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, published in the journal Discover Social Science and Health, was conducted by Gideon Tetteh Agormedah, Edmond Kwesi Agormedah, and Samuel Kwarteng of the Department of Business and Social Sciences Education. The team set out to determine whether emotional intelligence, often abbreviated as EI, predicts the level of teaching anxiety experienced by preservice business teachers during their practicum, and whether gender changes the strength of that relationship. Their findings point to a clear conclusion: emotional intelligence does influence teaching anxiety, and the effect holds regardless of whether the trainee is a man or a woman.

To understand why this matters, it helps to unpack the theoretical scaffolding the researchers chose. The study is grounded in the Transactional Model of Stress and Coping, a framework originally developed by psychologists Richard Lazarus and Susan Folkman in the 1980s. The model holds that stress is not simply a property of demanding situations, nor purely a feature of individual temperament. Instead, stress emerges from a transaction between the person and the environment: first, the individual appraises whether a situation threatens their well-being, and second, they appraise what resources they have to cope with it. A practicum classroom can therefore be read by one trainee as an intolerable threat and by another as a challenging but manageable task, depending on the cognitive and emotional resources each brings to the encounter.

Emotional intelligence fits into this model as exactly such a resource. Broadly, EI refers to the capacity to perceive, understand, regulate, and use emotions, both one’s own and those of other people. A trainee teacher high in EI can recognize the physical signs of their own rising panic, reframe a hostile-looking classroom as a normal feature of adolescent behavior, and modulate their emotional response before it spirals into full anxiety. A trainee low in EI, by contrast, may experience the same classroom events as an escalating series of threats with no obvious coping route. In transactional terms, EI shapes the secondary appraisal, the assessment of coping resources, and thereby determines how much stress the practicum ultimately produces.

The researchers tested this logic with an explanatory correlational cross-sectional design. Rather than sampling a subset of the population, they used a census approach, enrolling all 107 preservice business teachers available to the study, a group preparing to teach accounting and management subjects. Data were collected through a closed-ended structured questionnaire, a format that allows respondents to rate their emotional intelligence and their teaching anxiety on fixed scales and permits statistical comparison across the whole group. Because every member of the population was included, the study avoids the sampling bias that can creep in when only volunteers or easily reachable students participate.

The analysis proceeded in two statistical stages. First, the team used linear regression to test whether emotional intelligence scores predicted teaching anxiety levels during the practicum. Regression analysis estimates the direction and strength of the relationship between a predictor variable and an outcome, allowing researchers to ask whether higher EI is systematically associated with lower anxiety. Second, and more subtly, the researchers employed the PROCESS moderation procedure developed by statistician Andrew Hayes, a widely used tool for testing whether a third variable changes the strength or direction of a relationship between two others. Here, the third variable was gender. The question was not whether men and women differ in anxiety on average, but whether the protective effect of emotional intelligence operates differently for each gender.

The results answered both questions. Emotional intelligence did significantly influence the level of teaching anxiety reported by the preservice business teachers, consistent with the transactional prediction that stronger emotional coping resources should buffer the stress of the practicum. Yet the moderation analysis returned a null result: gender did not moderate the relationship. In other words, the anxiety-reducing benefit of emotional intelligence appears to operate similarly for male and female trainees. This finding is notable because much of the literature on teaching anxiety has documented gender differences in self-reported anxiety, and one might have expected the coping mechanism to work differently across genders. The Cape Coast data suggest that the underlying psychological machinery is shared.

There are technical caveats worth keeping in view. A cross-sectional design captures a single moment in time, so it can establish that EI and anxiety are related as theory predicts, but it cannot prove that emotional intelligence causes lower anxiety; it is at least conceivable that less anxious trainees find it easier to report high emotional competence. The modest sample of 107 participants, while a complete census of its population, is small by the standards of moderation analysis, and null moderation findings can sometimes reflect limited statistical power rather than a true absence of effect. Self-report questionnaires, meanwhile, depend on respondents’ willingness and ability to describe their own emotional lives accurately. None of these limitations undermines the study’s core contribution, but they mark the boundaries within which its conclusions should be read.

The practical implications, however, reach well beyond the statistical tables. Teacher education programs in many countries devote extensive time to subject-matter knowledge and pedagogical technique, while treating the emotional demands of teaching as something trainees will simply absorb through experience. The Ghanaian researchers argue that this passive approach should change. Their central recommendation is that stakeholders in teacher education be intentional about incorporating structured emotional intelligence development into the preparation of future business teachers, so that trainees enter the practicum equipped to manage the emotional pressures it generates. Structured EI development can take many forms, from explicit instruction in emotion recognition and regulation to supervised reflection on stressful classroom episodes, role-play exercises, and peer-support structures that normalize anxiety rather than stigmatize it.

The focus on business teachers is itself significant. Preservice accounting and management teachers occupy a distinctive niche: they must translate technical, often abstract content into engaging lessons while managing classrooms that may regard their subject as dry or intimidating. That combination of content-delivery pressure and classroom-management demand makes the practicum an especially potent stressor for this group, and it makes the finding that EI buffers their anxiety particularly actionable. If emotional competence can be built before trainees ever face their first class, the practicum could shift from a trial by fire into a genuinely formative experience.

More broadly, the study adds to a growing body of evidence that the emotional dimension of teaching is not a soft afterthought but a core professional competency. The Transactional Model of Stress and Coping reminds us that the same classroom can be experienced as threat or challenge depending on the appraisals a teacher brings to it, and emotional intelligence is one of the few trainable resources that shapes those appraisals directly. The Cape Coast team’s finding that this resource works across genders simplifies the intervention problem: teacher educators do not need gender-specific emotional training tracks, only well-designed programs that raise EI for everyone. As education systems worldwide grapple with teacher attrition and burnout, evidence that a trainable psychological skill can reduce the anxiety of the very first teaching experience offers a promising, and refreshingly practical, point of intervention. The study, published open access with a permanent DOI, invites teacher educators everywhere to ask whether their programs are teaching the emotional curriculum as seriously as the academic one.

Subject of Research: The relationship between emotional intelligence and teaching practicum anxiety among preservice business teachers

Article Title: Emotional intelligence and teaching practicum anxiety in preservice business teachers across gender differences

Article References: Agormedah, G. T., Agormedah, E. K., & Kwarteng, S. (2026). Emotional intelligence and teaching practicum anxiety in preservice business teachers across gender differences. Discover Social Science and Health. https://doi.org/10.1007/s44155-026-00472-w

Image Credits: AI Generated

DOI: 10.1007/s44155-026-00472-w

Keywords: emotional intelligence, teaching anxiety, teaching practicum, preservice teachers, business education, teacher education, stress and coping, Transactional Model of Stress and Coping, gender differences, Ghana, moderation analysis, teacher training

The research team, led by Deepika Menon of the University of Nebraska-Lincoln together with colleagues at Southern Methodist University, Towson University, and Indiana University Southeast, used a mixed-methods design combining a validated self-efficacy survey with open-ended questionnaires administered at the beginning and end of science or STEM methods courses. The survey instrument, known as SETIS, measures three dimensions of confidence: personal capability, the ability to connect disciplines and engage students, and the capacity to manage materials and technology. Statistical analyses, including repeated-measures multivariate tests and paired comparisons, revealed significant gains across all three dimensions by semester’s end, with the largest improvement in teachers’ beliefs about their own abilities.

That growth matters because self-efficacy, a concept rooted in social cognitive theory, is strongly linked to whether teachers adopt innovative practices, persist through difficulties, and remain in the profession. Teachers with low confidence in STEM tend to avoid reform-based instruction and give up more easily when lessons falter. The finding that a single semester of hands-on, inquiry-based methods coursework, whether embedded in a science methods course or a dedicated STEM semester, produced medium-to-large gains suggests that teacher preparation programs can move the needle on the beliefs that ultimately shape classroom practice.

Yet the qualitative side of the study tells a more complicated story. Analyzing roughly 1,300 coded response segments, the researchers identified six broad categories of perceived challenges: teacher affect and experience, student-related concerns, content and curricular demands, pedagogical difficulties, support from colleagues and parents, and time and resources. By the end of the semester, participants reported significantly fewer overall challenges, particularly around science content knowledge and pedagogical confidence, but two concerns intensified rather than faded: worries about teaching engineering and doubts about integrating technology.

The researchers interpret this pattern through two complementary frameworks. Windschitl’s model of teacher dilemmas, spanning conceptual, pedagogical, cultural, and political tensions, helps explain how future teachers wrestle with what integrated STEM knowledge even means and how to enact it in real classrooms. Ertmer’s distinction between first-order barriers, such as limited time, materials, and institutional support, and second-order barriers rooted in personal beliefs and confidence, maps closely onto the challenges participants described. Content knowledge gaps and pedagogical uncertainty, the internal barriers, shrank after coursework. External constraints, especially time for planning and teaching STEM amid crowded elementary schedules, grew more salient as participants gained a clearer-eyed view of classroom realities.

Several findings stand out for their implications. Concerns about meeting state standards and assessment demands barely budged, with participants observing that elementary curricula leave little room for STEM when reading and mathematics dominate. One participant at a STEM integration school noted that despite the label, STEM instruction was rare. Meanwhile, structural equation modeling revealed that teachers who still felt unprepared in science or STEM content at semester’s end scored significantly lower on self-efficacy, confirming that content preparedness remains a critical lever. Intriguingly, identifying technology access as a challenge was associated with higher self-efficacy, suggesting that more confident teachers may simply be more aware of the resource constraints they will face.

The study also surfaced demographic patterns that warrant attention. Participants who had spent five or more years in college reported lower self-efficacy, possibly because their extended, full-year student teaching exposed them to a more sobering view of classroom challenges. Hispanic/Latino participants reported lower self-efficacy than their White peers, a result the authors connect to broader literature on inequities in STEM access and representation, and one they argue demands further investigation across more diverse populations.

The authors are careful about limits. Because data were self-reported at only two time points, and because most participants had not yet completed full-time student teaching, self-efficacy estimates may be somewhat inflated. The analysis also pooled data across five quite different programs, so it cannot isolate which specific course features drove the gains. Still, the breadth of the sample strengthens confidence that the overall pattern, rising efficacy alongside persistent and evolving concerns, reflects something real about how teacher preparation works.

The practical implications are pointed. The researchers recommend that preparation programs increase dedicated attention to engineering design and technology integration, precisely the areas where confidence lagged or declined. They suggest strategic school and informal-education placements so future teachers can witness successful integrated STEM instruction in action, something many mentor elementary classrooms rarely model. They also call for partnerships between universities and school administrators to address resource gaps, and for collaboration between STEM content faculty and education faculty to build better curricula. Longitudinal follow-up studies, the authors argue, should track whether the challenges these future teachers anticipate actually materialize once they enter elementary classrooms, closing the loop between preparation and practice.

Subject of Research: Preservice elementary teachers' integrated STEM teaching self-efficacy and perceived challenges

Article Title: “I learned a lot, but…”: preservice elementary teachers’ integrated STEM teaching self-efficacy and perceived challenges

Article References: Menon, D., Wieselmann, J. R., Haines, S., Asim, S., & Johnson, A. (2026). “I learned a lot, but…”: preservice elementary teachers’ integrated STEM teaching self-efficacy and perceived challenges. International Journal of STEM Education, 13(1), Article 56. https://doi.org/10.1186/s40594-026-00645-8

Image Credits: AI Generated

DOI: 10.1186/s40594-026-00645-8

Keywords: integrated STEM, self-efficacy, preservice teachers, elementary education, teacher preparation, STEM challenges, engineering education, methods coursework, mixed methods, structural equation modeling, educational psychology, K-12 STEM