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

Critical reflection is more than casual thinking about a difficult shift. Researchers define it as a structured, analytical learning process in which nurses systematically analyse clinical experiences, evaluate evidence-based practices, reconsider weaknesses in their own performance, and weigh ethical dimensions of care in complex situations. In the delirium context, this competency takes on particular urgency. Delirium is dynamic, unpredictable, and often distressing, requiring nurses to interpret biological, psychological, social, and ethical dimensions simultaneously while preserving patient dignity, minimising restrictive interventions, and staying within the bounds of clinical safety. Existing tools, such as the Critical Reflection Competency Scale developed by Shin and colleagues, the California Critical Thinking Disposition Inventory, and the Delirium Care Critical-Thinking Scale developed by Chang and colleagues in 2024, capture related but distinct constructs. None was designed to assess delirium-specific critical reflection, leaving a genuine measurement gap.

The research team, led by Fahimeh Magsoodi and Fatemeh Ghaffari of Babol University of Medical Sciences, conducted an exploratory mixed-methods study between June 2025 and March 2026, following the eight-step scale development framework of DeVellis and Thorpe. The construct was operationally defined as the ICU nurse’s ability to integrate scientific evidence, professional experience, ethical values, and theoretical knowledge through reflection-on-action and reflection-in-action, with the aim of sharpening clinical decision-making, improving care quality, and safeguarding the safety and dignity of patients with delirium. This definition deliberately spans cognitive, ethical, and anticipatory dimensions of practice rather than reducing reflection to a purely intellectual exercise.

Item generation combined deductive and inductive strategies. A systematic search of PubMed, CINAHL, Scopus, Web of Science, ProQuest, and Google Scholar identified 130 records, of which 21 studies ultimately met inclusion criteria and yielded 10 preliminary items through content analysis. To ground the instrument in real clinical behaviour, the team also conducted semi-structured interviews with 15 ICU nurses, each with at least a year of intensive care experience and direct care of at least five delirious patients in the preceding six months. Interviews lasting roughly 35 to 40 minutes explored how nurses reassessed clinical decisions, recognised gaps between evidence and practice, evaluated patient dignity and privacy, communicated with families, and interpreted early warning signs. Conventional content analysis of the transcripts produced 20 items organised into seven subcategories and three principal categories: Analytical-Evaluative Clinical Reflection, Person-Centered Ethical Reflection, and Risk-Oriented Preventive Reflection.

The qualitative work was conducted with careful attention to trustworthiness, following the criteria of Guba and Lincoln. Credibility was supported by purposive sampling with maximum variation, member checking with eight participants, and independent review of portions of the coding process. Transferability, dependability, confirmability, and authenticity were each addressed through detailed documentation, transparent tracing of items back to raw data, and secure archiving of research files. One illustrative example shows how a participant’s description of covering a patient’s body during procedures, limiting unnecessary visitors, and documenting lapses in dignity was distilled into a final item asking nurses to evaluate the extent to which respect, privacy, and physical comfort of delirious patients were maintained.

Expert review then tightened the instrument. A panel of 10 nursing faculty members with critical care and instrument-development experience evaluated 30 initial items for relevance, clarity, and linguistic transparency. Content validity ratios ranged from 0.40 to 1.00, and nine items falling below the Lawshe threshold of 0.62 were removed. Item-level content validity indices for retained items ranged from 0.80 to 1.00, with scale-level averages of 0.93 and 0.86 by the averaging and universal-agreement methods respectively. Face validity testing with 10 ICU nurses using the impact score formula eliminated two further items whose abstract concepts invited inconsistent interpretations, leaving a preliminary version that was then administered to 700 ICU nurses employed at hospitals affiliated with Babol and Mazandaran Universities of Medical Sciences. Following the Nunnally and Bernstein rule of at least 10 participants per item, the sample was randomly split into equal halves of 350 for exploratory and confirmatory factor analysis.

The statistical results were striking. Sampling adequacy was excellent, with a Kaiser-Meyer-Olkin value of 0.912 and a highly significant Bartlett’s test. Exploratory factor analysis using principal axis factoring with Promax rotation confirmed a clean three-factor structure matching the qualitative categories, with all 19 items loading between 0.633 and 0.853 and no substantial cross-loadings. The three factors explained 20.38, 19.37, and 17.59 percent of variance respectively, for a cumulative 57.34 percent. Parallel analysis supported the three-factor solution. Confirmatory factor analysis on the independent subsample, using the WLSMV estimator appropriate for ordinal data, produced excellent fit: a chi-square to degrees-of-freedom ratio of 1.14, RMSEA of 0.029, CFI of 0.986, TLI of 0.984, and SRMR of 0.041. A second-order model loading the three dimensions onto a single overarching competency yielded identical fit indices, a mathematical necessity when exactly three first-order factors are specified, so the authors caution that this equivalence confirms correlated dimensions rather than proving a hierarchical structure.

Reliability and validity evidence was equally robust. Cronbach’s alpha for the total scale was 0.876 and McDonald’s omega 0.861, with dimension-level coefficients ranging from 0.845 to 0.878. Test-retest reliability over a two-week interval with 30 participants produced an intraclass correlation coefficient of 0.85 for the total score. Average variance extracted exceeded 0.50 for all three factors and composite reliability exceeded 0.70, while the Fornell-Larcker criterion confirmed that each dimension was empirically distinct, with square roots of AVE between 0.732 and 0.746 exceeding inter-factor correlations of 0.350 to 0.420. Standard errors of measurement were low, between 0.42 and 0.51, indicating precise scores. In a methodological departure from conventional validation studies, the team also applied exploratory graph analysis, whose network estimation with the graphical LASSO algorithm and Walktrap clustering reproduced exactly the three theoretical communities, and random forest regression, which identified analytical-evaluative reflection as the strongest contributor to the internal score structure, followed by person-centered ethical and risk-oriented preventive reflection.

The authors are careful about what these findings do and do not establish. Because predictors and outcome in the random forest analysis came from the same item set, the results describe internal score structure rather than external predictive validity, and the convergent validity evidence is model-based rather than criterion-related, since no external instrument was administered alongside the CRCDCS. Limitations include the cross-sectional design, a sample drawn exclusively from teaching hospitals of two Iranian universities, reliance on self-report vulnerable to social desirability bias, and the absence of measurement invariance testing across subgroups. The three-level score interpretation bands, spanning low, moderate, and high competency across the possible 19 to 95 range, are conventional equal-range divisions rather than clinically validated cut-offs.

Even with those caveats, the study represents a significant advance for critical care nursing. The CRCDCS is the first instrument designed specifically to measure, in a multidimensional way, how intensively care nurses reflect critically on delirium care, and its development blended theory, qualitative clinical experience, classical psychometrics, network science, and machine learning in a single coherent pipeline. Pending further validation against external criteria and longitudinal outcomes, the 19-item scale offers hospital managers, educators, and policymakers a practical tool for identifying performance gaps, benchmarking professional preparedness, and evaluating training programmes aimed at strengthening reflective thinking in delirium care. Given that delirium affects a large share of ICU patients and that early recognition depends heavily on nursing vigilance, a reliable measure of the reflective competency behind that recognition could ultimately translate into better outcomes for some of the most vulnerable patients in medicine.

Subject of Research: Development and psychometric validation of a scale assessing ICU nurses' critical reflection competency in delirium care

Article Title: Development and Psychometric Evaluation of the Critical Reflection Competency in Delirium Care Scale (CRCDCS) Among Iranian ICU Nurses

Article References: Magsoodi, F., & Ghaffari, F. (2026). Development and Psychometric Evaluation of the Critical Reflection Competency in Delirium Care Scale (CRCDCS) Among Iranian ICU Nurses. Nursing Open, 13(9), Article e70818. https://doi.org/10.1002/nop2.70818

Image Credits: AI Generated

DOI: 10.1002/nop2.70818

Keywords: delirium, ICU nursing, critical reflection, scale development, psychometric validation, factor analysis, nursing competency, patient safety, reflective practice, network analysis, Iran, critical care