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technologies by the users. Research shows that students who place their faith in AI
systems tend to use AI-generated written works and use generative AI-based applications
in their educational activities (Fel and Osman, 2025; Ibrahim and Ali, 2025).
Also, AI literacy and digital competence are significant in determining adoption of
generative AI technologies by students. Higher technological knowledge and AI literacy
allow students to see more possibilities and limitations of AI tools, which leads to a
positive impact on their attitude to usefulness and readiness to use the technologies in
the academic setting (Sergeeva and Ivanov, 2025; Zhou and Chen, 2023).
Several other determinants considered as a result of recent empirical studies are
perceived risk, facilitating conditions, and technological innovativeness, which also
determine the adoption of generative AI. The said factors affect the perception of
students towards the generative AI technologies and have an effect on their behavioral
intentions to implement AI tools in digital learning settings (Chen et al., 2023; Jin and Li,
2025).
2.4. Extended technology acceptance model for generative AI adoption
Due to the complexity of generative AI technologies, more and more scholars
suggest the adoption of the classic Technology Acceptance Model that includes other
psychological and contextual factors. The constructs of trust, AI literacy, social influence,
and facilitating conditions are also combined in extended TAM frameworks to improve
the clarification of the behavioral intentions of users towards AI technologies (Sousa and
Gomes, 2025; Ursavaş and Yildirim, 2025).

Figure 1. Conceptual framework
Source: Author
Empirical studies have shown that longer TAM models have greater predictive
capability in explaining the adoption of generative AI in higher education settings.
Although perceived usefulness or perceived ease of use are still the key factors
influencing technology adoption, other variables, including trust in AI systems and social
influence, are effective to increase the explanatory capacity of technology acceptance
models (Saif et al., 2024; Wang and Li, 2025).
Therefore, the extension of TAM offers a more detailed model of studying the
adoption of generative artificial intelligence tools in digital learning classrooms by
students. Extended TAM models combine technological, psychological, and social

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