The new tool, called the AI Literacy Scale, is a 33-item questionnaire organized around four distinct dimensions: Technical Understanding, Ethical Awareness, Practical Ability and Social Impact. Rather than treating AI literacy as a single vague skill, the scale breaks it into measurable components that map onto what students must actually know and do. Technical Understanding probes grasp of how AI systems work, Ethical Awareness covers bias, privacy and responsible use, Practical Ability addresses applying AI tools in learning and clinical contexts, and Social Impact examines the broader consequences of AI for healthcare systems and society.

What makes the study notable to measurement specialists is the theoretical scaffolding behind the item pool. The researchers drew on UNESCO’s AI Competency Framework for Students, the Technological Pedagogical Content Knowledge model and the Kirkpatrick evaluation model, supplementing these frameworks with a systematic literature review and qualitative interviews with students. Items were then refined through a Delphi process, in which expert panels iteratively rate and revise candidate questions, followed by content validation and pilot testing. This layered approach is designed to ensure that every question on the final scale has a defensible theoretical and empirical reason for being there.

To test the instrument, the team collected responses from 473 health professions students and split the sample in two. The first subsample of 241 students was used for exploratory factor analysis, a statistical technique that searches for hidden structure in the data and reveals how many independent dimensions the questionnaire is actually measuring. The second subsample of 232 students was reserved for confirmatory factor analysis, which tests whether the four-factor structure identified in the first half holds up in fresh data, alongside a Rasch rating scale analysis that examines each item individually.

The results were strong by conventional standards. The four-factor solution explained 63.676 percent of the total variance, meaning nearly two-thirds of the differences between students could be captured by the four dimensions. Internal consistency, measured by Cronbach’s alpha, reached 0.979, an exceptionally high value indicating that the items hang together coherently. Confirmatory factor analysis showed acceptable model fit, with a chi-square to degrees of freedom ratio of 2.175, incremental fit index of 0.917, Tucker-Lewis index of 0.908, comparative fit index of 0.916 and root mean square error of approximation of 0.071, all within or near accepted thresholds for a well-fitting measurement model.

The Rasch analysis added a different and arguably more demanding layer of evidence. Unlike classical factor analysis, Rasch modeling converts raw questionnaire responses onto a common interval scale, or logit scale, allowing researchers to compare the ability of each person with the difficulty of each item directly. In this study, person reliability was 0.96 and item reliability was 0.95, both excellent figures that indicate the scale produces highly reproducible measurements of students and stable difficulty estimates for items.

But the Rasch analysis also exposed a limitation that classical statistics would have missed. The person-item map, a visual plot that places students and questions along the same difficulty axis, showed that the average student scored 1.66 logits above the average item difficulty of 0.00 logits. In plain terms, the students were, on average, too skilled for the questions being asked. The scale has relatively few items hard enough to distinguish among the most capable students, a problem known as limited upper-end targeting. The authors suggest several possible explanations: the items may simply not be difficult enough, the sample may have had unusually strong AI backgrounds, students may have accumulated substantial prior AI experience outside the curriculum, or health professions programs may not yet emphasize advanced AI competencies in their teaching.

This finding carries practical consequences for how the scale should be used. The researchers conclude that the AILS is currently better suited to group-level assessment of foundational and intermediate AI literacy than to identifying students with advanced competence. For curriculum designers and education researchers, that means the questionnaire can reliably tell whether a cohort is building baseline AI knowledge and skills, but it cannot yet serve as a fine-grained diagnostic for expertise at the top of the distribution. The team recommends developing more challenging items and validating the scale in independent, multicentre samples before broader deployment.

The study also makes a methodological argument that extends well beyond AI literacy. Combining exploratory and confirmatory factor analysis with Rasch measurement gave the researchers two complementary lenses: factor analysis confirmed the internal structure of the construct, while Rasch analysis revealed how individual items function and whether the difficulty of the test matches the ability of the population. The authors argue that this combined approach illustrates the potential value of Rasch analysis for evaluating item functioning and person-item targeting in health professions education more generally, where many existing questionnaires have never been examined at the item level.

The timing of the work reflects a growing urgency in medical education. As AI reshapes healthcare, new competency requirements are falling on health professionals who must understand, critique and safely deploy these systems, yet assessment instruments tailored to this population have remained scarce and item-level measurement evidence scarcer still. A validated scale gives educators a common yardstick for evaluating whether curricula are working, for benchmarking students across institutions and for tracking progress as AI content is woven into training. The researchers caution that applying the AILS in other professional education domains would require discipline-specific adaptation and revalidation, but within health professions education the instrument now offers one of the most thoroughly tested measures available. The study was conducted in accordance with the Declaration of Helsinki, approved by the Academic Ethics Committee of Zhuhai Campus of Zunyi Medical University, and funded by the university’s Future Distinguished Faculty Development Program.

Subject of Research: Development and validation of an AI literacy questionnaire for health professions students using factor analysis and Rasch measurement

Article Title: Development and validation of the AI literacy questionnaire for health professions students using Rasch analysis

Article References: Li, Y., Ye, Q., Qin, W., & Liu, N. (2026). Development and validation of the AI literacy questionnaire for health professions students using Rasch analysis. BMC Medical Education. https://doi.org/10.1186/s12909-026-10613-z

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10613-z

Keywords: AI literacy, health professions education, Rasch analysis, questionnaire validation, psychometrics, medical education, scale development, confirmatory factor analysis, UNESCO AI Competency Framework, assessment instruments, artificial intelligence, Delphi method

The gap the scale fills is more than academic. Nursing education shapes the workforce that delivers the majority of direct patient care worldwide, and as AI-driven decision support, predictive analytics, and automated documentation become embedded in clinical practice, graduates must arrive fluent in these systems. That fluency depends on educators who can model, critique, and teach AI-related skills. Without a validated measure of faculty competency, curriculum committees have been designing AI integration strategies essentially blind, unable to benchmark where their educators stand or evaluate whether professional development programs actually work. The AI-CSNE was built to give institutions that benchmark, and its development followed the kind of methodological discipline that measurement scientists insist on but that few new instruments actually receive.

The researchers grounded the scale in the UNESCO Artificial Intelligence Competency Framework for Teachers, an international blueprint that outlines what educators across disciplines need to know about AI, supplemented by a review of the relevant literature. Content validity was established through expert evaluation and pilot testing before the instrument faced its real trial: a large cross-sectional survey of 470 nurse educators drawn from multiple nursing faculties in Egypt, conducted between mid-October 2025 and February 2026. Ethical approval came from the Scientific Research Ethics Committee of Tanta University, and participation was voluntary, anonymous, and electronically consented, with no personal identifiers collected at any point.

What makes the study methodologically interesting is how the authors handled validation. Rather than analyzing all 470 responses in a single pass, they randomly split the sample into two independent subsamples of 235 educators each. The first half was used for exploratory factor analysis, a statistical technique that lets the underlying structure of a questionnaire emerge from the data rather than being imposed by the researchers. The second, untouched half was reserved for confirmatory factor analysis, which tests whether the structure discovered in the first sample holds up in fresh data. This two-step design is considered the gold standard in scale development because it guards against overfitting, the statistical equivalent of memorizing answers rather than learning the subject.

The exploratory analysis revealed a clean three-factor structure. The first domain, Foundational AI Knowledge and Instructional Application, captures the technical and pedagogical core: understanding what AI is, how it works, and how to weave it into teaching. The second, Ethical and Human-Centered AI Use, addresses the values dimension, including responsible deployment, awareness of bias, and keeping human judgment at the center of care education. The third, AI-Supported Professional Growth, reflects educators’ use of AI for their own continuing development. Factor loadings, which indicate how strongly each item relates to its domain, ranged from 0.661 to 0.822, and together the three factors explained 56.10 percent of the total variance in responses, a respectable share for a multidimensional instrument.

Reliability, the consistency with which the scale measures what it measures, was assessed using Cronbach’s alpha. The Foundational AI Knowledge and Instructional Application domain scored 0.842, and Ethical and Human-Centered AI Use scored 0.810, both comfortably within the range conventionally regarded as satisfactory. The AI-Supported Professional Growth domain came in at 0.697, slightly below the traditional 0.70 threshold but close enough that the authors judged it acceptable for a newly developed scale, with comparable estimates appearing in the confirmatory subsample. These figures suggest the scale produces stable, internally coherent measurements across its domains, though the third subscale may benefit from refinement in future iterations.

The confirmatory factor analysis delivered the study’s most striking numbers. The three-factor model achieved a Comparative Fit Index of 0.989 and a Tucker-Lewis Index of 0.986, both far above the 0.95 benchmark typically considered evidence of excellent fit. The Root Mean Square Error of Approximation was 0.023 and the Standardized Root Mean Square Residual was 0.046, both well under the cutoffs that signal good model fit. In plain terms, the structure discovered in the first half of the sample reproduced almost perfectly in the second half, giving strong initial evidence that the AI-CSNE genuinely measures three coherent dimensions of perceived AI competency rather than statistical noise. Within the confirmatory sample, the Foundational Knowledge domain showed the highest standardized loadings, between 0.626 and 0.785, while the Ethics domain loaded somewhat lower, from 0.570 to 0.734, still within acceptable bounds.

One finding carries particular weight for anyone planning to use the instrument: the correlations between the three domains were near zero. This means the subscales are empirically distinct, tapping genuinely separate facets of competency rather than slightly different expressions of a single underlying ability. The practical consequence is that scores should be interpreted domain by domain rather than summed into one overall AI competency score. An educator might be technically knowledgeable yet ethically cautious, or professionally curious yet lacking instructional skills, and the scale’s structure is designed to expose exactly those profiles. The authors also assessed convergent, criterion, and known-groups validity, finding no significant differences across demographic subgroups, which they interpret cautiously as a sign that further validation work remains necessary.

Perhaps the most important caveat concerns what the scale does not measure. The AI-CSNE captures perceived competency, what educators believe they can do, not objectively demonstrated skill. Self-perception and actual ability can diverge, sometimes dramatically, in any domain of expertise. The authors are explicit that the instrument is therefore best suited for exploratory group-level needs assessment, research comparisons, program evaluation, and self-reflective faculty development, rather than for individual appraisal, certification, or high-stakes evaluation of any kind. Used appropriately, however, it offers nursing schools a practical starting point: a way to map where their faculty stand on AI knowledge, ethics, and professional growth, and to design training that targets real weaknesses. As AI continues its advance into clinical education, instruments like this one, built on transparent psychometric foundations and tested against independent data, will be essential for ensuring that the humans teaching the caregivers are not left behind by the machines now entering the classroom.

Subject of Research: Development and psychometric validation of a three-domain scale measuring perceived artificial intelligence competency among nurse educators

Article Title: Development and structural validation of the Artificial Intelligence Competency Scale for Nurse Educators (AI-CSNE): a three-domain measure of perceived AI competency

Article References: Mohamed, M. G., Mohamud, Z. A., & Sorour, M. S. (2026). Development and structural validation of the Artificial Intelligence Competency Scale for Nurse Educators (AI-CSNE): a three-domain measure of perceived AI competency. BMC Nursing. https://doi.org/10.1186/s12912-026-05453-7

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05453-7

Keywords: artificial intelligence, nurse educators, psychometric validation, scale development, nursing education, factor analysis, competency assessment, UNESCO AI Competency Framework, confirmatory factor analysis, faculty development, health professions education, self-perceived competency

The research, published in the Journal of New Approaches in Educational Research, compared two approaches to professional development among 57 preservice teachers enrolled in a teacher education program in Vietnam, whose average age was about 21. Participants were randomly assigned to one of two groups. The first group received a combined workshop that included both hands-on practice with generative AI tools and a mindset module on AI ethics, growth mindset, and human-centered instructional design. The second group received only the tools-focused training, covering prompt engineering and demonstrations of how generative AI can produce lesson plans, videos, infographics, songs, rubrics, and slide decks. To keep the comparison fair, the tools-only group was given access to the mindset module only after the study’s post-intervention questionnaires were completed.

The design of the combined workshop was deliberate. The mindset module, titled “Teachers’ New Mindsets for the AI Era,” came first, immersing participants in real-world problems such as algorithmic bias, AI hallucinations, misconceptions about what AI can do, and discrimination. Only after wrestling with these issues did participants move on to technical practice. The researchers grounded this sequencing in Merrill’s First Principles of Instruction, which hold that learning is most effective when it begins with authentic problems, activates prior knowledge, and then moves to guided application. By confronting ethical and pedagogical dilemmas before touching the tools, the teachers were primed to use technology reflectively rather than automatically.

To measure outcomes, the team used a mixed factorial design with pre- and post-intervention questionnaires. Anxiety was assessed with an adapted version of the Abbreviated Technology Anxiety Scale, while competence self-efficacy was measured with the Teacher AI Competence Self-efficacy (TAICS) instrument, chosen because it aligns closely with UNESCO’s AI Competency Framework for Teachers and the AI-TPACK model. TAICS captures six dimensions of teacher AI competence: AI knowledge, AI pedagogy, AI assessment, AI ethics, human-centered AI, and AI-related professional engagement. The researchers analyzed the data using linear mixed-effects models, controlling for each participant’s motivation to adopt AI and their frequency of AI use, and supplemented the quantitative results with a directed content analysis of participants’ written reflections.

The quantitative results revealed a striking asymmetry. Both groups showed an overall reduction in AI anxiety over the course of the training, but only the tools-only group’s anxiety reduction reached statistical significance. More tellingly, the tools-only group reported significant gains in self-efficacy across five of the six competence domains, including AI knowledge, pedagogy, assessment, human-centered education, and professional engagement. The mindset-inclusive group, despite receiving exactly the same technical instruction, showed a significant improvement in only one domain: human-centered AI competence, which measures the capacity to critically evaluate both the benefits and the risks of AI in education. Motivation to adopt AI emerged as a consistent and significant predictor of self-efficacy across all models, with medium effect sizes, suggesting that intrinsic interest in the technology is a central driver of teachers’ confidence.

At first glance, the tools-only group’s confidence surge might look like success. The researchers argue it may instead be a warning sign. They draw a parallel to the Dunning–Kruger effect, the well-documented tendency for people with limited expertise to overestimate their own competence. Teachers who feel highly confident about AI without having grappled with its limitations may over-rely on AI outputs, overlook data privacy and bias concerns, and underestimate the need for human oversight. A teacher who assumes AI is always accurate, for example, might skip fact-checking and inadvertently pass incorrect information on to students. The study’s authors suggest that structured ethical checkpoints, such as collaborative reviews of AI-generated content or bias audits, could help counteract this unwarranted confidence.

The qualitative data reinforced this interpretation. Reflections from the mindset-inclusive group were markedly more nuanced. Participants in that group frequently acknowledged AI’s limitations, stressed the need for human supervision, and worried about over-dependence. One wrote that educators should focus on “understanding AI limitations, using AI as a supporting tool without reliance on it,” while another cautioned peers to “avoid abusing AI or AI will replace you.” Several described the training as transformative for their professional growth, with one participant saying they felt they had “upgraded” themselves in applying AI to teaching. By contrast, the tools-only group’s reflections were enthusiastic but largely focused on practical utility and novelty—learning to write complete prompts, create teaching materials, and make lessons more lively—with little explicit consideration of ethics, risks, or pedagogical responsibility.

The researchers interpret the mindset group’s more modest confidence gains not as a failure but as a natural stage in professional learning. When educators engage in deep critical reflection on complex ethical and pedagogical issues, they often experience a temporary dip in confidence before reconstructing a more robust and realistic sense of competence. Moderate, task-relevant anxiety, the authors note, has been shown in prior research to promote deeper learning and more conscientious teaching behavior. In this framing, the heightened ethical awareness of the mindset group may have produced a kind of productive discomfort—a reflective tension that fosters pedagogical intentionality and guards against the technosolutionist assumption that every classroom problem has a technological fix. Future professional development, they suggest, should pair brief hands-on tool practice with guided reflection exercises such as journals or peer-review tasks to support both immediate skill-building and sustained growth.

The findings carry weight because they arrive amid a global policy push to define what teachers actually need to know about AI. UNESCO’s AI Competency Framework for Teachers, introduced in 2024, identifies a human-centered mindset and ethical awareness as core domains alongside technical knowledge, pedagogy, and professional learning. Yet most teacher training on AI remains predominantly tool-centric, and most empirical studies have treated professional development programs as undifferentiated wholes, making it difficult to isolate the contribution of ethics and pedagogy content. By experimentally separating these components, the new study provides rare direct evidence that mindset-oriented content does something tools training alone cannot: it cultivates ethical judgment and critical reflection, even at the cost of a short-term confidence boost.

The authors also caution that their results come with limitations. The study relied on self-report measures and written reflections rather than objective indicators such as scored lesson plans or classroom observations, the intervention lasted only a single day with immediate post-testing, and the sample consisted entirely of preservice teachers from one institution in Vietnam—a context marked by significant disparities in digital infrastructure between urban and rural regions. Longitudinal follow-up and replication across culturally and technologically diverse settings will be needed to confirm that the effects endure. Still, the central message is clear: preparing teachers for the age of generative AI is not just about technical proficiency. Teacher preparation programs and policymakers, the researchers argue, should place ethical reflection and human-centered pedagogy on equal footing with hands-on skills, so that future educators can teach with, about, and even critically against AI in socially responsible ways.

Subject of Research: The effects of mindset-oriented versus tool-focused professional development on preservice teachers' responsible use of generative AI

Article Title: Mindset matters: Fostering teachers’ responsible AI use through professional development

Article References: Huynh, L., Le, T. H., Dang, B., An, B. T., Vu, C. T., & Nguyen, A. (2025). Mindset matters: Fostering teachers’ responsible AI use through professional development. Journal of New Approaches in Educational Research, 14(1), Article 23. https://doi.org/10.1007/s44322-025-00043-y

Image Credits: AI Generated

DOI: 10.1007/s44322-025-00043-y

Keywords: generative AI, teacher professional development, AI ethics, preservice teachers, AI competence, human-centered education, self-efficacy, AI anxiety, UNESCO AI Competency Framework, critical reflection, educational technology, randomized controlled trial

The research team, led by Maria Dolores Vivas-Urias, Cristina Obispo-Diaz, and Maria Auxiliadora Ruiz-Rosillo, tracked 180 university teachers across two consecutive academic years, 2023–2024 and 2024–2025, using a pre-experimental one-group pre-test and post-test design. Rather than comparing trained teachers against an untrained control group, the study measured the same cohort twice: once before and once after the rollout of the university’s GenAI Institutional Plan 2023–2025. This design allowed the researchers to capture within-person change on three core dimensions: general awareness of generative AI, beliefs about its impact on professions, and the perceived importance of learning AI tools for integration into student training.

The headline result is striking in its magnitude. Teachers’ self-reported awareness of generative AI rose by an average of 2.24 points on a ten-point scale, climbing from 4.82 to 7.06, a difference the authors characterize as clear and statistically significant. Perceptions followed a parallel trajectory. By the second year, 80.9 percent of surveyed teachers believed generative AI would transform the way they teach, an increase of 3.76 percentage points. Belief in the technology’s impact on students’ professional futures also rose, with the overall mean score increasing from 7.97 to 8.67 out of 10. Perhaps most tellingly for institutional planners, the perceived importance of learning AI tools for integration into student training reached 8.63 in 2024–2025, a statistically significant gain of 0.56 points over the previous year.

Beneath these averages lies a structural insight that may prove the study’s most consequential contribution: awareness, perceived professional impact, and the motivation to learn form an interlocking psychological chain. Correlation analysis revealed a strong positive relationship of r = 0.718 between teachers’ perceptions of generative AI’s impact on students’ professional futures and the importance they attached to learning AI tools. A more moderate correlation of r = 0.316 linked overall AI awareness to perceived professional impact. In practical terms, teachers who understand the technology better are more convinced it will reshape their fields, and that conviction drives their appetite for training. The authors interpret this through established frameworks such as the Technology Acceptance Model, in which performance expectations, the belief that adopting a technology improves outcomes, fuel adoption.

The institutional machinery behind these shifts deserves scrutiny. UAX launched its GenAI Institutional Plan in the 2023–2024 academic year, establishing milestones for both short- and medium-term transformation. The centerpiece for faculty was a compulsory training program, the Level II Teaching Certification in Generative Artificial Intelligence, deliberately aligned with UNESCO’s AI Competency Framework for Teachers across its five dimensions: a human-centred mindset, ethics of AI, AI foundations and applications, AI for professional learning, and AI pedagogy. The certification combined asynchronous online micro-courses introducing fundamentals, ethical use, data privacy, and academic integrity with face-to-face or synchronous workshops in which teachers practiced prompt design, generated learning activities mapped to Bloom’s Taxonomy, and built assessment strategies and personalized educational materials.

Participation figures point to genuine appetite rather than mere compliance. Of 1,300 invited teachers, 65 percent engaged in at least one training activity, and 428 completed the full certification. Satisfaction ratings were high across the board: the certification scored 8.6 for course level and 8.5 for usefulness for professional development, while the shorter two-hour practical workshops scored even higher, reaching 9.1 and 9.2 on usefulness and recommendation respectively. Notably, 72 percent of participants reported they had been able to apply what they learned in their personal and professional lives, and the training’s culminating activity required each teacher to submit a concrete proposal for integrating AI into their own teaching practice. That contrasts with prior research in which educators reported strong general technology training yet still felt unprepared to work generative AI into their classrooms.

Behavioral indicators tracked alongside perceptions tell a consistent story. The share of teachers reporting no use of AI tools collapsed from 54.38 percent in 2023–2024 to 10.42 percent a year later, and use of AI for teaching purposes jumped from 16.59 percent to 43.40 percent of responses. ChatGPT remained the dominant tool, cited by 89.14 percent of users in 2024–2025, but Copilot usage rose sharply, from 7.57 percent of responses to 28.99 percent, likely aided by the institution’s provision of Microsoft Copilot through university Office 365 accounts. Nearly half of the sample, 45.73 percent, used both tools concurrently. Because 72.6 percent of participating teachers held part-time contracts and worked outside the university, the faster uptake in teaching contexts relative to professional practice suggests that institutional training, rather than external professional exposure, was the decisive accelerant.

The study is candid about its constraints. The absence of a control group means the changes cannot be causally attributed to the training program alone; broader cultural momentum around generative AI between 2023 and 2025 undoubtedly contributed. The voluntary response rate was low, at 16.6 percent of the overlapping faculty population, raising the possibility of self-selection bias in which the most motivated teachers dominated the sample. The measurement instrument itself, a three-item one-dimensional perception scale, showed acceptable reliability with a Cronbach’s alpha of 0.72 and omega of 0.76, but the authors acknowledge that the saturated factor model limits interpretive weight of the confirmatory fit indices, and the modest Kaiser–Meyer–Olkin value of 0.59 indicates restricted factorability that future work should address. Reliance solely on teacher self-report, without observational evidence of changed classroom practice, further narrows what the data can establish.

Qualitative responses flesh out where teachers want to go next. An inductive thematic analysis of open-ended answers from 67 respondents identified seven broad categories and 29 specific training needs, clustered around pedagogical integration of generative AI, personalized content creation, automated assessment, ethics and responsible use, contextualized technical development, and certified continuing education. The authors note that most of these demands map onto the existing certification program, but more technical requests, such as discipline-specific tool development, will require specialized professional development beyond the current generalist offer. Faculties of Education Sciences and Technology and Business led awareness scores in both years, while significant pre-post gains appeared across Healthcare Sciences, Medicine, Dentistry, and Veterinary Medicine, indicating that the training effect penetrated fields far removed from computer science.

For the global higher education sector, the study offers a rare data point in a debate dominated by policy documents rather than measured outcomes. Recent international analyses have catalogued how universities are issuing guidelines, redesigning assessment, and drafting ethical codes, but few have quantified how such institutional efforts move the people on the front lines. The UAX results support a systemic, ethical, and sustainable strategy in which AI literacy is treated as an ongoing faculty competency rather than a one-off compliance exercise, and in which generative AI is embedded directly into curricula to close the gap between university education and labor market demands. The authors recommend that future research measure the transfer of training into actual teaching practice, incorporate student perspectives, and complement self-report with behavioral evidence. If replicated with stronger designs, the core message, that well-structured institutional training can convert teacher anxiety about AI into informed professional momentum, could reshape how universities worldwide approach the fastest-moving educational transformation of the decade.

Subject of Research: University teachers' perceptions of generative artificial intelligence following an institutional training plan in higher education

Article Title: Analysis of teachers’ perceptions of the impact of Generative Artificial Intelligence in higher education

Article References: Vivas-Urias, M. D., Obispo-Díaz, C., & Ruiz-Rosillo, M. A. (2026). Analysis of teachers’ perceptions of the impact of Generative Artificial Intelligence in higher education. Journal of New Approaches in Educational Research, 15(1), Article 12. https://doi.org/10.1007/s44322-026-00060-5

Image Credits: AI Generated

DOI: 10.1007/s44322-026-00060-5

Keywords: generative artificial intelligence, higher education, teacher training, AI literacy, faculty perceptions, institutional policy, UNESCO AI competency framework, ChatGPT, educational innovation, pre-test post-test study, teacher professional development, Spain