This study explores women English-language teachers’ perceptions and experiences of well-being in public colleges in Nepal. Although teacher well-being has received increasing scholarly attention, research on well-being in Nepal has largely focused on school settings, with limited attention to the gendered experiences of women English language teachers in higher education. Addressing this gap, the study examines how these teachers perceive, experience, and negotiate well-being across personal, institutional, and sociocultural contexts. Guided by an interpretive research paradigm and narrative inquiry as a method, the study draws on stories collected through in-depth interviews with four women faculty members from public campuses in Nepal's inner Terai region. Participants’ narratives were interpreted through the lens of Ecological Systems Theory. The findings reveal that teacher well-being is shaped by interconnected influences at the micro-, meso-, macro-, and chronosystem levels. Supportive classroom environments, positive student interactions, and collegial relationships emerged as important sources of professional satisfaction and well-being. Likewise, family support, collaborative workplace relationships, work satisfaction, and a balanced personal and professional life enhanced teachers’ resilience and professional engagement. However, entrenched gender expectations and restrictive sociocultural norms constrained their well-being and professional growth. The study contributes to the literature on women teacher well-being and offers insights for policymakers and educational leaders seeking to create supportive environments that enhance teacher well-being and student learning outcomes.
Generative artificial intelligence (GenAI) is rapidly transforming pedagogical practices in higher education by generating explanations, feedback, simulations, learning resources, and dialogic prompts. Existing AI frameworks in education predominantly conceptualize AI through functional roles, such as tutoring, assessment-centric models, institutional governance principles, or learner literacy perspectives. However, higher education institutions often regard GenAI as a complementary tool while simultaneously framing it as a threat to academic integrity, triggering reactive responses such as prohibition, surveillance, and detection. These framings leave a theoretical gap, offering limited insight into how GenAI redefines pedagogical agency, responsibility, and knowledge work in everyday interactions among instructors, students, and institutional structures. To address this gap, the present study proposes a nested instructor-student-GenAI triadic conceptual model for higher education. The model is derived through a focused integrative interdisciplinary synthesis that brings together literature from higher education, educational technology, learning sciences, instructional design, human-computer interaction, cognitive psychology, policy, ethics, and institutional governance. The model positions GenAI as a bounded didactic-pedagogical mediator operating within a shared didactic mediation space. Higher education institutions are conceptualized as the governance layer that enables, constrains, and legitimizes triadic practice through policies, infrastructure, regulations, and accountability mechanisms, while wider stakeholders shape external expectations. The study further formulates researchable propositions and discipline-sensitive implications to support future empirical validation and responsible GenAI integration in higher education.
The introduction of theatre arts as a subject within Latvia's competence-based education reform has increased the importance of understanding how theatre arts teachers interpret the educational goals of theatre pedagogy. This study aimed to identify profiles of theatre arts teachers in Latvia based on response patterns reflecting inferred orientations toward aesthetic, social, and personal/cultural educational goals. The study employed a quantitative survey design using a questionnaire specifically developed for theatre arts educators. Data were collected in 2023 from 63 theatre arts teachers working in general education schools across Latvia. Cluster analysis was conducted using TwoStep and hierarchical procedures to examine the number and structure of clusters, followed by K-means analysis to obtain the final profile membership. The primary categorized-score analysis yielded three exploratory teacher profiles. Profile 1 comprised educators with comparatively lower endorsement across the aesthetic, social, and personal and cultural development goal dimensions. Profile 2 included teachers with consistently higher endorsement across all three dimensions, particularly aesthetic and personal and cultural development goals. Profile 3 was characterized by comparatively stronger endorsement of social goals, while aesthetic and personal and cultural development goals received more moderate endorsement. Descriptive comparisons showed clear differences among the identified response profiles in aesthetic, personal and cultural development, and social goal dimensions, while awareness of official curriculum goals showed little differentiation. The findings suggest heterogeneity among theatre arts teachers in this exploratory sample, with distinct response patterns across the educational goal dimensions examined. The study contributes to theatre pedagogy research by proposing empirically grounded teacher profiles and highlights the importance of addressing pedagogical beliefs and educational aims within teacher education and professional development in theatre arts.
IntroductionStudents now use generative artificial intelligence (GenAI) to interpret assignments, revise drafts, check evidence, and respond to feedback on assessed work. However, students’ capacity to use GenAI responsibly within assessment contexts remains insufficiently conceptualized and measured. This study developed and validated the Generative AI Assessment Literacy Scale (GAA-LS) for higher education students.MethodsA two-study scale development and validation design was used. An initial pool of 30 items was generated from literature on assessment literacy, AI-supported assessment, feedback engagement, and academic integrity. After expert review and pilot testing, Study 1 conducted item analysis and exploratory factor analysis with 486 students. Study 2 examined confirmatory factor analysis, reliability, convergent and discriminant validity, measurement invariance, criterion-related validity, known-group validity, and structural associations with 798 students.ResultsThe final 18-item scale supported a five-factor structure: assessment criteria awareness, AI-task appropriateness judgment, verification and evidence checking, ethical attribution and academic integrity, and feedback uptake and revision literacy. In Study 1, exploratory factor analysis indicated good sampling adequacy (KMO = .931) and a five-factor solution explaining 67.82% of the variance. In Study 2, the five-factor CFA model showed acceptable fit, χ2(125) = 344.72, CFI = .954, TLI = .944, RMSEA = .047, and SRMR = .042. Internal consistency was satisfactory to strong, with alpha ranging from .83 to .88 across subscales and .93 for the total scale. Evidence of convergent, discriminant, criterion-related, and known-group validity was obtained, and measurement invariance was supported across gender, discipline, and AI-use frequency groups. GAA-LS scores were positively associated with feedback engagement and academic integrity intention, with a significant indirect association through feedback engagement.DiscussionThe GAA-LS provides a psychometrically supported instrument for assessing students’ assessment-specific capacity to use GenAI responsibly. The findings contribute to applied measurement, feedback design, and academic integrity policy in AI-supported higher education.
BackgroundThe educational environment is a key determinant of medical students’ academic performance, professional development, and psychological well-being. Systematic assessment of this environment in Jordanian medical schools is lacking.ObjectiveThis study aimed to evaluate medical students’ perceptions of their learning environment at a public school of medicine in Jordan using the Dundee Ready Educational Environment Measure (DREEM), and to examine associations of these perceptions with year of study, academic performance, gender, nationality, and socioeconomic status.MethodsA cross-sectional study was conducted among 491 undergraduate medical students across all six academic years. The validated Arabic DREEM questionnaire was administered electronically and in person. Data were analyzed using SPSS 23; between-group comparisons used the Mann–Whitney U and Kruskal–Wallis tests with Dunn's post-hoc analysis. Significance was set at p < 0.05.ResultsThe median total DREEM score was 121 (IQR 99–140), indicating a generally positive educational environment. The highest subscale scores were for perceptions of teachers (SPT = 28.02 ± 7.74) and learning (SPL = 26.99 ± 8.31), while social self-perception (SSSP = 14.38 ± 4.78) was the lowest. Pre-clinical students reported significantly higher perceptions of atmosphere (SPA) than clinical students (p = 0.006). Higher GPA correlated positively with SPA (p = 0.014) and SSSP (p = 0.012). No significant differences were observed by gender, nationality, or scholarship status.ConclusionsStudents at the University of Jordan perceive their educational environment positively overall. However, some deficiencies in student mental health support and social well-being infrastructure require institutional attention, particularly for students in clinical years. Investment in mental health services, mentorship programs, and interactive teaching methodologies are recommended.
Introduction and methodsAs an emerging technology, Generative Artificial Intelligence (GAI) tools are currently subject to constant re-inventions, and they may be employed in widely different ways by users. This study sought to develop understanding pertaining to the adoption of GAI tools on the basis of a cross-sectional survey conducted within a specific institutional context. It explored the following research question: “How do staff and students in an academic setting perceive their own responses to the specific emerging technology of Large Language Models?”.ResultsThe study found that appropriate uses of GAI tools in the research setting were not straightforwardly obvious for participants. Variation was identified in both the use of GAI tools and in expertise on the part of staff, so that it would have been difficult for many staff participants to appreciate educationally appropriate uses of GAI tools and the extent to which students were manifesting those uses. While student respondents were more positive than staff about likely gains to be had from making use of these tools, and considered that facilitating conditions to support their use of GAI tools were in place, the main use that came consistently into view for students was to use GAI tools to improve language-usage and for text production.DiscussionThe findings suggest that more is needed than to frame the challenges posed by GAI tools for Higher Education across the world as a technical challenge for learning design or for training that addresses the AI literacy of students. The affordances of GAI tools play a key in determining their effective use. These affordances need to be imagined more adequately by both students and staff if this technology is to serve educational purposes effectively, with this imagination on the part of staff also needing to account for understanding of the ways that students themselves imagine the affordances of GAI tools.
BackgroundTraditional teacher education often struggles to bridge the gap between pedagogical theory and classroom practice. This study, grounded in experiential learning theory, ventures into uncharted territory, exploring the transformative potential of chatbot-driven simulations in enhancing the lesson planning prowess, pedagogical and content mastery, and teaching self-confidence of pre-service elementary educators.MethodsConducted at a public university, this quasi-experimental investigation engaged 91 primary education undergraduates divided into an intervention group (n = 44) and a no-treatment control group (n = 47). The intervention group, using a chatbot devised by the research team, immersed themselves in dynamic, AI-orchestrated scenarios, crafting lesson plans and navigating virtual teaching encounters, enriched by incisive AI-generated feedback. In contrast, the untreated group proceeded without such technological augmentation. Outcomes were estimated before and after eight simulation sessions.ResultsThe findings unveil that the chatbot-supported cohort exhibited marked improvements in overall lesson planning, with notable gains in crafting worked examples and posing probing questions, alongside significant advancements in pedagogical knowledge, and a robust boost in teaching self-efficacy.ConclusionThese revelations suggest the profound capacity of AI simulations to sculpt adept, confident educators, poised to revolutionize classroom dynamics and elevate educational outcomes. The primary implication is that AI-driven simulations can serve as a rather potent tool in teacher education programs to enhance practical teaching skills.
IntroductionWith the growing integration of generative artificial intelligence (GenAI) into higher education, research attention must shift from the frequency of tool use to how these tools are used by learners. This study examines how GenAI literacy is related to learners' patterns of GenAI use and cognitive engagement during GenAI interactions.MethodsDrawing on questionnaire data from a large, diverse international sample of academic learners (N = 848), hierarchical multiple regression analyses were conducted to examine the relationships between dimensions of GenAI literacy and actual usage behaviors. Cluster analysis was then used to identify learner profiles based on patterns of GenAI use, literacy, monitoring, and reliance.ResultsThe findings indicate a dual role for critical-ethical awareness, a dimension of GenAI literacy that is associated with both monitoring GenAI outputs and using GenAI as a cognitive shortcut. Cluster analysis identified three learner profiles: Engaged and Literate users, who integrate monitoring and reliance; Uncritical Reliant users, who show high reliance but limited monitoring; and Skeptical Minimal users, who have low GenAI use but high reliance when they do engage. These patterns point to an “illusion of cognitive independence,” in which learners report high levels of independent thinking while engaging in cognitive offloading by relying on GenAI outputs without sufficient monitoring, particularly among Uncritical Reliant users.DiscussionCollectively, these findings suggest that GenAI literacy does not necessarily reduce reliance on GenAI but instead shapes how learners combine monitoring with reliance. Conceptually, this positions GenAI literacy as a regulatory mechanism of human–AI interaction, extending self-regulated learning to contexts in which cognitive processes are distributed between learners and systems. These results highlight the importance of developing GenAI literacy as a practice of reflective and regulated use rather than focusing solely on increasing technology adoption.
IntroductionPersistent limitations of traditional lecture-based instruction in promoting deep understanding and equitable learning outcomes have prompted exploration of innovative pedagogical approaches. Technology–Enhanced Inquiry-Based Learning (TIBLEP) has emerged as a promising alternative, integrating digital tools with inquiry processes to foster active engagement and knowledge construction.MethodsThis study investigated the effectiveness of a Technology–Enhanced Inquiry-Based Learning Package (TIBLEP) on students' achievement in Natural Science and examined the influence of demographic variables. The study included two groups of students: an experimental group exposed to TIBLEP and a control group taught with traditional instructional methods. Data were collected using the Natural Science Achievement Scale (NSAS), administered as both a pre-test and a post-test over a ten-week intervention period. Participants were assigned identification numbers to track performance.ResultsDescriptive statistics (frequency counts) summarized data trends, and Analysis of Covariance (ANCOVA) assessed the effect of instructional method on post-test scores, controlling for pre-test performance. Students in the TIBLEP group achieved significantly higher post-test scores than those in the traditional instruction group, demonstrating the effectiveness of technology-enhanced inquiry-based learning. Pre-test scores significantly predicted post-test outcomes, underscoring the role of prior knowledge. However, gender, age, location, and socioeconomic background did not significantly affect achievement, and no interaction effects were observed, indicating that the intervention was equally effective across demographic groups.DiscussionThe study concludes that TIBLEP is a highly effective and equitable instructional approach for improving students' achievement in Natural Science. Its consistent impact across diverse learner characteristics underscores its potential as a scalable solution to enhance science education. The findings emphasize the importance of integrating technology with inquiry-based pedagogy to promote meaningful, inclusive, and student-centered learning experiences.
School science education has long been expected to prepare scientifically informed citizens, yet classroom practice and assessment often remain centered on conceptual understanding rather than on students’ capacity to use scientific knowledge in public life. This perspective article argues that scientific literacy remains essential but is insufficient unless connected to epistemic judgment, public reasoning, participatory knowledge production, and science-informed civic action. We propose the framework of socio-scientific civic capacity as an operational bridge between scientific literacy, socioscientific reasoning, citizen science participation, and responsible scientific citizenship. Socio-scientific civic capacity refers to students’ individual and collective ability to evaluate evidence and sources, reason under uncertainty, deliberate on socio-scientific controversies, participate in knowledge production, and connect scientific understanding with responsible responses to shared public problems, including climate change, public health, genetic technologies, artificial intelligence, energy, and biodiversity loss. We further argue that school science education should be understood as civic infrastructure: the curricular, pedagogical, and institutional arrangements that enable schools to connect science learning with community problems, citizen-generated knowledge, democratic deliberation, and collective action. The article discusses implications for curriculum, teacher education, citizen science programs, equity, assessment, and science education research.
Reliability in coded interaction research is usually reported for labels assigned to episodes that have already been identified. That practice leaves a prior question unanswered: would independent coders identify the same episodes in the first place? This brief report examines that question in naturalistic, automatically transcribed online English tutoring. Two coders independently screened the same predefined 1,004 learner turns from six de-identified adult lessons using a frozen corrective-feedback manual. This fixed-frame design evaluates agreement on turn-level episode presence; it does not estimate unrestricted segmentation or boundary placement in an unsegmented record. One coder identified 12 positive turns and the other 19; only three overlapped. Overall agreement was 97.5%, yet Cohen's kappa was .182 [95% turn-level bootstrap CI (–.011, .377)] and positive specific agreement was .194 (95% CI [.000, .385]). One shared positive had appeared as a worked example in the manual. Excluding that turn reduced kappa to .126 and positive specific agreement to .138, while leave-one-lesson-out analyses gave kappa values of .086–.240 and positive specific agreement of .095–.250. Classification agreement across the three shared positives was descriptive only (20/20, 11/20, and 18/20 matched fields); the perfect case was the worked example. The results show strong agreement on absence but weak reproducibility of the rare positive class. Corrective-feedback studies should separate identification from classification, state whether candidate units were predefined, publish the confusion matrix, and report class-specific alongside overall and chance-corrected statistics.
The increasing emphasis on convergence research to address complex societal challenges has exposed persistent gaps in how STEM education prepares students and faculty for effective interdisciplinary collaboration. At the same time, efforts to broaden participation in STEM have often relied on deficit-oriented models that overlook the cultural dimensions of academic systems. In this Perspective paper, we suggest that Multicontext Theory (MCT) provides a critical, yet underutilized, framework for addressing both challenges. The integration of MCT into student and faculty development is illustrated by using the example of the National Science Foundation funded Western Alliance to Expand Student Opportunities (WAESO) Louis Stokes Alliance for Minority Participation (LSAMP) program. WAESO sought to expand and enhance US domestic student participation in science, technology, engineering and mathematics (STEM) research, with the goal of significantly expanding the persistence and success of the US STEM population. MCT was introduced into the WAESO LSAMP program development workshops. Preliminary feedback from WAESO LSAMP participants suggested that MCT offers a shared language for understanding diverse approaches to learning, mentoring, and research, and may facilitate more inclusive and effective collaborative environments. These preliminary findings suggest that embedding MCT into STEM training can simultaneously advance broadening participation and strengthen convergence research by equipping individuals and teams to operate effectively across multiple contexts. Directions are outlined for scaling and sustaining MCT-informed approaches in the evolving STEM research and education landscape.
IntroductionFree play represents an essential context for development and learning in early childhood education. The present study aimed to develop and psychometrically validate the Free Play and Childhood Engagement Scale (FPCE-S).MethodsThe preliminary version of the FPCE-S was tested on a sample of 3,577 children aged 3–5 years (target age range: 37–60 months) from early childhood education settings in 14 Romanian counties, assessed by 156 teachers in natural free-play contexts. Statistical procedures comprised item analysis, internal consistency evaluation, exploratory and confirmatory factor analyses on distinct subsamples, construct validity and internal-structure evidence, and an exploratory comparison across age groups.ResultsResults demonstrated high internal consistency, a unidimensional factor structure, and statistically significant, though minor, age-group differences. The final version of the instrument included 14 items with adequate psychometric properties.DiscussionThe study empirically supports the utility of the FPCE-S for assessing child engagement in the context of free play in early childhood education, with relevance for educational research and practice.
There is a pressing need to support students and teachers in developing foundational computer science (CS) and artificial intelligence (AI) literacy. Emerging technologies are evolving rapidly, making it difficult to develop and scale current, evidence-informed learning experiences. This article describes an iterative approach to designing guided playful learning experiences for CS and AI with physical computing in primary and lower secondary classrooms. Classroom observations, student activities, teacher interviews, and instructional artifacts informed successive design decisions related to engagement, collaboration, conceptual understanding, and classroom implementation. The resulting design principles emphasize adaptive scaffolding, iterative reasoning across physical and computational representations, equitable pathways for participation, and implementation-informed formative evaluation. This work illustrates how guided playful learning can be translated into engaging, inclusive, and scalable CS and AI learning experiences.
Extended reality (XR) enables users to interact with and control simulated 3D elements, creating opportunities to support meaningful learning experiences, but it also introduces cognitive load due to its novel interactions. Researchers and designers have attempted to address these challenges by manipulating the user experience through the design of the virtual environment or by varying the level of interaction complexity. While traditional media benefit from established instructional frameworks, immersive media such as XR lack similarly refined design guidelines. In this perspective article, we argue that Mayer’s multimedia learning principles cannot be directly transferred to XR learning environments without reinterpretation, as XR introduces distinctive cognitive and interaction demands, including embodiment, spatial interaction, presence, agency, and environmental complexity. From this perspective, we organize XR design considerations around cognitive load processes and discuss how these choices may either support or hinder learning. We then propose key considerations for designing effective XR-based learning experiences, including content representation (e.g., 2D and 3D elements, color, positioning), virtual environment settings (e.g., background elements, haptic feedback, sound, voice interactions), and the structure of learning materials (e.g., pre-training materials, interactions and tasks, assessment).
Teacher supervision frequently operates at the intersection of accountability expectations and professional development goals. However, supervision may also serve as a relational leadership practice that supports teacher wellbeing and professional growth. Despite increasing scholarly attention to teacher wellbeing, limited research has explored how everyday supervisory interactions relate to teachers’ psychological and professional experiences. This qualitative study examines supervision as a relational leadership practice in UAE schools, focusing on how supervisory interactions may support or constrain teachers’ wellbeing and professional experiences. Drawing on the Job Demands–Resources (JD-R) model and Self-Determination Theory (SDT), the study conceptualizes supervision as a relational practice that may function simultaneously as a source of support, accountability, and psychological demand while relating to teachers’ experiences of autonomy, competence, and relatedness. Data were generated through semi-structured interviews with teachers and school leaders from schools in the United Arab Emirates. Analysis was primarily inductive but theoretically sensitized, followed by the application of JD-R and SDT as interpretive frameworks to examine how supervisory practices function as organizational and psychological resources or demands. The findings indicate that teachers experienced supervision positively when it was characterized by trust, dialogue, credible feedback, recognition, and meaningful professional guidance, while supervision associated with excessive monitoring, documentation demands, unclear expectations, and limited teacher voice was linked to stress, pressure, and diminished psychological safety. Teachers also described supervision as more developmental when feedback was relational, specific, and supportive rather than compliance-oriented and evaluative. The study highlights the importance of balancing accountability expectations with relationally supportive supervisory practices and offers implications for educational leadership, supervision policy, and teacher wellbeing initiatives within high-accountability school contexts.
This study determined the impact of a personalized educational platform based on machine learning (ML) on the cognitive learning of secondary education students at a public institution in Piura, Peru. The research followed an applied, quantitative approach with a quasi-experimental design. A sample of 56 fourth-grade students was distributed into a control group (n = 26, traditional teaching) and an experimental group (n = 30, platform-assisted teaching), using two pre-existing classrooms randomly assigned to each condition. Cognitive learning was assessed across four dimensions (attention, memory, reasoning, and comprehension) with pretest and posttest exams administered at the start and end of the same two-hour session per topic, over three consecutive weeks. The platform combined an unsupervised clustering model for cognitive profiling with a supervised classifier that predicted, in real time, the optimal content level for each student. Analyses were based on improvement scores (posttest - pretest); intergroup comparisons used the Mann–Whitney U test with rank-biserial effect sizes, and the relationship between platform metrics and the posttest score was examined through Spearman correlations. Results showed significant differences favoring the experimental group across the three topics (p < .001), with increasing effect sizes (r = 0.527, 0.610, 0.738), confirmed through a sensitivity analysis that controlled for the pretest. The experimental group achieved an average improvement of 8.29 points out of 20, versus 4.90 for the control group. Requested adaptations showed a moderate association on average with the posttest score (ρ = 0.485, p < .001), strengthening over the course of the intervention (up to ρ = 0.588), while the predictive weight of the initial micro-diagnostic declined progressively. The platform was associated with a significant, progressive, and dimension-differentiated improvement, although the single-classroom-per-condition design and the within-session measurement warrant interpretive caution. This work contributes to inclusive education in resource-limited contexts and aligns with Sustainable Development Goals 4 and 10.
IntroductionAlthough metacognitive scaffolding enhances self-regulated learning (SRL), its continuous impact on learners' internal cognitive states remains difficult to assess using traditional outcome-based measures.MethodsTo investigate these latent regulatory dynamics during a computer-supported learning task, we applied non-linear pupillary phase-space reconstruction to track the continuous cognitive engagement of university students (N = 82).ResultsLinear Mixed-Model analyses indicated a distinct divergence in pupillary dynamics: rather than uniformly reducing cognitive load, metacognitive prompts were associated with sustained physiological mobilization. Specifically, the scaffolded cohort demonstrated significantly elevated dynamic intensity (p < 0.001) and greater structural trajectory curvature (p < 0.001). Notably, these pupillary dynamics showed no significant linear association with final post-test scores (all p > 0.4).DiscussionWe interpret these pupillary dynamics as candidate physiological correlates of sustained regulatory micro-adjustment; however, because pupillometry is sensitive to arousal, effort, attention, and task-related factors, they cannot be equated with metacognitive monitoring directly. Immediate performance metrics alone may therefore fail to capture the physiological dynamics that accompany digital learning, although no definitive process–outcome dissociation can be inferred from the present data. By introducing non-linear pupillary dynamics as candidate continuous correlates of regulatory effort, this analytical framework provides a method for evaluating instructional designs and informing the development of real-time adaptive tutoring systems.
IntroductionThe increasing number of children with foreign backgrounds in Japan has intensified the need for effective support for school adaptation. Chinese children who require Japanese-language instruction may experience difficulties in academic learning, interpersonal relationships, and participation in school life. However, relatively few studies have examined how these dimensions are interrelated in Chinese children's own accounts of their school experiences in Japan.MethodsSemi-structured interviews were conducted with six Chinese elementary school children attending public or private schools in Japan. The interview data were analyzed using KH Coder for word-frequency, co-occurrence network, hierarchical cluster, and correspondence analyses. Self-determination theory and self-efficacy theory were used as interpretive frameworks. Japanese-language proficiency was not formally assessed using standardized measures; instead, participants’ language-learning histories and descriptions of classroom comprehension and language-related difficulties were used as contextual information.ResultsFrequently occurring words reflected core domains such as lessons, Japanese language, friends, teachers, study, and school life. The co-occurrence network and hierarchical cluster analyses identified recurring patterns across domains including social relationships and identity, daily life and language adaptation, school subjects and learning difficulties, classroom participation, tests and outcomes, learning experiences and motivation, inquiry and problem solving, and school engagement. The correspondence analysis further indicated participant-specific patterns of word use. Participants A and B were positioned closer to words associated with friendships, Japanese-language learning, and school life; Participants C and D were positioned closer to words associated with teachers, effort, and school subjects; and Participants E and F were positioned closer to words associated with grades, tests, and learning-related attitudes.DiscussionSchool adaptation among Chinese elementary school children in Japan should be understood as a multidimensional process involving language-related functioning, academic participation, interpersonal relationships, motivation, and responses to challenges. The findings suggest that effective educational support should integrate Japanese-language instruction with academic guidance, opportunities for supportive peer and teacher relationships, and assistance that encourages autonomy, competence, and relatedness. Collaboration among schools, families, and local communities may also help address both academic and socio-emotional needs.
Artificial intelligence (AI) and gamification are increasingly combined in language learning, and learners' acceptance of such tools is typically measured with multidimensional questionnaires whose dimensional structure is rarely tested. This study asked whether the commonly assumed subdimensions of acceptance of AI-gamified adaptive learning are empirically separable in English-as-a-foreign-language (EFL) learners, or whether acceptance is better understood as a general evaluative orientation. Using a 13-item instrument, survey data from 401 EFL students at a Kazakhstani university were analyzed with reliability analysis, exploratory factor analysis with parallel analysis, competing confirmatory models (one-factor, four-factor, second-order, and bifactor), the heterotrait–monotrait (HTMT) ratio, bifactor dimensionality indices, ordinal (WLSMV) sensitivity analysis, and independent confirmation on a held-out subsample. Parallel analysis (exploratory half) retained a single factor. Although a strict one-factor model fitted poorly, the four-factor, second-order, and bifactor models fitted better; however, the subdimensions lacked discriminant validity (HTMT up to .94; latent correlations up to .95 under WLSMV). A bifactor analysis showed a dominant general factor (ECV = .82; ωh = .93; PUC = .81), and the bifactor model was not identifiable under WLSMV, consistent with very weak specific factors (although non-identification can also reflect model instability). Formal comparisons (χ2 difference tests and log-likelihood-based information criteria) favored the bifactor representation, and an S-1 bifactor diagnostic implicated the effectiveness-specific factor as a major contributor to the WLSMV non-identification. A Harman test (first factor = 66.5%), together with the uniformly positive, single-occasion, self-report design, suggests that common-method variance may also contribute to this generality. The evidence supports scoring this 13-item measure as a single, possibly method-influenced general factor and warns against interpreting fine-grained subscales, with perceived gamification the only partly distinguishable facet. These findings, obtained from a predominantly female sample of prospective English teachers at one Kazakhstani university, provide structural validity evidence only; nomological and external-criterion validity were not examined and remain the necessary next step.
