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Concerning the aspect of innovation, collaborative knowledge sharing and open
innovation processes tends to affect the technological adoption in organizations.
According to the research, organizations are more and more dependent on external
sources of knowledge and collaborative networks to boost their innovation performance
and technological growth (Carmona-Lavado et al., 2021). The same process can affect the
use of generative AI technologies in higher education as universities implement them into
the process of teaching and learning.
Although there is a rising trend in the use of the generative AI technologies, it
remains affected by the users perceptions, attitudes, and behavioural intention towards
the use of technology. Technology Acceptance Model (TAM) has been extensively utilized
in the explanation of how people accept and adopt emerging technologies. TAM indicates
that, in the perception of users, both usefulness and perceived ease of use are crucial
factors that affect their attitude and behavioural intentions regarding the use of
technology (Zou and Huang, 2023; Ma, 2025). Past research, which has explored the
implementation of generative AI in higher education, has indicated that the attitudes,
perceived usefulness, and social aspects are significant in informing the intentions of
people using AI-based systems (Miranda and Chamorro-Mera, 2025; Ma, 2025).
Since the technologies of generative AI are quickly spread and can soon become an
important aspect of the digital learning environment, it is crucial to explore the factors
affecting the use of such technologies by students. The perception and behavioural
intentions of students regarding generative AI may be used to formulate effective policies
that allow universities to incorporate AI technologies in their teaching and learning
activities. Thus, this research paper will analyze how generative artificial intelligence can
be adopted by digital learning students by extrapolating the Technology Acceptance
Model in higher education. Determining the aspects that affect the acceptance and use of
generative AI tools by students, the study will add to the existing literature on AI-modified
education and the innovation of digital learning.
2. Literature review
2.1. Generative artificial intelligence in higher education
The blistering development of artificial intelligence had a strong impact on the
contemporary educational systems, especially the introduction of Generative Artificial
Intelligence (GenAI) technologies. Such technologies as large language models and
intelligent tutoring systems can produce human text, help to create knowledge, and offer
students automatic academic support. According to recent studies, high education
institutions have become more and more equipped with generative AI tools to ensure
digital education, academic writing, and customized learning (Chen et al., 2023; Strzelecki,
2023; Zhou and Chen, 2023).
The adoption of the generative AI technologies in universities has enabled students
to have greater access to information and digital learning platforms. The use of AI- driven
systems enables students to brainstorm and summarize complicated academic work as
well as give them automated feedback when engaging in learning processes. These
technological advancements have increased the digital transformation of higher
education and have stimulated the use of new learning space that facilitates student
interaction and academic performance (Kanont et al., 2024; Kong and Song, 2024; Jin and
Li, 2025).
In addition to this, generative AI technologies can support personalized learning
since students can communicate with intelligent systems, which can adjust to their

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