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used the conventional Technology Acceptance Model (TAM) to study technology
adoption; nevertheless, the classical TAM paradigm largely focuses on perceived
usefulness and perceived ease of use and might not adequately represent the
complexities that may arise with new technology like generative AI.
Moreover, the literature tends to examine AI adoption through the lens of overall
technology acceptance or use of AI tools by educators in comparison to somewhat scarce
studies that look at the behavioral intentions of students to adopt generative AI in the
environment of digital learning in higher education. Moreover, such significant variables
as trust in AI systems, social influence, and AI literacy have not been paid much attention,
and the TAM-related studies of generative AI adoption have been conducted so far.
Thus, these gaps need to be filled by expanding the Technology Acceptance Model
by including more contextual variables that can explain the behavioral intentions of
students to use generative AI technologies in a better way. The contribution of the
current study to existing literature is that the researcher has formed a comprehensive
framework of TAM that can be used to analyze the conditioning factors surrounding the
adoption of generative artificial intelligence in online learning facilities by students in
institutions of higher learning.
2.7. Research objectives
To examine the relationship between perceived ease of use and perceived
usefulness of generative AI technologies among university students.
To investigate the influence of perceived usefulness on students’ attitudes toward
generative artificial intelligence tools in digital learning environments.
2.8. Research questions
How does perceived ease of use influence perceived usefulness of generative AI
technologies among university students?
To what extent does perceived usefulness affect students’ attitudes toward
generative artificial intelligence tools in digital learning environments?
2.9. Theoretical framework
The paper is based on the Technology Acceptance Model (TAM) and relates it to
understanding why students start using generative artificial intelligence (GenAI)
technologies in digital learning environments in higher education. The theoretical
framework is a combination of cognitive, affective, social, and psychological determinants,
which serve to explain AI adoption behavior comprehensively.
2.9.1 Technology acceptance model (TAM)
One of the most influential theoretical models that was employed to explain the
acceptance and use of new technologies by individuals is the Technology Acceptance
Model (TAM) that was first introduced by Davis (1989). TAM assumes that two major
cognitive beliefs, namely, perceived usefulness (PU) and perceived ease of use (PEOU),
are the primary determinants of the behavioral intention of users to adopt a technology.
Perceived usefulness is the degree to which one believes that the utilisation of a
specific system can improve the performance. When applied to generating AI, perceived
usefulness is the view of students that AI tools can help them to work on their studies
more effectively and efficiently, as well as to acquire knowledge. Perceived ease of use is
the level at which one believes that a system is less demanding. In the case of generative
AI tools, user-friendliness is connected to user- friendly interface, availability, and
interaction.

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