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Language Development Primary School Education

Inference Training for Homonyms: Evidence from Two Randomized Controlled Trials in Primary Schools

A recent study by Booton and colleagues, investigated whether a brief lexical inference intervention could support children aged 7–8 years in learning the multiple meanings of homonyms, words that share the same spelling but carry distinct meanings (e.g., bat, bank, bark). Despite the prevalence of homonyms in everyday English and their well-documented challenge for young readers, no effective targeted intervention had previously been identified in the literature.

The researchers conducted two separate randomized controlled trials (RCTs) across English state primary schools. In Study 1, 180 children from six schools were randomly assigned to either an inference training condition (n = 60) or a spatial reasoning active control condition (n = 120). Participants attended four 30-minute intervention sessions delivered in small groups of four over a two-week period. In Study 2, 76 children, including 37 with English as an Additional Language (EAL) and 39 with English as a first language (EL1), were assigned through stratified randomisation to either the inference training (n = 40) or an implicit exposure control involving contextualised reading (n = 36). This second study also incorporated pre-registered methodology and measured metacognitive and inference skills alongside homonym knowledge.

The inference intervention, referred to as “Word Detectives,” trained children to use contextual clues within sentences to deduce the intended meaning of a homonym. Children were taught to notice, question, and infer meanings in a structured, experimenter-led format. The control groups received time-matched activities of a different nature—either spatial reasoning tasks (Study 1) or implicit reading exposure to the same target vocabulary without explicit inference instruction (Study 2). Receptive knowledge of both taught and untaught homonyms was assessed before and after the intervention using a researcher-developed homonym recognition task, while Study 2 additionally employed the York Assessment of Reading for Comprehension (YARC) to measure standardised reading comprehension.

Results from both RCTs consistently demonstrated that children in the inference training conditions made significantly greater gains in receptive homonym knowledge than their counterparts in the control groups. In Study 2, trained children also showed improved performance on the inference task itself. Importantly, while children with EAL displayed a specific baseline disadvantage in receptive homonym knowledge relative to their EL1 peers, the intervention proved equally effective for both language groups, suggesting its broad applicability across diverse classroom populations. Furthermore, receptive knowledge of homonyms and inference ability each predicted unique variance in reading comprehension scores beyond other vocabulary measures, highlighting the educational significance of homonym understanding for broader literacy outcomes.

The study did, however, identify notable limitations. Transfer of learning to untaught homonyms was limited, although error analysis suggested emergent generalisation of the inferencing strategy. The intervention window was brief (approximately two weeks), and follow-up data beyond the immediate post-test were not collected, leaving questions about the durability of gains unanswered. The researchers call for future studies with longer intervention periods, delayed follow-up assessments, and investigations into whether the intervention could be scaled for classroom delivery by teachers rather than trained researchers.

These findings carry meaningful implications for educational practice. Explicitly teaching lexical inference as a skill, rather than relying on incidental vocabulary acquisition through reading alone, may represent an efficient and equitable approach to bolstering both vocabulary and reading comprehension in the primary years, particularly in linguistically diverse classrooms where English language learners are present.

 

Source (Open Access): Booton, S. A., Birchenough, J. M., Gilligan‐Lee, K., Jelley, F., & Murphy, V. A. (2026). Lexical inference training for homonyms: Two randomized controlled trials for children with English as a first and an additional language. British Journal of Educational Psychology.

https://doi.org/10.1111/bjep.70056Read the rest

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Effective Teaching Approach K-12 Education

The effect of AI-driven intelligent tutoring systems on K-12 students’ learning and performance: A systematic review

A recent systematic review published in npj Science of Learning examines the effects of intelligent tutoring systems (ITSs) on students’ learning and performance in K-12 education. As artificial intelligence in education (AIEd) has expanded rapidly, ITSs have emerged as a key application with the potential to personalize learning and improve educational outcomes. However, despite their growing adoption, their actual educational value remains uncertain. While some studies suggest that ITSs can enhance learning outcomes and even outperform traditional instruction, others report limited or inconsistent effects. In addition, existing research often conflates different educational contexts or focuses on broader AI applications, leaving a lack of systematic understanding of ITS effectiveness specifically in K-12 settings. This study therefore aims to assess the effects of ITSs on K-12 students’ learning and performance and to examine the experimental designs used to evaluate these systems.

The authors conducted a systematic review of 28 empirical studies involving a total of 4,597 students. Most studies adopted quasi-experimental designs, typically comparing an ITS-based intervention group with control conditions such as traditional teacher-led instruction, non-intelligent tutoring systems, modified ITSs, or no control group. The studies covered a range of countries, subjects, and school levels, with a strong concentration in middle and high school STEM education. Intervention durations varied considerably, from a single class session to several weeks or months. The review categorized studies based on educational context, experimental design, and intervention characteristics to enable a structured comparison of findings.

The review finds that ITSs generally have a positive effect on students’ learning and performance in K-12 education, particularly when compared to traditional teacher-led instruction, where most studies report medium to large effects. However, when compared with non-intelligent tutoring systems, the results are more mixed, with several studies finding no significant differences. Substantial heterogeneity is observed across studies due to differences in design, duration, and context. Importantly, the effectiveness of ITSs depends on key features such as personalization, adaptivity, and real-time feedback, as well as on implementation conditions. ITSs that are integrated with teacher support, encourage self-regulated learning, and are used over longer periods tend to produce better outcomes. In contrast, short interventions may be influenced by novelty effects, and learner characteristics such as prior knowledge and educational level also shape outcomes.

Taken together, the findings suggest that ITSs can enhance learning and performance in K-12 education, but their effectiveness is contingent upon pedagogical design and implementation conditions rather than technology alone. ITSs are most effective when aligned with sound instructional principles and used in combination with teacher guidance. The study also highlights limitations in the existing literature, including short intervention durations, limited sample diversity, and a lack of attention to ethical considerations. It calls for future research with more robust experimental designs, longer interventions, and greater attention to ethical issues, particularly as AI technologies continue to evolve and play an increasing role in education.

Source (Open Access): Létourneau, A., Deslandes Martineau, M., Charland, P., Karran, J. A., Boasen, J., & Léger, P. M. (2025). A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education. npj Science of Learning10(1), 29.

https://doi.org/10.1038/s41539-025-00320-7Read the rest

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Effective Teaching Approach Secondary School Education

LLM-Based Collaborative Programming: Effects on Computational Thinking and Self-Efficacy

Yan et al. (2025) examine whether integrating large language models (LLMs) into collaborative programming can enhance students’ computational thinking, self-efficacy, and learning processes. Recognizing that traditional collaborative programming is often constrained by uneven skill levels among students, the study proposes an LLM-supported collaborative framework in which AI acts as a learning partner, transforming the conventional human–human interaction into a human–human–AI collaboration model. A quasi-experimental design was conducted with 82 sixth- and seventh-grade students in China, who were randomly assigned to either an LLM-supported collaborative programming group (experiment group) or a traditional collaborative programming group (control group).

The intervention lasted five weeks and included 12 programming sessions (90 min each) using C++ as the instructional language. Students in both groups worked in teams, but the experimental group used an LLM-based platform that provided structured, problem-based, and knowledge-based scaffolding throughout the programming process, including problem analysis, coding, debugging, and evaluation. Pre- and post-tests measured students’ computational thinking and self-efficacy, while cognitive load was assessed through questionnaires, complemented by semi-structured interviews.

Results indicate that students in the LLM-supported collaborative programming group achieved significantly higher gains in computational thinking compared to those in the traditional group, though the effect size was relatively small. In addition, students in the experimental group reported significantly lower cognitive load, particularly in mental load, suggesting that LLMs can reduce the cognitive burden associated with complex programming tasks. However, no statistically significant differences were found in self-efficacy between the two groups. Both groups showed a decline in self-efficacy over time, likely due to the transition from graphical programming to more abstract text-based coding, though the decline was less pronounced in the LLM-supported group.

Qualitative findings further reveal that LLM integration enhanced students’ learning experiences by increasing interest, improving problem-solving efficiency, and supporting collaboration. Students reported that LLMs provided immediate feedback, multiple solution strategies, and personalized guidance, enabling more effective engagement in programming tasks. Overall, the study demonstrates that LLMs can function as effective scaffolding tools in collaborative learning, reducing cognitive load and enhancing higher-order thinking. While their impact on self-efficacy remains inconclusive, the findings highlight the potential of AI-supported collaborative learning environments as a promising approach for programming education in K–12 contexts.

Source (Open Access): Yan, Y. M., Chen, C. Q., Hu, Y. B., & Ye, X. D. (2025). LLM-based collaborative programming: Impact on students’ computational thinking and self-efficacy. Humanities and Social Sciences Communications12(1), 149.https://doi.org/10.1057/s41599-025-04471-1Read the rest

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Primary School Education Social and Motivational Outcomes

Effects of virtual reality exercise on social skills and emotional recognition among children with autism spectrum disorder: a meta-analysis of randomized controlled trials

A meta-analysis by Cui and colleagues assessed the effects of virtual reality (VR) exercise on social skills (SS) and emotional recognition (ER) among children with autism spectrum disorder (ASD). Analysing data from randomized controlled trials published between January 2005 and October 2025 across PubMed, Web of Science, Scopus, and EBSCO databases, the authors investigated the relationship between VR exercise interventions and children’s social-emotional development outcomes.

The authors employed standardized mean difference (SMD) as the effect size, utilizing random-effects models to synthesize results across studies. VR exercise interventions were compared with standard treatment approaches. The methodology included comprehensive database searches using keywords: virtual reality, autism spectrum disorder, and children. Quality assessment followed Cochrane Handbook guidelines, with heterogeneity evaluated through I² statistics. Subgroup analyses examined intervention duration effects (< 14 weeks versus ≥ 14 weeks), and secondary outcomes included cognitive function, anxiety, language function, and depression.

The results revealed significant positive effects of VR exercise on SS (SMD = 0.94 [0.71, 1.17], p < 0.05, I² = 74%) and ER (SMD = 0.42 [0.18, 0.65], p < 0.05, I² = 0%). Furthermore, subgroup analysis demonstrated that interventions lasting less than 14 weeks (SMD = 0.63 [0.36, 0.91], p < 0.05, I² = 0%) and those exceeding 14 weeks (SMD = 1.70 [1.27, 2.13], p < 0.05, I² = 44%) both substantially improved SS, with longer interventions showing greater effect sizes. Additionally, VR exercise improved cognitive function (SMD = 0.49 [0.06, 0.93], p < 0.05, I² = 0%) and reduced anxiety (SMD = 0.56 [1.10, 0.02], p < 0.05, I² = 0%). Notably, effects on language function and depression remained unclear due to insufficient evidence.

The findings underscore the effectiveness of VR exercise as a technological intervention modality superior to standard treatment approaches in enhancing social-emotional competencies among children with ASD. Therefore, future clinical practice should consider integrating VR exercise interventions into rehabilitation programs for children with ASD, particularly emphasizing intervention duration optimization to maximize therapeutic benefits. The moderate effect sizes and cautious interpretation regarding cognitive and anxiety outcomes require validation through larger-scale longitudinal studies with standardized outcome measures.

Source (Open Access): Cui, T., Ariffin, R. B., Wang, X., & Wang, X. (2026). Effects of virtual reality exercise on social skills and emotional recognition among children with autism spectrum disorder: a meta-analysis of randomized controlled trials. BMC psychology.

https://doi.org/10.1186/s40359-026-04160-xRead the rest

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Effective Teaching Approach Kindergarten Language Development

The Playful Pen: Strategies for embedding Writing Instruction into Daily Play

To explore how educators can integrate early writing instruction into kindergarten classrooms through guided play. Sanchez (2025) addresses the growing tension between play-based learning and the rigid, policy-driven academic curricula common in modern early childhood education. Because strict mandates often cause frustration for young learners during formal writing tasks, Sanchez (2025) proposes guided play—a blend of child-led exploration and intentional adult scaffolding—as a pedagogical solution to meet literacy goals while preserving autonomy.

The study was designed as a five-month participatory action research project conducted by Sanchez (2025) in a public kindergarten classroom of 25 students. Sanchez (2025) dedicated one hour daily to guided play, intentionally introducing targeted writing tools, books, and printables into popular areas like the playdough, block, dramatic play, and Lego centers. By carefully observing and interacting with the children, Sanchez (2025) seamlessly integrated early writing prompts into their natural play routines.

By thoughtfully curating materials and engaging in collaborative dialogue, Sanchez (2025) successfully motivated students to independently incorporate writing into their spontaneous play. Children naturally began authoring authentic texts, including “how-to” guides for playdough snowmen, labels for complex block mazes, dramatic play pie recipes, and step-by-step Lego instructions. This playful approach transformed writing from a stressful, mandated chore into a joyful, self-directed activity that empowered even the most reluctant students.

Sanchez (2025) concludes that guided play effectively dismantles the false dichotomy between structured academic learning and early childhood play. The research highlights that successful implementation requires dedicating adequate classroom time, encouraging storytelling with an audience, and fostering a supportive community among educators. Ultimately, intentional scaffolding allows writing to evolve from an isolated academic skill into an authentic, meaningful communication tool that honors young children’s agency.

 

Source (Open Access): Sanchez, A. (2025). Guided play in the kindergarten classroom: One teacher’s inquiry into scaffolding play-based writing instruction. Early Childhood Education Journal53(6), 2089-2098.

https://doi.org/10.1007/s10643-025-01931-wRead the rest

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Primary School Education Secondary School Education Social and Motivational Outcomes

Teachers’ Teaching Emotions, Teaching Mindset, and AI Readiness

Ti and colleagues employed a cross-sectional survey design with hierarchical regression and moderation analyses to examine how in-service teachers’ teaching emotions and teaching ability mindset predict their AI readiness, and whether mindset moderates the relationship between emotions and AI readiness. The study included 424 in-service teachers in China (mean age = 38.76 years) from both primary and secondary schools. AI readiness was measured using Wang et al.’s (2023) four-dimensional framework, including cognition, ability, vision, and ethics. Teaching emotions were categorized into positive and negative emotions, and teaching mindset was classified as growth or fixed. Gender and social desirability bias were controlled in the analyses, and interaction effects were tested using the PROCESS macro.

The results showed that positive teaching emotions significantly and positively predicted all four dimensions of AI readiness (B ≥ .48, p < .001), whereas negative emotions did not significantly predict any dimension (|B| ≤ .06, p ≥ .309). Regarding mindset, a growth teaching mindset had significant positive effects on cognition, ability, vision, and ethics (B ≥ .18, p < .01), indicating that teachers who view teaching ability as developable are better prepared to respond to AI-related educational change. Interestingly, a fixed teaching mindset did not uniformly produce negative effects; instead, it positively predicted the cognition and ability dimensions (B ≥ .17, p < .01), although it was not significant for vision and ethics. Overall, the inclusion of emotions and mindset in the models yielded medium to large effect sizes (.28 ≤ f² ≤ .36), suggesting substantial explanatory power.

Moderation analyses further revealed that a growth teaching mindset strengthened the positive relationship between positive emotions and AI cognitive readiness (B = .11, p < .05). In other words, teachers with both high positive emotions and a strong growth mindset demonstrated higher levels of understanding regarding AI roles and functions. In contrast, a fixed teaching mindset moderated the relationship between negative emotions and the cognitive dimension (B = .10, p < .05). When fixed mindset was low, negative emotions significantly reduced cognitive readiness (B = –.18, p < .05), whereas this effect was not significant when fixed mindset was high. Notably, moderation effects were observed only for the cognition dimension, suggesting that the cognitive aspect of AI readiness is particularly sensitive to the interaction between emotional and mindset resources.

Overall, this study indicates that teachers’ readiness for AI integration in education is influenced not only by technical training or institutional support but also by their emotional experiences and beliefs about the malleability of teaching ability. Positive emotions and a growth teaching mindset serve as important psychological resources that enhance AI readiness, especially in shaping teachers’ cognitive understanding of AI. The authors recommend that AI-related professional development initiatives incorporate emotional regulation support and mindset cultivation to foster more comprehensive and sustainable AI readiness among teachers.

Source (Open Access): Ti, Y., Sun, Y., & Li, X. (2026). Predicting in-service teachers’ AI readiness from emotions in teaching and mindsets about teaching ability: Testing the direct and moderating effects. Teaching and Teacher Education175, 105433.

https://doi.org/10.1016/j.tate.2026.105433Read the rest