Understanding students’ perceptions of generative AI: Implications for pedagogy and graduate employability

Oct 15, 2026

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As artificial intelligence (AI) transforms workplaces, understanding how future graduates engage with AI technologies is crucial for enhancing employability. This study examines higher education students’ familiarity with and perceptions of generative AI (GenAI) in learning. Using the Technology Acceptance Model (TAM) with personal innovativeness, an online survey (April 30–May 11, 2024; n=233) finds significant positive relationships, with perceived usefulness, ease of use, attitude, and intention predicting actual GenAI use, alongside gender and field effects.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages26

Reading Time1 h 6 min

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Abstract

Introduction

The Journal of Teaching and Learning for Graduate Employability ISSN: 1838-3815 (online) Journal Homepage: [https://ojs.deakin.edu.au/index.php/jtlge/](https://ojs.deakin.edu.au/index.php/jtlge/) Understanding students’ perceptions of generative AI: Implications for pedagogy and graduate employability Clara Rispler1, Michal Mashiach Eizenberg2 and Gila Yakov2,3 Corresponding author: Clara Rispler ([clarar@yvc.ac.il](clarar@yvc.ac.il)) [1](1 M.A. in)[ M.A. in](1 M.A. in) Organizational Dev. and Consulting/Department of Human Services, The Max Stern Yezreel Valley College. 2 Department of Health Systems Management, The Max Stern Yezreel Valley College. 3 Center for Teaching Development, The Max Stern Yezreel Valley College. Abstract As artificial intelligence (AI) transforms workplaces, understanding how future graduates engage with AI technologies is crucial for enhancing employability. This study investigates higher education students’ familiarity with and perceptions of generative artificial intelligence (GenAI) in their learning. Using the Technology Acceptance Model (TAM) and incorporating personal innovativeness in information technology, we examined factors influencing students’ adoption of GenAI. An online survey was conducted between April 30 and May 11, 2024, with 233 students from a college in northern Israel completing the questionnaire. Results revealed significant positive correlations, supporting the study’s theoretical framework. Personal innovativeness was strongly related to TAM variables. Perceived usefulness, perceived ease of use, attitude toward use and behavioural intention to use the technology were each significant predictors of actual GenAI use. Gender and field of study influenced adoption, with both males and students studying information systems and economics showing higher usage rates. To the best of our knowledge, this study is the first to integrate TAM with personal innovativeness and demographic factors to assess student engagement with GenAI. The findings provide a theoretical and empirical foundation for understanding student responses to new technologies in higher education. The identified gender gap and field-based differences suggest that tailored approaches are necessary to enhance student engagement with GenAI tools. Overall, the findings imply that teaching practices should include scaffolded, inclusive strategies that foster GenAI literacy, adaptability and ethical awareness. Such approaches may strengthen students’preparedness for AI-enhanced workplaces and support higher education’s role in assuring graduate employability. Keywords GenAI, TAM (Technology Acceptance Model), personal innovativeness in IT, employability, AI in education, workforce transformation, digital competencies, higher education policy Introduction Amid ongoing debates on the impact of technology in the sphere of education (Baytak, 2023; Tamimet al., 2011), the emergence of artificial intelligence (AI) presents transformative promise but also considerable challenges (Nguyen et al., 2023) . With its ability to execute tasks that normally require human intelligence, by simulating cognitive processes using computers (Dwivedi et al., 2023), AI holds immense potential for education (Huang et al., 2023) . Already, AI is used in realms such as language acquisition (Ali et al., 2023), mathematics education (Chu et al., 2021) and medical training (Wood et al., 2021) . Beyond its immediate uses, the integration of AI in education not only enhances students’academic engagement but also has the potential to equip them with skills that are increasingly essential for employability in an AI-driven job market (Zouhaier, 2023) . This facet of AI adoption within educational systems is highlighted by emerging concepts such as “AI readiness”(Karaca et al., 2021),“AI capabilities”(Markauskaite et al., 2022) and “AI literacy”(Long & Magerko, 2020), which capture the need to incorporate AI as part of stu

FAQs

What research question does the study address about generative AI?

It investigates how higher education students perceive and engage with generative AI (GenAI) in their learning and what drives their adoption.

Which theoretical framework and additional factor are used to explain adoption of GenAI?

The study uses the Technology Acceptance Model (TAM) and incorporates personal innovativeness in information technology.

What variables were found to predict students’ actual use of GenAI?

Perceived usefulness, perceived ease of use, attitude toward use, and behavioural intention to use were significant predictors of actual GenAI use.

Oct 15, 2026

Uploaded by Putri