Jul 2026· Knowledge Management & E-Learning An International Journal· 0 citations
TL;DR
It is argued that AI self-efficacy, characterized as a student’s confidence in their capacity to employ AI ethically and efficiently, constitutes a substantial determinant and the focus of instruction should transition from a “one-size-fits-all” approach to tailored interventions designed to enhance student empowerment.
Abstract
Generative artificial intelligence (GenAI) has brought both challenges and opportunities for teachers in higher education. This study does not focus on restrictive methods; instead, it explores how educators can actively promote academic integrity. We contend that AI self-efficacy, characterized as a student’s confidence in their capacity to employ AI ethically and efficiently, constitutes a substantial determinant. This paper delineates significant findings derived from a survey administered by 177 university students. A moderation analysis indicates that the adverse correlation between AI usage frequency and academic integrity is markedly diminished among students exhibiting high AI self-efficacy. Moreover, a cluster analysis clearly delineates three distinct AI user profiles: Confident and Cautious Users, Pragmatic High Users, and Dependent Users. Demographic studies reveal a significant correlation between these profiles and the students’ academic disciplines. The results suggest that the focus of instruction should transition from a “one-size-fits-all” approach to tailored interventions designed to enhance student empowerment. This article outlines practical implications and customized strategies for each student profile.
The rapid integration of generative artificial intelligence (GenAI) into marketing practice presents new challenges and opportunities for marketing education, yet little research examines how students, faculty, and industry professionals navigate divergent expectations for AI use across institutions. This study addresses that gap using Role Theory from the organizational behavior literature, drawing on three sources: industry data (N = 521), a faculty survey (N = 24), and student survey responses from three universities (N = 574). Industry data suggests AI use in the workplace is still taking shape rather than fully settled; against that backdrop, we hypothesize that AI Efficacy and Classroom AI Preparation both differ across university locations, consistent with self-efficacy theory and Role Theory’s expectation that unclear or inconsistent role expectations may be associated with lower confidence and preparation. A related research question asks whether differing Role Clarity across institutions may help interpret these differences. Among spontaneous open-ended comments about AI-use expectations, students more often described ambiguity than clear policy understanding. Synthesizing these insights, the paper introduces the INSPIRE Model, a framework for adaptive AI integration in marketing curricula, and calls for further research testing classroom interventions.
Sarah Fischbach, Michael Pettiette, Wangari Njathi· Journal for advancement of m...· 0 citations
First-year university students’ perceptions of generative AI in academic work are investigated, foregrounding student agency in a Global South context and offering pedagogical and policy implications for responsible AI adoption.
Sharifuzzaman, M. Rahman· Asian Journal of Contemporar...· 0 citations
The rapid integration of Artificial Intelligence (AI) into modern newsrooms presents both structural challenges and transformative possibilities for media education. Grounded in the Technology Acceptance Model (TAM), this study which was conducted at the beginning of 2025, explores early reactions towards using of AI, empirically investigates how Georgian journalism students perceive, evaluate, and adopt AI tools within their academic and professional workflows. Based on a stratified sample of N=318 students across major Georgian universities (78.5% completion rate), inferential statistics ( -tests and path analysis) were conducted to test hypotheses regarding gender, degree level, and prior technical training. The findings demonstrate high overall technology acceptance, driven primarily by prior AI training ( ) and graduate-level studies ( ). Gender differences yielded no statistically significant variation in perceived ease of use. These results revial a crucial gap between student enthusiasm and institutional preparation, offering an empirical foundation for modernizing journalism curricula in transitional media environments.
Dali Osepashvili· Studies in Media and Communi...· 0 citations
The rapid advancement of artificial intelligence (AI) in education has identified the need to understand how future educators perceive and engage with these technologies. This study investigates the AI self-efficacy of English as a Foreign Language (EFL) student teachers across three distinct educational contexts: Japan, Poland, and Slovakia. Recognising the crucial role of self-efficacy in technology adoption, this research aims to compare how pre-service teachers in these countries perceive their capabilities in utilising AI tools for language learning and teaching. The study employed a mixed-method design to collect quantitative data using a specifically designed evaluation scale and qualitative data via an open-ended questionnaire. The sample comprised 98 EFL student teachers from the selected countries. Results revealed observable differences in AI self-efficacy scores among participants from Japan, Poland, and Slovakia, with Japanese participants reporting higher confidence levels than their Polish and Slovak counterparts. The findings highlight a need for further research examining which contextual, educational, or experiential variables may underlie these cross-country differences.
Z. Kráľová, Osamu Takeuchi, Viktorie Vršanská et al.· Asian-Pacific Journal of Sec...· 0 citations
Generative artificial intelligence (GenAI) is transforming assessment practices in higher education, offering opportunities for efficiency and personalization while raising concerns related to academic integrity, fairness, and responsible use. Understanding how different users perceive and engage with GenAI is essential, particularly within rapidly advancing digital contexts such as the United Arab Emirates (UAE).
This study investigates students' and instructors' perceptions of GenAI integration in assessment and examines the relationships among perceived usefulness, perceived ease of use, perceived risk, attitudes, and responsible use, including the moderating role of user type.
An explanatory sequential mixed‐methods design was employed. Quantitative data were collected from 193 participants (163 students and 30 instructors) using a validated survey instrument. Correlation, regression, mediation, and moderation analyses were conducted, complemented by qualitative thematic analysis of open‐ended responses.
Findings revealed significant positive relationships among all constructs. Perceived usefulness and ease of use were combined into a higher‐order Technology Acceptance Model construct that strongly predicted attitudes. Perceived risk significantly influenced both attitudes and responsible use, emerging as the strongest predictor of responsible behaviour. Attitude partially mediated the relationship between technology acceptance and responsible use. User role significantly moderated this relationship, with a stronger effect observed among instructors. Qualitative findings indicated generally positive but cautious attitudes, highlighting concerns about over‐reliance, accuracy, and academic integrity, alongside a continued intention to use GenAI.
GenAI adoption in assessment is shaped by a balance between perceived benefits and risk awareness. The findings highlight the importance of promoting responsible use through clear policies, training, and assessment design, offering practical implications for integrating GenAI within higher education systems.
Fatima Salem Al Mohsen, Areej Elsayary· Journal of Computer Assisted...· 0 citations
This study explored the perceptions of 80 first-year college students regarding the use of artificial intelligence as a learning tool in Purposive Communication. Using a mixed-methods approach, the research integrated quantitative survey data and qualitative responses to provide a comprehensive understanding of students’ experiences and attitudes. The quantitative component examined the extent to which students perceived AI as beneficial in relation to efficiency, comprehension and communication skills. Meanwhile, the qualitative data provided deeper insights into students’ reflections, highlighting both the advantages and concerns associated with AI use. Findings revealed that students generally viewed artificial intelligence as a supportive academic resource that enhanced productivity and facilitated clearer expression of ideas. Many participants reported that AI tools assisted in organising thoughts, improving grammar and simplifying complex concepts. However, the results also indicated concerns related to overreliance and ethical considerations, particularly in relation to academic integrity and independent learning. These concerns suggest that while AI offers educational benefits, its use may also present challenges if not properly guided. The triangulation of findings underscores the dual role of AI in education as both an enabler of learning and a potential source of dependency. The study emphasises the importance of responsible and guided integration of AI tools within instructional contexts. It further highlights the need for clear institutional policies and the development of students’ critical awareness when using such technologies. Overall, the research contributes to the growing discourse on artificial intelligence in education by presenting empirical evidence from the perspective of first-year college students, thereby informing future pedagogical practices and policy development.
Jefferson J. Acala· Asian Journal of Education a...· 0 citations
