Page 643 - ISC PROCEEDINGS 21.4

learning requirements. Some of the tasks that AI-powered systems could perform are the
possibility of giving real-time feedback and recommending appropriate materials to help
students with their studies and helping them learn on different academic issues.
Consequently, generative AI systems are viewed as useful learning aids that are effective
in improving academic performance and learning efficiency (Li and Wong, 2025;
Mogelvang and Pedersen, 2025; Tran and Nguyen, 2025).
Nevertheless, in spite of these advantages, some researchers have raised the issues of
the ethical and pedagogical aspects of generative AI in education. The effects of issues like
academic integrity, misinformation and over-dependence on automated systems might affect
the learning behavior and academic performance of the students. Thus, universities need to
establish policies and guidelines that would guarantee responsible and ethical use of the
generative AI tools in the digital learning settings (Strzelecki, 2023; Ibrahim and Ali, 2025).
2.2. Technology acceptance model and AI adoption
This has been a major issue in the research in information systems and educational
technology to understand the factors influencing the adoption of technology. Technology
Acceptance Model (TAM) has been extensively applied in the explanation of how people
decide to adopt and use technology systems. TAM states that the behavioral intention of
users to adopt a technology depends mainly on perceived usefulness and perceived ease
of use, which control the attitudes of users towards technology and eventually define
their willingness to use it (Sousa and Gomes, 2025; Zhang and Zhao, 2024).
Based on recent studies, TAM has been implemented to study the adoption of
generative AI technologies in the context of higher education. Empirical studies suggest
that utility and perceived ease of use are key factors that determine the level of
willingness of students towards the use of generative AI tools in academic tasks. As soon
as students find the AI technologies convenient and simple to use, they will tend to feel
positive towards these systems and will use them in their learning processes (Saif et al.,
2024; Kanont et al., 2024).
Moreover, a number of researchers indicate that TAM might not be a sufficient
explanation of the uptake of emerging technologies like generative AI. Due to this,
scholars have suggested combining TAM with other theoretical models to enhance its
explanatory capability. As an example, the research using TAM and innovation diffusion
theory alongside self-determination theory has revealed that psychological and
contextual elements have a remarkable impact on students intention to use AI
technologies in learning settings (Ghimire and Edwards, 2024; Tbaishat and Al- Okaily,
2026).
2.3. Factors influencing students’ adoption of generative AI
Despite that TAM offers a powerful theoretical background of the technology
adoption process, scholars note that it is necessary to focus on supplementary variables,
which affect generative AI adoption in higher education. Social influence is one of the
most significant factors that have an impact on decisions made by students to use new
technologies due to the influence of their peers, instructors, and institutional support.
Studies indicate that in cases where students believe that their fellow learners and
educators endorse the use of the generative AI tools, they tend to form positive attitudes
towards these tools and use them in their learning endeavors (Ursavaş & Yildirim, 2025;
Wang and Li, 2025).
The other important aspect that affects adoption of AI is the confidence in artificial
intelligence systems. Trust indicates the reliability, accuracy and ethical application of AI

642