Jul 2026· International Journal of Drug Delivery Technology· Vol 16· 0 citations· 6 references
TL;DR
It is concluded that strengthening critical AI literacy, implementing authentic assessment practices, and reinforcing human agency are essential for preserving intellectual autonomy, ethical accountability, and professional identity among future educators in the digital era.
Abstract
The rapid proliferation of generative artificial intelligence (GenAI) has profoundly transformed teaching, learning,
and assessment practices, creating unprecedented challenges for academic integrity in higher education. This study
investigates the relationships between the level of AI usage, AI literacy, academic stress, and reliance on AI, as
well as the implications of AI reliance for academic integrity awareness among pre-service teachers. Adopting a
quantitative cross-sectional survey design, data were collected from 350 students enrolled in teacher education
programs and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The proposed
model further examined the mediating roles of critical thinking and academic self-efficacy in the relationship
between AI reliance and academic integrity awareness. The findings indicate that academic stress and the level of
AI usage significantly increase students' reliance on AI, whereas AI literacy significantly reduces such
dependence. Furthermore, reliance on AI exerts a significant negative effect on academic integrity awareness. The
mediation analysis demonstrates that critical thinking and academic self-efficacy significantly mitigate the adverse
effects of AI reliance on integrity awareness. The results suggest that AI reliance extends beyond instrumental use
and increasingly functions as a normative reference and epistemic authority, contributing to the emergence of
phenomena such as delegated ethics and the illusion of competence. This study concludes that strengthening
critical AI literacy, implementing authentic assessment practices, and reinforcing human agency are essential for
preserving intellectual autonomy, ethical accountability, and professional identity among future educators in the
digital era.
This study explores the perceptions of college students toward the integration of Artificial Intelligence (AI) in education, with a focus on its general use, role in learning processes and outputs, and associated ethical concerns such as transparency, fairness, privacy, and data security. A total of 293 students from the Nueva Ecija University of Science and Technology – Gabaldon Campus participated in the study, representing various programs including Agriculture, Education, Information Technology, and Hospitality Management. A validated researcher-made questionnaire was used, comprising 25 items distributed across five constructs. Data were analyzed using descriptive statistics, mean and standard deviation, to determine levels of agreement, while Principal Components Analysis (PCA) was employed to examine communalities and ensure construct validity. The findings revealed a general agreement among students regarding the positive role of AI in enhancing learning experiences, increasing assignment efficiency, and developing skills relevant to future careers. However, lower mean scores in areas such as reliance on AI for research and collaboration suggest a need for further support and training. Notably, students expressed high confidence in the ethical management of their data, with strong agreement on institutional safeguards and transparency in data usage. Communality values ranging from 0.459 to 0.889 indicated that most items were well-represented within their respective constructs. The results highlight both the benefits and challenges of AI integration in education. The study recommends that institutions enhance AI-related communication, training, and ethical safeguards to support students in maximizing the potential of AI tools in their academic and professional development.
J. L. Galang, A. T. Capinding, Rosalie A. Pineda· TEM Journal· 0 citations
With the emergence of artificial intelligence (AI) in recent decades, AI literacy has become a critical component of K-12 education. Although research on AI literacy in K-12 education continues to advance, existing studies have largely focused on its definition and conceptualization. There is limited empirical research on the influencing factors of AI literacy, and even less at the primary education level. To address this gap, the present study aimed to explore the relationships among key influencing factors (i.e., affective, behavioral, self-perceived cognitive, and ethical) and their associations with AI literacy acquisition. This study collected a sample of 445 primary school students (aged 8-12), and then structural equation modeling was conducted. To control for potential confounding effects and improve the robustness of the results, background variables were added to the research model. The results of this study revealed a set of positive associations, wherein affective factors are linked to behavioral factors, which in turn are linked to self-perceived cognitive and ethical factors, and to AI literacy acquisition. The result also revealed a suppression effect of the ethical factor on the link between students' self-perceptions and their actual test performance, which suggests a new research direction in AI literacy education. The study enriches our conceptual understanding of AI literacy by revealing the relational structure among its cognitive and non-cognitive components in the primary school context, extending the emergent research line to both dimensions. The findings could provide actionable insights for researchers and educators seeking to explore and integrate AI literacy instruction in primary education.
Artificial intelligence (AI) is increasingly reshaping higher education, yet the psychological mechanisms that determine when and for whom AI-assisted learning translates into academic success remain insufficiently understood, particularly within developing educational contexts. This study examined the relationship between artificial intelligence-assisted learning and academic achievement among university students, with academic self-efficacy tested as a moderating variable, drawing on Bandura's Social Cognitive Theory. A quantitative, cross-sectional survey design was employed, and data were collected from 477 undergraduate and postgraduate students enrolled in public and private universities across Punjab and Sindh, Pakistan, using a convenience sampling technique. A structured questionnaire comprising validated scales measuring artificial intelligence-assisted learning, academic self-efficacy, and academic achievement was administered via Google Forms, and data were analyzed using IBM SPSS Version 27. Pearson correlation analysis revealed a strong, statistically significant positive relationship between artificial intelligence-assisted learning and academic achievement (r = .684, p < .001). Multiple linear regression confirmed that artificial intelligence-assisted learning significantly predicted academic achievement, accounting for 49.9% of the variance (R² = .499, F(1, 475) = 470.836, p < .001). An independent samples t-test showed that students with high academic self-efficacy achieved significantly higher academic outcomes than those with low academic self-efficacy (t(475) = -14.126, p < .001). Most notably, hierarchical multiple regression analysis demonstrated that academic self-efficacy significantly moderated the relationship between artificial intelligence-assisted learning and academic achievement (ΔR² = .029, ΔF(1, 473) = 12.87, p < .001), with the interaction term emerging as a significant predictor (B = .156, β = .142, p < .001), indicating that the positive effect of artificial intelligence-assisted learning on academic achievement was stronger among students with higher academic self-efficacy. These findings extend Social Cognitive Theory into AI-mediated educational settings and suggest that the effectiveness of AI-assisted learning technologies is conditional upon students' belief in their own academic capabilities. The study offers practical implications for higher education institutions seeking to maximize the benefits of AI-based learning tools by simultaneously strengthening students' academic self-efficacy.
Shumaira Rahim, Ziauddin, Saima Zaman Khalil et al.· Aposta: Revista de Ciencias...· 0 citations
The skill-based dimensions of AI literacy were positively associated with AI dependency, whereas AI self-efficacy and academic self-efficacy were both negatively associated, suggesting a unified compensatory self-efficacy mechanism.
Artificial Intelligence (AI) has become an important part of higher education by providing students with quick access to information, personalized learning support, and assistance in completing academic tasks. The increasing use of AI tools has improved learning opportunities; however, concerns have also been raised about students becoming overly dependent on these technologies. Excessive dependence on AI may influence students' learning habits and encourage them to delay academic responsibilities. Therefore, this study examined the impact of AI dependence on students' academic procrastination in higher education. A quantitative research approach was adopted using a cross-sectional survey design. Data were collected from 100 higher education students through a structured questionnaire using a five-point Likert scale. The questionnaire consisted of demographic information, AI dependence items, and academic procrastination items. The collected data were analyzed using the Statistical Package for the Social Sciences (SPSS). Descriptive statistics, reliability analysis, and simple linear regression were used to analyze the data. The findings indicated that AI dependence had a significant positive impact on students' academic procrastination. The study highlights the importance of encouraging students to use AI responsibly while maintaining independent learning and effective time management. The findings may help educators and higher education institutions develop policies and strategies for the ethical and balanced use of AI in academic settings.
Mohammad Atif Gondal, Zartaj Nawaz, Dr. Almas Shoaib· SOCIAL PRISM· 0 citations
There is a strong positive correlation between the prevalence of academic dishonesty and the use of AI tools and the conclusion that Nigerian universities must reconsider their frameworks for academic integrity.
Solomon O. Ojedeji· Frontiers in Education· 0 citations
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