Trends in AI Acceptance Among IT Students

Examining Perceived Ease of Use and Usefulness as Determinants of Behavioral Intention to Use AI

Key Takeaways

• Robust Validation of TAM Principles: The Technology Acceptance Model (TAM) continues to underpin research on AI adoption, with perceived ease of use and perceived usefulness serving as key predictors to students’ behavioral intentions.
• Emerging Integration of Additional Variables: Studies from 2019 to 2025 are increasingly integrating trust, ethics, and contextual factors that together refine and extend TAM in academic and professional settings.
• Empirical Evidence Across Diverse Contexts: Research indicates significant geographical and disciplinary variations in AI adoption, with IT students frequently at the forefront of testing novel AI tools such as advanced chatbots and adaptive learning systems.

Introduction

The rapid incorporation of Artificial Intelligence (AI) in higher education has reshaped teaching and learning frameworks globally. Information Technology (IT) students are at the epicenter of this transformation, given their early exposure to advanced technologies. An in-depth look at the empirical trends from 2019 to 2025 reveals that the core principles of the Technology Acceptance Model (TAM) remain pivotal, with perceived ease of use (PEOU) and perceived usefulness (PU) emerging as robust predictive factors for students’ behavioral intentions toward AI usage.

This trend analysis section will explore the evolution of research on AI acceptance among IT students, emphasizing key insights, methodological advances, and theoretical integrations observed in studies conducted during this dynamic period. By discussing trends observed in both quantitative and qualitative studies, this section aims to provide a comprehensive understanding of how PEOU and PU influence the decision-making processes of IT students in adopting AI-powered tools and applications.

Trends in AI Acceptance Research (2019 – 2025)

1. Continued Relevance of the Technology Acceptance Model (TAM)

Since the seminal work on the Technology Acceptance Model by Davis (1989), recent studies have consistently reaffirmed the relevance of TAM in explaining AI adoption among IT students. Modern investigations focus on how perceived ease of use and perceived usefulness remain determinants of a student’s intention to adopt AI technologies.

A multitude of studies published between 2019 and 2025 have validated these constructs within the education sector. For example, research involving AI chatbots specifically aimed at improving learning outcomes has demonstrated that when students find AI tools easy to interact with (high PEOU) and valuable in providing educational support (high PU), their likelihood of using these systems in academic settings increases significantly. This body of research reinforces that the fundamental tenets of TAM not only persist but continue to be refined through the integration of supplementary factors such as trust and transparency.

Notably, quantitative studies have measured usage frequencies, tool variety, and satisfaction scores, thereby providing empirical evidence that the basic TAM constructs explain a substantial variance in students' behavioral intention to use AI. These findings are supported by statistically significant regressions that account for up to 60-70% of variance in usage intentions, indicating strong predictive validity across diverse educational contexts.

2. Integration of Supplemental Variables: Trust, Ethics, and Transparency

While TAM remains central to understanding AI acceptance, researchers have increasingly proposed the inclusion of additional variables that address the evolving digital landscape. A surge in investigations during this period has advocated for the integration of trust, system transparency, and ethical considerations as moderators or mediators in the adoption process.

Studies have demonstrated that trust in AI technologies is significantly intertwined with perceptions of ease of use and usefulness. When AI systems display transparency—such as providing real-time feedback or explainable AI outputs—students’ confidence in these systems increases, thereby enhancing their behavioral intention to use them. For instance, investigations on adaptive learning systems that offer explainable outputs have shown that increased transparency leads to higher perceived usefulness, which in turn directly influences students’ willingness to adopt such tools.

Moreover, ethical considerations have gained prominence as researchers examine how concerns over data privacy and misinformation can moderate the relationship between PEOU, PU, and behavioral intention. In an era where generative AI tools are rapidly evolving, addressing these ethical concerns ensures that technological advancements are balanced with user safety and responsible usage. IT students, in particular, are attuned to these issues, given their technical proficiency and awareness of cybersecurity challenges.

3. Diverse Methodological Approaches: Quantitative and Qualitative Insights

Research into AI acceptance has witnessed methodological diversification over these recent years. Early studies in the reviewed period were predominantly quantitative, relying on surveys and statistical analyses to measure the predictive power of PEOU and PU on behavioral intentions. The increased use of Likert-scale instruments, regression models, and structural equation modeling (SEM) has provided robust statistical evidence for the central constructs of TAM.

Simultaneously, the qualitative dimension has enriched the research landscape by exploring in-depth student experiences, perceptions, and nuanced challenges associated with AI tool adoption. Interviews, focus groups, and case studies have highlighted personal narratives around the ease of use and utility of AI. Such mixed-methods research has offered a more holistic view, incorporating sociocultural and psychological dimensions that influence user acceptance in academic environments.

This methodological expansion has also fostered the development of integrative models that merge TAM with theories such as the Unified Theory of Acceptance and Use of Technology (UTAUT) and Self-Determination Theory (SDT). This approach not only deepens theoretical understanding but also provides practical insights for educational institutions aiming to enhance digital learning environments.

4. These Trends in Business, Healthcare, and Cross-Cultural Studies

Emerging research indicates that the same predictors of AI acceptance observed among IT students are finding resonance in other sectors including business and healthcare. Large-scale surveys and multinational studies have documented that AI adoption trends are consistent across different professional environments. For instance, IT students, often seen as early adopters, frequently exhibit similar behavioral intentions as observed in healthcare students using AI for diagnostic support or business students leveraging AI for data analytics.

Furthermore, studies conducted in diverse geographical regions—from East Asia to Europe—illustrate that cultural and contextual factors can influence perceptions of AI systems. Variations in digital literacy, infrastructural support, and organizational culture have been shown to moderate the strength of the relationship between PEOU, PU, and behavioral intention. Such findings underscore the importance of developing localized strategies and policies that consider cultural nuances and resource disparities.

5. The Influence of Generative AI and Advanced Systems

One of the most groundbreaking developments in this period is the emergence of generative AI systems, such as advanced chatbots and adaptive learning platforms. Research during 2019-2025 has focused on examining how these systems, through their ability to generate content and provide personalized learning experiences, are influencing traditional constructs of perceived ease of use and usefulness.

These generative AI systems have been particularly impactful in enhancing academic support functionalities. For example, studies have shown that tools like ChatGPT and other similar applications are being used widely by IT students for composing code, drafting project write-ups, and serving as interactive tutors. Their user-friendly interfaces, combined with complex underlying algorithms, have significantly simplified complex academic tasks. This simplicity, coupled with high performance, reinforces the notion that both PEOU and PU are critical determinants of the widespread adoption of AI in educational settings.

Moreover, these studies have not shied away from highlighting potential challenges, including over-reliance on AI tools that may hinder critical thinking. Thus, the literature presents a balanced view, cautioning educators to design curricula that leverage AI’s benefits while mitigating potential negative impacts on cognitive skills.

Trends Summary Table

Final Thoughts and Conclusion

This review of trends in AI acceptance among IT students provides a comprehensive snapshot of a field in constant evolution. The persistent validation of TAM reinforces the importance of perceived ease of use and usefulness in predicting behavioral intentions. However, as AI technologies evolve, research has increasingly recognized the need to incorporate supplementary factors such as trust, ethical considerations, and cultural nuances. These factors not only add depth to theoretical frameworks but also pave the way for more sophisticated integrative models that provide nuanced insights into technology adoption.

Academic institutions and policymakers can leverage these findings to develop strategies that enhance digital learning experiences while ensuring that AI tools continue to augment rather than diminish critical learning skills. Emphasizing a balanced approach will be key in harnessing the transformative capabilities of AI, ensuring that IT students and other stakeholders benefit responsibly and sustainably.

References

https://doi.org/10.1108/ITP-07-2019-0338
https://doi.org/10.1016/j.chb.2020.106444
https://doi.org/10.1016/j.compedu.2022.104836
https://doi.org/10.1016/j.techsoc.2023.101457