Schools are increasingly shaped by societal change and growing cultural diversity, calling for refined approaches to understanding the acculturation of minoritized students. This study examined acculturation profiles among minoritized primary students (n = 736) in Switzerland, applying a tridimensional framework that incorporates a multicultural orientation, beyond heritage and majority orientation. Using a three-stage latent profile analysis, four distinct acculturation profiles emerged: Multiculturalists (33.3%), Heritage-oriented Multiculturalists (29.9%), Majority-oriented Multiculturalists (29.2%), and a smaller group of Assimilationists (7.6%). The number of parents born abroad, religious practice, Swiss citizenship, and socioeconomic status predicted students’ profile membership. Comparisons of academic self-concept showed that only Majority-oriented Multiculturalists differed from the other profiles. Our findings suggest that a high multicultural orientation may support students’ academic self-concept mainly when paired exclusively with a strong majority orientation. In contrast, our results demonstrate that a strong heritage orientation may be less favorably related to academic self-concept, even when paired with a high multicultural orientation. However, given the cross-sectional design, the results call for further longitudinal research. Nonetheless, the results of this study indicate a necessity for more differentiated acculturation frameworks that consider the multidimensionality of acculturation in contemporary culturally diverse classrooms.

Generative Artificial Intelligence (GenAI) has the potential to bring substantial benefits to language education, making it essential to examine how teachers engage with these technologies in practice. This exploratory qualitative case study draws on semi-structured interviews with four in-service upper-secondary English teachers in Norway to examine the factors shaping their engagement with GenAI. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study examined factors shaping teachers’ engagement with GenAI, including performance expectancy, effort expectancy, social influence, and facilitating conditions. Thematic analysis revealed a pattern of selective, context-sensitive use rather than straightforward adoption. While teachers recognised the potential of GenAI to support planning, idea generation, and formative feedback, their engagement was constrained by concerns about assessment validity, academic integrity, privacy, and institutional guidance. The findings suggest that teachers’ use of GenAI is shaped not only by perceptions of usefulness and ease of use but also by trust, assessment considerations, and the availability of clear policy frameworks. By using UTAUT as a qualitative analytical lens, this study contributes to research on technology acceptance and teacher agency by showing how teachers negotiate the use of GenAI in ways that reshape assessment practices and professional roles. The findings point to the need for clear institutional guidance, AI-resilient assessment practices, and targeted teacher education that supports ethical, pedagogically grounded use of GenAI.

This qualitative study explores the induction experiences of Palestinian Arab novice teachers in Jewish-majority schools in Israel during a period of intense national tension (2023–2025). Amid ongoing teacher shortages in the Jewish sector and a surplus of qualified teachers in the Arab sector, Boundary-Crossing Teaching (BCT) has become a notable phenomenon. Using semi-structured interviews and reflective journals of 23 beginning teachers and eight mentors, the study investigates how minority educators navigate cultural and political divides in a conflict-affected society. The findings reveal that during periods of heightened tension, teachers’ professional identity is often overshadowed by ethnic suspicion, leading to a “dual burden” of professional and national representation. The data shows that teachers navigate national ceremonies through “strategic ambiguity”—performing outward compliance (e.g., standing for the siren) while maintaining internal identity boundaries. Furthermore, the study identifies a paradox in language dynamics: while Palestinian Arabic is often “securitized” and viewed with suspicion in staffrooms, teachers successfully leverage their linguistic background as “intercultural capital” to build empathy with students. The research finds that shared-identity mentors provide an essential “third space” for processing experiences of racism that are otherwise silenced within the school hierarchy. These empirical results demonstrate that teacher retention in conflict zones requires active institutional protection to prevent professional status from collapsing into national categorization.

This study reports the design, implementation, and descriptive evaluation of “Codici del Futuro”, a STEM-oriented education pathway developed by the Italian National Research Council (CNR) to promote students’ interest in science and awareness of research-related careers and addressed to local high school students. The programme involved 167 high school students organised in 10 groups and combined an orientation session with hands-on workshops delivered in CNR research facilities (chemistry, biotechnology, artificial intelligence, eXdended Reality/Augmented Reality (XR/AR), and game design). The chemistry workshop will be described as a case study. The study addresses two research questions: (RQ1) What group-level outcomes (participation, engagement, interest, behaviour) are observed across the multidisciplinary pathway? (RQ2) What post-activity satisfaction and short-term knowledge outcomes are observed in the chemistry workshop as an embedded case study? Group-level outcomes were assessed through a facilitator-based evaluation grid, using four single-item indicators rated on a 10-point scale and including field notes. The chemistry case study included an anonymous post-activity questionnaire (satisfaction, prior experience, and an eight-item knowledge test). Results documented high levels of engagement, interest, and appropriate behaviour across groups, whereas participation showed greater variability. In the chemistry case study, students reported high satisfaction and moderate post-activity knowledge scores, with differences across knowledge domains. Overall, findings provide descriptive evidence on student responses within a research-centre-based, multi-workshop STEM pathway.

The integration of artificial intelligence (AI) into online education has transformed the digital learning space, offering new ways to enhance learner satisfaction and engagement. This systematic literature review, covering a five-year span from 2020 to 2025, explores how AI technologies, such as chatbots, intelligent tutoring systems (ITS), sentiment analysis, gaze tracking and predictive analytics, support learner engagement across cognitive, emotional, behavioral, and social dimensions. Drawing from 30 peer-reviewed studies, the current review addresses three central research questions: (1) What aspects of AI positively influence learner satisfaction and engagement in online courses within higher education institutions; (2) What potential challenges from using these technologies may arise; and (3) What research approaches are most commonly used to assess AI’s impact in such learning contexts? The findings highlight that adaptive learning, real-time feedback, and emotion-aware systems contribute positively to personalized learning and motivation. However, concerns persist around data privacy, algorithmic bias, over-reliance on automation, and system usability. Experimental and quasi-experimental designs, as well as machine learning, mixed methods, and survey-based approaches are found to dominate in reviewed studies. Based on these insights, this work offers a foundation for future AI-enhanced learning management systems designed primarily to enhance learner engagement across cognitive, emotional, behavioral, and social domains.

The purpose of this study is to explore how adolescents in self-contained classrooms or schools for students with autism or ADHD, with no prior involvement in criminality, perceive and interpret different forms of early offending behavior through fictional case stories. The study specifically aims to examine their ability to discern what constitutes offending behavior, based on the double empathy problem. In total, 13 participants currently receiving secondary-level education (grades 10–12, aged 16–20 years) in self-contained classes at schools for adolescents with autism or ADHD participated. No student had cognitive disabilities or had been involved in any criminal act or criminal justice issues. The students were individually given three fictional written cases of offending behavior (theft, physical assault, and sexual assault). Audio-recorded stimulated recall interviews were obtained while the students solved tasks in relation to the cases, and these were analyzed to capture whether and what aspects of early offending were discerned. Overall, the results indicated limited awareness and enhanced social vulnerability, risking unwitting engagement in early offending behavior. Adapting social science education to students’ special educational needs to understand social interactions might be used to prevent and enhance their awareness of early offending behavior.

The challenge of designing generative AI (GenAI) tutors that are both pedagogically sound and effective for guided inquiry remains significant. This paper introduces and evaluates a replicable design framework-termed a Pedagogy-to-Prompt Engineering Framework-that systematically translates established pedagogical models into structured AI interactions. We engineered a 5E-structured GenAI coach by integrating the 5E Learning Cycle as the instructional architecture and the 5S Prompting Principles to govern the AI’s dialogue. The coach was evaluated in a middle school chemistry context (N = 60) focusing on procedural skill acquisition for balancing chemical equations. A quasi-experimental study showed the GenAI group achieved significantly higher learning gains than a control group receiving traditional instruction (t(58) = 2.646, p = 0.011, Cohen’s d = 0.68). Crucially, a Johnson-Neyman analysis revealed that the coach was particularly beneficial for students with lower prior knowledge (pre-test scores < 39.39), effectively narrowing the achievement gap. Furthermore, Lag Sequential Analysis of the interaction logs confirmed that the student-AI dialogue successfully adhered to the intended 5E pedagogical sequence (e.g., Engage → Explore transition, z = 11.157). This study demonstrates that the proposed framework is a viable method for creating effective, scalable AI-driven learning environments. Beyond chemistry, this approach is readily adaptable to other STEM disciplines requiring guided inquiry, such as physics and mathematics. By validating a low-code, pedagogy-first methodology, this work offers a scalable blueprint for instructional designers to bridge the gap between generative AI capabilities and rigorous educational standards.

Although child-centred approaches are increasingly adopted in disaster risk reduction (DRR) education, cultural heritage remains largely absent from pedagogical models addressing risk, agency, and community belonging. This study explores how heritage-based experiential learning can support young children’s cognitive, emotional, and civic development in DRR contexts. A qualitative intrinsic case study was conducted with 18 pupils (ages 8–9) in a primary school in Ravenna, Italy, through a four-session intervention grounded in Learning-by-Doing and Play-Based Learning. Activities included risk identification games, tableaux vivants, archaeological puzzles, and a simulated triage of heritage objects. Data from structured observations, teacher notes, children’s artefacts, and feedback discussions were analysed through qualitative content analysis. Findings indicate that experiential, embodied, and collaborative tasks facilitated children’s understanding of risk, promoted metacognitive reflection, and nurtured an emerging sense of responsibility toward cultural heritage. Heritage provided a meaningful learning context that supported emotional engagement, sense-making, and early civic agency. The study highlights the pedagogical value of integrating cultural heritage into DRR education and suggests avenues for extending holistic, community-relevant learning in early childhood.

We examined gender and racial parity in agricultural education enrollment and engagement among 230,228 students representing 1245 secondary-level agricultural education programs in nine U.S. states. Guided by the Representative Bureaucracy Theory, we evaluated agricultural education demographic enrollment and compared it to the demographics of the school that each program served to assess gender and racial representation differences. We also analyzed engagement patterns in Supervised Agricultural Experiences (SAE) and FFA activities by comparing the agricultural education enrollment to program engagement data. The results indicated that minority students were enrolled in school-based agricultural education (SBAE) programs at rates slightly below their representation in the school population. However, engagement data suggested that females were enrolled in agricultural education at expected rates but participated in SAE and FFA at higher-than-expected rates. These findings highlighted modest discrepancies in demographic representation and engagement, though the differences were generally minor and reflect progress toward inclusivity. The study concludes that, while increasing diversity and parity remain goals, agricultural education programs have made measurable strides in engaging broader student audiences. Recommendations include continued efforts to recruit and support underrepresented groups in SBAE, with a focus on both enrollment and meaningful participation.

The effective transfer of research-based instructional innovations into classroom practice requires assessment tools that allow teachers to critically examine the quality and applicability of STEM learning designs. This study employs the RubeSTEM rubric to analyze a STEM teaching–learning sequence on fire ecology, focusing on how preservice and in-service teachers evaluate disciplinary integration, structural coherence, and classroom feasibility. By involving teachers at different stages of professional experience, the study examines patterns in teachers’ evaluative judgments and explores differences according to teaching experience and specialization. The findings indicate a high level of perceived disciplinary integration, particularly in the dimensions of argumentation and authenticity, highlighting strengths in the design of the sequence. At the same time, limitations were identified in relation to engineering design and the evaluation of the learning process, pointing to areas for improvement in STEM instructional planning. Statistically significant differences in evaluations were found according to teaching experience, especially in the assessment of the theoretical dimension, with higher ratings from teachers with intermediate experience. Overall, the results illustrate how a structured evaluation rubric can be used to examine the quality of integrated STEM teaching–learning sequences from a teacher perspective, providing empirical evidence on design coherence, disciplinary integration, and classroom applicability.