Classroom environments that support students’ adaptive learning goals are essential for fostering deeper learning and self-regulated learning. Although teacher enthusiasm has been widely associated with student motivation, the relational and environmental processes underlying its association with students’ learning goals remain insufficiently understood. Drawing on Self-Determination Theory, Broaden-and-Build Theory and Emotional Contagion Theory, this study investigated a dual-pathway model examining the associations between teacher enthusiasm and students’ learning goals through two mediating mechanisms: students’ sense of belonging and classroom disciplinary climate. The study analyzed data from 6855 fifteen-year-old students participating in the Turkish sample of the Programme for International Student Assessment (PISA). Survey-weighted structural equation modeling with Fay-BRR standard errors showed that teacher enthusiasm was directly associated with students’ learning goals and showed significant indirect associations through both sense of belonging and disciplinary climate. Both pathways showed significant indirect associations, and the combined indirect effect was consistent with a complementary dual-pathway mechanism. The study contributes to a more comprehensive understanding of the mechanisms linking teacher enthusiasm to students’ learning goals and highlights the importance of fostering supportive classroom relationships and a positive disciplinary climate within a centralized educational system.
Background: In systems where access to early childhood intervention is mediated through diagnosis, families may be required to navigate complex assessment and educational pathways before support becomes available. The aim of the present study was to investigate how parents in Greece experienced the process of seeking assessment, early support and educational provision for their young autistic children attending mainstream early childhood education settings. Methods: This qualitative study explored the experiences of 13 parents whose young autistic children were attending mainstream early childhood education settings in Greece. Data were generated through in-depth semi-structured interviews and were analyzed thematically. Results: Four interrelated themes were identified: assessment as a restrictive gateway to support, medicalized and fragmented support, conditional and uneven inclusion in early childhood education, and perceived loss and parental coping. The results showed that access to support was organized in ways that required parents to coordinate fragmented services, seek private provision, secure educational support through diagnostic and special education procedures, and manage emotional, financial and social pressures with limited formal guidance. Conclusions: Parents’ experiences of seeking assessment, early support and educational provision were shaped by systemic restrictions, service fragmentation and insufficient recognition of family needs. A more effective and equitable support system would require not only earlier and better-coordinated support but also forms of provision that respond to children’s and families’ needs without making access depend solely on diagnosis and eligibility procedures.
This study examined attitudes toward artificial intelligence (AI) among pre-service primary school teachers in Mongolia and evaluated the psychometric properties of a Mongolian adaptation of the Students’ Attitudes toward Artificial Intelligence (SATAI) scale. A cross-sectional survey yielded an analytic sample of 672 students at a public teacher education university, randomly divided by academic-year strata into exploratory (n = 334) and confirmatory (n = 338) subsamples. Factor-retention criteria disagreed, and the exploratory solution did not match the component grouping of the source adaptation. A model reproducing that structure fitted poorly and yielded an inadmissible solution. Of seven measurement models estimated from polychoric correlations by diagonally weighted least squares, a bifactor model fitted best (CFI = 0.975; TLI = 0.970), although RMSEA remained 0.086; a four-factor model fitted comparably, but its factor correlations (0.86–0.93) failed the Fornell–Larcker criterion for every pair. Omega hierarchical (0.949) and explained common variance (0.876) indicated a dominant source of common variance, while subscale-specific reliability was limited; internal consistency of the total score was high (α = 0.972). Attitudes were moderately positive (M = 3.59, SD = 0.85), with enjoyment and educational relevance endorsed more strongly than everyday application and self-rated competence. Metric and scalar invariance across academic years was supported by change-in-fit criteria, and fourth-year students scored higher than all earlier cohorts. The findings favor cautious use of the total score rather than subscale scores, and they provide a descriptive basis for designing and then evaluating structured, ethically informed AI preparation in teacher education rather than evidence that such provision is effective.
Industry 5.0 and Society 5.0 call for a shift in AI and robotics development from a primary focus on automation and technical sophistication to holistic solutions that address human needs, social contexts, stakeholder perspectives, and broader societal impacts. Preparing future engineers for this shift requires educational approaches that connect technical development with design thinking and responsible innovation. To address this need, this study introduces Innovator 5.0, an educational framework that extends the Science–Engineering–Design (SED) model by incorporating responsibility (R + SED) within a design-thinking process. The proposed framework was implemented through seven embedded workshops during a five- to six-month international research internship involving nine undergraduate students across six AI and robotics projects. An exploratory post-programme evaluation examined participants’ design-oriented practices, perceived capability development, and experiences within the research environment. Participants reported high ratings across several evaluated dimensions, particularly for challenging problem statements before solution development (M = 6.78/7), systems thinking (M = 6.67/7), creative innovation (M = 6.56/7), contextual awareness (M = 6.33/7), and iterative revision (M = 6.11/7). They also highlighted project autonomy and facilitative mentoring as valued features, while limited direct stakeholder access was identified as an important implementation constraint. Together, these findings provide practical guidance for preparing engineers to consider the purpose, societal implications, and value of AI and robotics innovations alongside their technical feasibility, supporting people-centric and responsible engineering innovation.
English for Academic Purposes (EAP) has traditionally focused on helping students enter disciplinary discourse communities yet has largely overlooked explanatory competence. This gap is particularly pronounced at cross-disciplinary universities where students must articulate specialized concepts across diverse audiences. This study investigated the role of AI-mediated dialogue in helping EAP students enhance their explanatory competence, as manifested in their metaphor refinement across three dimensions: clarity, accuracy, and audience adaptation. A three-week intervention was conducted with 41 first-year undergraduate EAP students at a cross-disciplinary university in Guangzhou, China. Students refined their metaphorical explanations of the term ‘recursion’ under two conditions: Group A (n = 26) engaged in AI-mediated dialogue via ParallelX.cn, an AI-powered educational platform, featuring branching conversation prompts and peer feedback, while Group B (n = 15) refined independently. Results showed that both groups reported positive gains in perceived explanatory competence. In comparison, however, Group A scored slightly higher than Group B in perceived clarity, accuracy, and audience adaptation, while also rating higher AI’s capacity to balance simplicity with accuracy though this difference did not reach statistical significance. Group B valued independent reflection but also expressed a desire for additional support. By contrast, Group A registered slightly higher scores in their belief that AI-mediated dialogue facilitated their deep thinking and metaphorical clarity. Qualitative analysis of AI dialogue logs further suggested iterative refinement and increased technical precision, consistent with the survey findings. These findings suggest that AI-mediated refinement can support the development of metaphor-mediated explanatory competence, with implications for EAP pedagogy and AI-enhanced language learning.
