Search Results (1,014)

Modeling historically situated gender ideology remains challenging for language models, as contemporary embeddings struggle to reflect temporally specific semantic structures beyond surface lexical patterns. Although large language models exhibit extensive general-purpose performance, their direct use with history-specific semantic analysis is limited by the distributional mismatch between contemporary training data and historical linguistic patterns. These constraints encourage the distillation of temporally based semantic knowledge into small student architectures. To solve this issue, we propose Temporally Informed Distillation of Embedding Semantics (TIDES), which integrates continued pretraining on temporally specific corpora with feature-level distillation from large embedding teachers. We evaluate TIDES across teacher architectures with distinct pretraining objectives. While continued pretraining provides lexical and syntactic adaptation, our results show that improvements in ideological modeling cannot be attributed to additional training exposure alone. Rather, teacher–student structural alignment is also critical to transfer effectiveness. Contrastive, encoder-aligned teachers yield substantially more stable preservation of fine-grained, historically situated semantic distinctions. These findings suggest that temporal ideology transfer is representation-dependent: ideological meaning can be shaped by the geometry and training objectives of embedding spaces. By introducing TIDES and providing evidence that architectural compatibility can influence ideological inheritance, this study advances a representation-centered account of modeling ideology in temporally grounded semantic research. Full article

This article examines how kindness can be understood, expressed and enacted through intercultural citizenship education in higher education, with particular attention to equity, diversity and inclusion (EDI). Situated within a theoretical framework that brings together intercultural citizenship and EDI, the study argues that these fields are mutually reinforcing and that their integration is enriched by foregrounding kindness. Empirically, the article reports on a qualitative multiple case study conducted in 2023, involving university students from Argentina and the United Kingdom who collaboratively designed English language teaching materials focused on kindness. Data consisted of student-generated textual and artistic artefacts, including lesson plans, teachers’ notes, drawings, comics and other teaching materials, which were analysed using a multimodal approach. Across cases, kindness functioned as a relational disposition, ethical compass, emotional anchor and intentional action, serving as a pedagogical response to issues of gender inequality, mental health and disability inclusion. The study argues that a structured grammar of intercultural kindness offers a vocabulary that makes visible the relational, ethical, emotional and action-oriented dimensions through which kindness shapes the pedagogical enactment of intercultural citizenship and EDI. This approach demonstrates that kindness can be taught; however, its transformative potential depends on a deliberate political orientation. Full article

Nowadays, Python is very popular as the first programming language for novices, including high school students, to learn due to its short code features with rich libraries. Thus, it is important to provide a learning environment supporting studies starting from the fundamentals, since students have no knowledge on how a program runs on a computer. Previously, we have developed a web-based programming learning assistant system (PLAS) to allow the self-study of major programming languages, including Python, by university students. It offers several types of exercise problems that have different learning goals and levels for step-by-step study. Any student answer is automatically marked at the answer interface for quick feedback. However, PLAS has not implemented functions to assist the learning needs of high school-level students. In this paper, we propose a novice-friendly answer interface for a Python programming learning assistant system (PyPLAS) that introduces a code behavior visualization and an AI assistant with learning logs. The visualization allows learners to observe the changes in variable states and the control flow. The assistant provides multi-level hints during learning and reflective feedback after it by analyzing the logs based on engagement, reasoning strategies, learning pace, and tool usage. For evaluation, we implemented the proposed interface using Python Flask for the web platform and Ollama as a locally deployed AI model. A pilot application was conducted with high school students solving introductory Python exercises in PyPLAS. The results showed high task completion, positive questionnaire responses toward embedded visualization and interface usability, and teacher-observed usefulness of the four-dimensional learning analytics for interpreting learner behaviors. These findings provide preliminary evidence for the feasibility and practical value of the proposed interface, while larger controlled studies are required to validate its instructional effectiveness. Full article

Computational thinking (CT) is increasingly recognized as essential in education, yet teacher preparation programs struggle to develop both computational proficiency and pedagogical readiness in pre-service teachers (PSTs). This study examines an AI-mediated, game-making course grounded in the emerging “vibe coding” paradigm, where 24 novice PSTs iteratively constructed programs through natural language prompting. Adopting a mixed-methods design, the study drew on pre- and post-course attitude questionnaires, reflective accounts of prompting strategies, and open-ended responses. Results indicate that participants substantively engaged with core CT practices, particularly debugging, iterative refinement, and problem decomposition. Nonetheless, this downward recalibration in self-reported coding and teaching confidence represents a productive adjustment rather than a failure. Conversely, attitudes toward game-making improved significantly, with a statistically significant medium effect size for perceived instructional value (d = 0.51), the largest practical effect observed across dimensions. Most participants intended to integrate CT into future teaching. These findings suggest that prompt-driven learning environments support meaningful engagement with computational processes when carefully scaffolded, but do not inherently ensure pedagogical readiness, particularly for higher-order CT practices such as abstraction and pattern recognition. Unlike prior research that has examined game-making processes or PST attitudes toward CT in isolation, this study empirically integrates all three within a single scaffolded instructional design using vibe coding. This integration enables a process-level account of how CT is enacted—and how it develops—when code generation is partially delegated to AI systems. Beyond documenting attitude shifts, the study introduces an analytical rubric for identifying CT engagement in AI-mediated prompting and derives evidence-based design principles that specify the pedagogical conditions under which vibe coding supports, rather than bypasses, computational reasoning. Full article

Student teachers can seek help from large language models when writing their academic texts during their initial teacher training, and teacher educators can use large language models to evaluate the submitted academic texts. However, existing research presents inconsistent evidence regarding whether large language models assess academic work in ways comparable to human evaluators. To our knowledge, few studies have examined evaluations made by both student teachers and teacher educators alongside those generated by large language models. This study addresses two research questions concerning how academic texts are evaluated by student teachers, teacher educators, and large language models. First, we found that the two large language models showed agreement with each other but did not consistently align with the evaluations provided by either the student teachers or the teacher educator. Second, the large language models produced substantially longer evaluation texts that closely followed the structure of the assessment criteria but struggled with evaluating the discussion sections. Although the large language models offered practical suggestions for improving academic texts, their feedback did not emphasize the same aspects highlighted by the teacher educator. Implications for practical use of generative AI-tools and needs for further research are discussed. Full article

Research on language teacher emotional labour has expanded substantially, and recent scholarship includes both psychological and sociopolitical accounts of teachers’ emotional lives. However, within the 2020–2025 literature mapped in this review, leadership and institutional conditions are not always foregrounded with the same analytical specificity as teacher-focused constructs such as burnout, resilience or regulation. To examine this pattern, the study combines bibliometric mapping of 103 records with a focused interpretive thematic synthesis of 14 studies situated in or directly relevant to secondary school contexts. The bibliometric results show a field organised mainly around emotional labour, burnout, identity and teacher psychology vocabularies, with leadership and policy terms less prominent at the keyword level. The thematic synthesis identifies three recurrent school-level mechanisms through which emotional labour is discussed: accountability and governance, micropolitical recognition and exclusion, and support arrangements that shape how emotional burdens are shared or individualised. Across the 14 studies, non-native English-speaking teacher (NNEST) positioning and racialisation are most visible in studies that explicitly foreground legitimacy and marginalisation, but less visible in more generic studies of support or accountability. The review concludes that future research and practice would benefit from more clearly specifying institutional mechanisms and examining how secondary school conditions and NNEST positioning shape teachers’ emotional labour. Full article

Recent research in grounded language learning has seen remarkable success due to advances in large vision and language models (VLMs). However, these models (i) are extremely costly to train and update; (ii) struggle with generalisation; and (iii) do not support continual learning. In this paper, we introduce baby-ds integrating the Dynamic Syntax (DS) framework with automated planning within the multimodal BabyAI platform as a testbed. We provide methods whereby DS lexicons are induced continually from teacher demonstrations within BabyAI. We study (i–iii) by experimenting with the compositional complexity of natural language instructions in the data to compare data efficiency, generalisation, and continual learning properties of baby-ds with a simple neural model. The results show that the baby-ds model: (i) needs much less data than the neural model to reach threshold performance; (ii) generalises much faster to more complex instructions; and (iii) is a more effective continual learner. We argue that it is the attendant linguistic bias within DS and the rich inferential power of TTR that enables (i–iii), highlighting the importance of further research on hybrid grammar–neural approaches. Finally, we discuss several important limitations of baby-ds and sketch a path forward for further DS research. Full article

The rapid development of artificial intelligence (AI) and Generative AI (GenAI) based on large language models (LLMs) is reshaping teaching practices, assessment criteria, and ethical questions regarding authenticity, source reliability, and educational responsibility. Understanding teachers’ attitudes toward AI is crucial for identifying acceptance, resistance, and professional development needs. This study aimed to adapt and validate, for the Italian context, the questionnaire developed by Alsudairy and Eltantawy for assessing teachers’ attitudes toward AI in education, and to explore attitudinal differences according to selected socio-professional variables. A convenience sample of 682 in-service teachers from different school levels and Italian regions completed the 36-item questionnaire on a 3-point Likert scale. Exploratory factor analysis suggested an interpretable two-factor structure, although some items showed weak, non-salient, or cross-loadings. A confirmatory factor analysis conducted on a refined 32-item ordinal model supported a correlated two-factor solution with good global fit indices. However, the strong correlation between the two latent factors and the presence of selected weak indicators suggest that further refinement and cross-validation are needed. Educational attainment was the only socio-professional variable significantly associated with attitudes toward AI, although the effect size was small. Post hoc analyses showed a significant difference between teachers holding a postgraduate Master’s degree and those holding only a high school diploma, whereas other differences should be interpreted as descriptive trends. Taken together, these findings provide preliminary support for the Italian adaptation of the instrument and offer initial insight into the role of professional characteristics in shaping teachers’ attitudes toward AI in educational settings. Full article

Social bots significantly threaten online public opinion through manipulation and misinformation, posing detection challenges due to high anthropomorphism and concealment. GNN methods show superior performance but face deployment hurdles on real-world platforms because of their reliance on multi-hop neighbor information during inference. Conversely, pure text-based methods lack collective behavior modeling and robustness against advanced bots. This paper proposes LKD, a social bot detection framework for graph-less deployment. The framework utilizes large language models to summarize historical tweets, compressing long-text information to construct multi-source inputs including metadata, profiles, and tweets. By employing a GNN as the teacher and a pre-trained LM as the student, LKD transfers structural knowledge to a text-based model via dual-objective knowledge distillation across prediction distributions and feature spaces. Experiments on Cresci-2015 and TwiBot-20 datasets show that the graph-less LKD-LM mode outperforms state-of-the-art methods in accuracy and F1-score. It maintains stable performance in label-scarce and sparse-graph scenarios, providing an efficient, robust solution for social media platforms with restricted interfaces or real-time requirements. Full article

Background/Objectives: Developmental Language Disorder (DLD) affects approximately two children in every classroom and significantly impacts literacy development and academic achievement. Given the central role of language in learning, teachers are well-positioned to identify, support, and advocate for children with DLD through referrals, interventions, and inclusive curriculum delivery. However, evidence consistently indicates that teachers lack fundamental knowledge of DLD, highlighting an urgent need for targeted professional training. This study, conducted in Cyprus, aimed to (1) explore pre-school and primary school teachers’ knowledge and views regarding DLD and (2) synthesize an evidence-based checklist of essential topics for DLD teacher training. Methods: A total of 133 teachers completed an online questionnaire addressing three research questions: teachers’ knowledge of DLD and its characteristics; their attitudes toward DLD; and their perceptions of their role in supporting children with DLD. Results: Findings aligned with international trends, showing limited confidence in supporting students with DLD despite reasonable familiarity with the label and its core features. Teachers demonstrated a broad understanding of their supportive role but acknowledged knowledge limitations and requested structured professional development. Based on these findings and existing literature, the Basic-DLD Guide was created for researchers, practitioners, and continuing education providers, to inform the development of basic trainings. Conclusions: The study’s findings and the guide can have direct clinical significance, providing an evidence-informed foundation for designing structured professional training to improve identification and support for children with DLD in educational settings. Full article

The study aimed to systematically analyze studies published between 2013 and 2025 on the second-grade primary school EFL curriculum in Türkiye. The study identified the strengths and weaknesses of the components of the English curriculum and examined the suggestions proposed regarding implementation issues. In order to answer the research questions, 27 studies were analyzed using content analysis, and their methodological characteristics were reviewed. The analysis revealed that the objectives constituted the strongest component of the second-grade English curriculum, whereas the assessment component was the weakest. The problems with the curriculum implementation include the incompatibility of the curriculum with the Common European Framework of Reference (CEFR) for Languages, insufficient class hours, teachers’ inclusion of reading and writing skills even though they are not included in the learning outcomes, individual differences between students, students’ unpreparedness for foreign language learning, inadequate instructional materials, and parents’ lack of interest in foreign language education. The findings revealed that, while the curriculum is well-structured in terms of its objectives, its effectiveness is hindered by persistent challenges in assessment and implementation. This underscores the importance of improving the alignment between curricular intentions and instructional practices, highlights the need for targeted improvements in assessment practices, and offers practical insights for ongoing curriculum development efforts. Full article

The current study examined the perceptions of elite high school students in the United Arab Emirates about their experiences in learning and acquiring mathematical concepts and skills through the ALEKS system that stands for Assessment and Learning in Knowledge Spaces. ALEKS is an e-assessment and tutoring platform that facilitates the teaching and learning of mathematics for students in Grades 5–12 using versatile and personalized teaching functions. Eight participants of equally mixed gender participated in the study, four Grade 9 and four Grade 10 students. A qualitative research design in the form of one-to-one semi-structured interviews was used to have a deeper understanding of students’ ALEKS experiences, identify the challenges encountered while studying with it, and pinpoint the benefits and advantages of using ALEKS. Results showed that participating students frequently used ALEKS because of two main factors, including rewards promised by teachers and immediate feedback and feeling of immediate achievement provided by the platform. Challenges related to ALEKS were language barriers among the Arabic-speaking students studying in English, a lack of human interaction and support, time management issues, and the necessity for supplementary resources. Multiple advantages were also found, most noticeably how the ALEKS individualized adaptive learning environment helped participants gain more knowledge of mathematical concepts and develop their mathematics skills. Recommendations for mathematics teachers and policymakers include allowing students to utilize ALEKS in small groups in school, aligning ALEKS themes and topics with textbooks learning goals and objectives, giving systematic and personal guidance for increased independent use at home, and making bilingual editions and Arabic-language assets (e.g., tutorial videos) available. Full article

Policy learning under delayed reward conditions remains a significant challenge for end-to-end reinforcement learning (RL) agents. The difficulty increases for problems that require long-term planning and the execution of multiple dependent subtasks. As a result, solutions based on a single monolithic policy often suffer from unstable training. One possible solution to this problem could be to delegate the long-term planning to a separate model. This paper presents an implementation comprising two models: a large language model (LLM) for long-term planning and an execution model that solves subtasks. The execution model was trained via distillation from multiple teacher models trained with RL on individual tasks. The results presented in this paper demonstrate the benefits of this approach. By delegating long-term planning to the LLM, the agent can solve more complex problems than end-to-end agents trained with the proximal policy optimization (PPO) algorithm. Full article

Artificial intelligence (AI) tools are increasingly integrated into mathematics education, yet most reviews emphasize achievement rather than how AI shapes mathematical thinking. This scoping review mapped literature published between 2020 and 2026 on AI-supported mathematics learning through three cognition frameworks: APOS (Action–Process–Object–Schema), Sfard’s process–object duality and reification, and Conceptual Image theory. Searches were conducted in Scopus, Web of Science, ERIC, PsycINFO, Education Source, and IEEE Xplore, followed by duplicate removal and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR)-aligned screening. Twenty-one peer-reviewed studies met inclusion criteria (18 empirical studies plus three theoretically oriented studies). Evidence growth accelerated after 2022, with most studies situated in secondary and higher education. Large language models (LLMs) and Intelligent Tutoring Systems (ITS) were the most frequently investigated modalities. Across studies, AI commonly supported theoretically inferred action-level execution and procedural management (APOS) via adaptive feedback, hinting, and stepwise scaffolding, and it often broadened learners’ conceptual images through multiple representations and generated explanations. However, these interpretations were necessarily cautious, because very few studies directly operationalized theory-linked conceptual mechanisms such as process internalization, object encapsulation, reification, or alignment between conceptual images and formal definitions. In LLM-supported contexts, gains in explanation quality coexisted with risks of procedural outsourcing when students relied on generated solutions without prior reasoning. By contrast, ITS-based environments more often supported tightly structured procedural engagement, suggesting that different AI modalities afford different forms of cognitive support and risk. Overall, AI’s conceptual impact appears to depend less on tool availability and more on instructional orchestration (task design, prompting, and teacher mediation). The findings also suggest that sustainability-related dimensions—particularly learner agency, transparency of AI support, and equitable participation—are closely connected to whether AI use promotes durable conceptual learning rather than superficial performance gains. Future research should operationalize cognitive transitions, assess structural understanding, and report AI-use conditions transparently to support cumulative, theory-driven synthesis. Full article

The rapid proliferation of generative artificial intelligence (GenAI) tools has created an urgent need for instruments to evaluate their educational value as teachers, faculty, administrators, and instructional designers consider adopting them. While rubrics exist to assess mobile applications and virtual reality tools, no comparable instrument has been developed specifically for large language models (LLMs) and AI media generators. The authors reviewed existing evaluation rubrics for edtech and GenAI tools, with edtech meaning digital tools that support ethical teaching to improve student learning and GenAI referring to neural networks that simulate human interactions by contextualizing relevant content based on learning needs. Grounded in Waks’ framework, the resulting Edu-GenAI Rubric comprises multiple dimensions organized into five domains: the Instrumental, Technical, Hedonic, Use, and Beneficial values. Dimensions include accuracy, productivity, personalization, citation, user interface, user experience, sharing, storage, and ethical dimensions encompassing data privacy, data transparency, guardrails, fair use, and algorithmic discrimination. The Edu-GenAI Rubric offers decision-makers with a preliminary, theory-informed instrument for evaluating GenAI tools in educational contexts that can be applied to institutional adoption decisions, developer benchmarking, and future research. Full article