chatbots-based Systematic Review for Student Learning and Engagement with LLMs
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Ninaquispe, J. C. M., Tocto, R. I. M., Jiménez, F. A. A., Rázuri-Rubio, H. H., Huatay, K. C. V., & Rivera, L. G. A. (2025) · Lecture notes in educational technology · doi:10.1007/978-981-95-4689-3_17
Research Methodology in Higher Education in Ecuador
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WIP: An AI-Based Virtual Assistant for Supporting a Large Engineering Course
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“…framework, and data will be analyzed using structural equation modeling to determine the contribution of constructs like performance expectancy, effort expectancy, social influence, and facilitating conditions on the intention to use the chatbot in the future [7], [18], [19], [20], [21], [22].”
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Yapay Zekâ Araçlarının Kullanımına İlişkin Akademisyenlerin Deneyim ve Değerlendirmeleri
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“A pesar de estos retos, la IA se posiciona como una herramienta clave para fomentar métodos educativos más efectivos en la era pospandemia, alineándose con tendencias globales hacia la innovación pedagógica (Alhwaiti, 2023; Saihi et al., 2024).”
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How Digital Literacy Affects AI Literacy Among Preservice Teachers: Media Literacy as a Moderator
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- 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
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › 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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