Frontiers in Education: Digital Learning Innovations

The rapid integration of Artificial Intelligence (AI) into higher education underscores the urgent need to assess and foster robust AI literacy among future teachers. This cross-disciplinary competence is a critical, ethical, and equity-oriented foundation that empowers educators to personalize instruction and promote inclusive learning. However, current literature exhibits a methodological gap due to the scarcity of validated Spanish psychometric instruments. Furthermore, previous studies often evaluate pre-service teachers homogeneously, overlooking specialization-specific asymmetries and hindering the development of tailored, realistic, and effective curricula. To address this limitation, this study validates the Spanish adaptation of the Meta AI Literacy Scale (MAILS) to multidimensionally assess AI literacy. The 34-item instrument—measuring application, understanding, detection, ethics, creation, self-efficacy, and self-competence—was administered to a sample of 471 students in the Faculty of Education at the University of Salamanca across Sport and Exercise Sciences (48.6%), Primary Education (34.4%), Early Childhood Education (11.9%), and minor academic tracks (5.1%), providing high ecological validity. Psychometric validation demonstrated excellent internal consistency (overall Cronbach's alpha = .903). Overall, students exhibited strengths in practical application and self-efficacy, but only moderate levels of theoretical understanding and detection. ANOVA tests revealed significant competency disparities across seven of the nine assessed dimensions. Sport Sciences students excelled in creation (M = 7.62) and application (M = 17.22) due to their frequent use of performance-monitoring technologies. Conversely, Early Childhood Education students exhibited high digital vulnerability, scoring lowest in creation (M = 5.41), application (M = 14.38), and problem-solving self-efficacy (M = 6.34). Primary Education students demonstrated the highest resistance to algorithmic persuasion (M = 9.65). Transversely, the ethics dimension showed no significant differences (F = 0.053, p = .948), reflecting a shared, highly developed deontological awareness. In conclusion, these findings highlight the need to abandon one-size-fits-all digital training in higher education. Designing tailored curricular pathways that address the specific epistemological, technical, and affective demands of each degree is essential. Only through targeted preparation can AI literacy drive equity and social justice, empowering future educators to implement AI, open educational resources, and Universal Designfor Learning frameworks safely, ethically, and inclusively.

Background/objectivesArtificial intelligence (AI) Chatbots have benefited students in many ways but several issues have arisen with regards to ethical aspects and students’ critical thinking skills. This study aims to provide insights to the prevalence and frequency of AI Chatbot usage among Faculty of Medicine, Universiti Kebangsaan Malaysia (FPER UKM) undergraduate students, and explore the behavioral and psychological factors associated with AI Chatbot use and dependency.MethodsThis study employed a cross-sectional design. An online survey involving 300 undergraduate students from FPER UKM consisting of medical, nursing and emergency medicine courses was conducted using questionnaires that were adapted and screened from the Bergen Facebook Addiction Scale (BFAS) and other literature. All items were rated on a five-point Likert scale with an inclusion of four open-ended questions at the end of the questionnaire for participants to give their opinion on AI chatbot usage. Two main factors contributing to AI dependency, which are academic stress and academic self-efficacy, were measured using the same scale. Data analysis of the collected responses was then done via bivariable correlation and multivariable regression using SPSS version 29.ResultsOur study included 300 students in FPER UKM which mainly consisted of Malaysian students with only 4 of them being non-Malaysian. It was found that the prevalence of AI Chatbot usage among all undergraduate courses to be 100%. Most of the respondents used AI tools for one to three hours per week with the main purpose of studying or revision. The factors most strongly associated with AI dependency score was weekly usage of more than four hours (p < 0.001, β = 0.301), followed by academic stress (p = 0.001, β = 0.188). The final multiple regression model was significantly associated with AI Chatbot dependency (p < 0.001) with R2 of 0.182.ConclusionAll undergraduate students have used AI Chatbot previously with its main benefit on studying and revision. AI Chatbot dependency is significantly associated with duration of AI Chatbot usage and level of academic stress. Further studies are warranted to classify dependency on AI Chatbot and explore other potential correlates related to over-reliance on AI Chatbot.