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variables to provide more profound information on the factors influencing the adoption
of generative AI in college students.
2.5. Explanation of the conceptual framework
The current study has a conceptual framework which is anchored on the
Technology Acceptance Model (TAM) but it has added other variables that have been
related to the use of generative artificial intelligence in the digital learning setting. TAM
assumes that behavioral intention to use a technology depends on perceived usefulness
and perceived ease of use as the major factors that affect user behavior. These constructs
define the attitudes of the users towards the technology and eventually their intention to
utilize the technological systems (Sousa and Gomes, 2025; Zhang and Zhao, 2024).
Perceived ease of use in terms of generative artificial intelligence implementation is the
degree to which learners feel that AI applications are user friendly and they do not
demand a lot of effort when undertaking academic tasks. In cases where students within
the learning community find it easy to use AI systems, they tend to consider them useful
in learning. In turn, perceived ease of use has a positive impact on the perceived
usefulness, the extent to which students are of the view that the use of generative AI
technologies will improve their academic performance and learning productivity (Kanont
et al., 2024; Saif et al., 2024).
Moreover, the attitudes of students toward generative AI technologies are also
influential in determining the intention to use these technologies in their behaviors. When
individuals hold positive views of AI technologies, it is likely that they will have greater
intentions of applying them in academic activities like writing, research, and information
search. Moreover, social influence and trust are external factors that have great influence on
the behavior of students in adopting technology. The social influence is the effect that peers,
instructors, and the institutional context have on the desire of students to use AI technologies,
and trust is the degree to which students are assured that the results of AI technology use are
reliable and accurate (Ursavaş and Yildirim, 2025; Ibrahim and Ali, 2025).
AI literacy is another significant variable that can be added to the extended model
of TAM and consists of knowledge and skills of the students related to the artificial
intelligence technologies. AI literate students have a more favorable attitude towards
generative AI systems, as they can comprehend more features and limitations of the
systems, which has a positive effect on their beliefs about the usefulness of the specific
technologies and enhances their intent to use them in teaching. Finally, the behavioral
intention will culminate into the actual use of generative AI tools in digital learning
environments, which will allow students to enjoy intelligent learning systems and
academic aiding technologies based on artificial intelligence (Jin and Li, 2025).
2.6. Research gap
The rapid development of generative artificial intelligence (GenAI) has been a key
factor that has revolutionized the digital learning setting in the higher education system.
Recent works identified the possible potential of AI-powered tools in academic writing,
knowledge production, and custom learning experiences. In spite of these changes, the
integration of the generation of AI technologies in higher education has been uneven and
depends on various technological, psychological and contextual factors. The current
literature on generative AI has been mainly concentrated on the technical potential and
the educational use of this technology, whereas comparatively little effort has been put
towards the identification of the factors that determine the adoption of the generative AI
technology by students in online learning settings. Furthermore, most of the research has

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