Category: K-12 Education

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Educational Administration and Leadership Higher Education K-12 Education

The impact of generative artificial intelligence on students’ higher order thinking: Evidence from a three-level meta-analysis

Given the current trends in AIEd, it is crucial to synthesize the overall impact of Gen-AI on HOT, as Gen-AI has been creatively applied by researchers to assist in teaching and learning. Concerns exist in current discourse that Gen-AI may harm HOT. However, empirical research on the specific effects of Gen-AI on HOT remains scattered, and no consensus has been reached. Nie et al.’s (2025) study synthesized 19 experimental and quasi-experimental studies conducted in different contexts (k=68), involving 2,347 participants. A three-level meta-analysis was employed to account for within- and between-study variability, assessing the impact of Gen-AI on HOT and exploring the effects of moderators.

The results revealed that Gen-AI had a significant positive effect on enhancing students’ HOT (Hedges’s g=0.851, p<0.001). Moderator analyses were conducted based on impact target, Gen-AI elements, study contexts, and methodological characteristics. When the sample size was less than 80, the promotion effect was more significant. Both short-term interventions (less than 4 weeks) and long-term interventions (more than 8 weeks) have the potential to produce significant positive effects.

These findings offer vital policy implications for integrating Gen-AI into education. First, educational authorities should mandate smaller class sizes or optimized student-to-teacher ratios in AI-driven curricula, as large groups dilute Gen-AI’s efficacy in fostering HOT. Second, curriculum frameworks must transition from short-term pilots to extended, long-term intervention timelines, given that 4-8 weeks is insufficient for deep cognitive development. Lastly, policymakers should fund targeted, granular research into AI’s specific impacts on different dimensions of HOT to guide the scaling of intelligent, personalized, and adaptive educational ecosystems.

Source (Open Access): Nie, X., Tian, Y., Liu, M., Wu, D., & Guo, Y. (2025). The impact of generative artificial intelligence on students’ higher order thinking: Evidence from a three-level meta-analysis. Education and Information Technologies, 30(17), 25359-25390.https://doi.org/10.1007/s10639-025-13735-x… Read the rest

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K-12 Education Programme Evaluation

The Effects of Integrated STEM Education on K12 Students’ Achievements: A Meta-Analysis

Integrated STEM education refers to T&L of scientific, technological, engineering, and mathematical knowledge and skills in integrative ways, emphasizing the connection between abstract knowledge and real-world problems. Integrated STEM education is characterized by four core features: multidisciplinary integration, real-world application, authentic inquiry or design-based practice, and active student learning. Based on 124 extracted and coded studies (2010-2022), Chen et al.’s (2025) meta-analysis reports on the effects of integrated STEM education based on three main types of interventions: (1) adopting integrated STEM education, (2) using extra teaching and learning strategies to enhance integrated STEM education, and (3) using specific learning technologies to support integrated STEM education.

All three types of interventions yielded a medium effect on knowledge acquisition and a small effect on student perceptions. Besides, adopting integrated STEM education had a large effect on cognitive skills; using extra teaching and learning strategies in integrated STEM programs produced a medium effect on cognitive skills and problem-solving task performance; using specific learning technologies had a small effect on problem-solving task performance. Some factors, such as task type (inquiry or design-based task) and program duration, may influence STEM learning outcomes.

To maximize the efficacy of integrated STEM education, practitioners should embed its four core characteristics into curriculum design while favoring short-to-medium duration programs (one month to a semester). Educators must carefully balance hands-on design and minds-on inquiry tasks by providing necessary scaffolding tailored to students’ prior knowledge. Furthermore, deploying targeted instructional strategies and learning technologies can enhance engagement with complex, real-world problems. Ultimately, evaluating these programs requires a multidimensional approach that prioritizes skill development and practical problem-solving performance alongside traditional knowledge acquisition.

Source (Open Access): Chen, B., Chen, J., Wang, M., Tsai, C. C., & Kirschner, P. A. (2026). The effects of integrated STEM education on K12 students’ achievements: A meta-analysis. Review of Educational Research, 96(2), 619-668.

https://doi.org/10.3102/00346543251318297… Read the rest

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K-12 Education Social and Motivational Outcomes

Social and Emotional Learning Programs and Students’ Prosocial Behavior: A Meta-Analysis

A recent meta-analysis conducted by Hung and colleagues examined the effectiveness of school-based social and emotional learning (SEL) programs for K–12 students’ prosocial behavior. Prosocial behavior is conceptualized as any voluntary behavior intended to benefit others, such as helping, sharing, comforting, and defending others. The researchers analyzed 66 studies and 157 effect sizes involving 52,914 youth.

Effect sizes were calculated using Hedges’ g, which includes a small sample bias correction to the effect size estimate to account for small studies. Because most studies contributed multiple effect sizes, the authors used a correlated effects (CE) model with robust variance estimation (RVE) to account for within-study dependence among effect sizes. Approach refers to whether an SEL program takes a curricular, interactional, structural, or combined approach. To examine whether the effect of SEL programs on prosocial behavior was moderated by sample, program, methodological, and publication characteristics, the authors conducted a mixed-effects meta-regression analysis with all moderators added to the model simultaneously. In total, they investigated 14 moderating variables such as approach, school level, urbanicity, and dosage.

The remaining moderating variables yielded no statistically significant differences. Results indicated that effects for rural, suburban, and combination areas were not statistically different from samples from urban areas. Results also indicated that effects were not statistically significantly different between samples with higher versus lower proportions of students qualifying for free or reduced-price lunch. Effects of curricular and curricular combined with structural or interactional approaches were not significantly different from SEL programs that only used an interactional approach. Findings indicated that effects of studies delivered at Tier 2 were not statistically different from Tier 1 studies. Studies that used a quasi-experimental design and single-group pre–post design yielded similar effects on prosocial behavior compared to studies that used a randomized controlled trial. Effects were similar across different types of prosocial behavior measures. Effects from studies that did not meet baseline equivalence were not statistically significantly different from studies that met baseline equivalence, and effects from studies that did not report implementation fidelity were not statistically significantly different from studies that reported fidelity of implementation. Results indicated that effects from studies conducted across earlier decades were not statistically significantly different from studies conducted more recently, and effects were similar for peer-reviewed and non-peer-reviewed studies.

The authors further noted that most studies were conducted with elementary school children (56%), the majority implemented universal Tier 1 interventions (89%), and a curricular approach was the most common (77%). Additionally, a considerable proportion of studies did not report key demographic data, with 71% failing to report free or reduced-price lunch rates.

A key implication for practice from this meta-analytic review is that school-based SEL programs are effective in promoting K–12 students’ prosocial behavior, and that “more is not necessarily better” — a moderate dosage and moderate duration may be most ideal. Future policy and practice should take into account this “less is more” finding. At the same time, more research is needed involving secondary schools, rural schools, non-curricular approaches, and diverse student populations in order to fully understand the effectiveness of SEL programs.

Source (Open Access): Hung, C., Brass, N. R., Brockmeier, L., Bergin, C., Imler, M., & Luper, S. B. (2026). Social and Emotional Learning Programs and Students’ Prosocial Behavior: A Meta-Analysis. Review of Educational Research, 00346543261438462.

https://doi.org/10.3102/00346543261438462… Read the rest

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K-12 Education Social and Motivational Outcomes

The Gap Between Teachers’ Self-Efficacy, Management Strategies, and Actual Classroom Management Behaviors

Shi and colleagues combined questionnaire survey methods with AI-supported classroom behavior analysis to examine the relationships among teachers’ classroom management self-efficacy, self-reported classroom management strategies, and actual classroom management behaviors observed by AI. The study involved 345 Chinese K-12 in-service teachers, collected questionnaire data on their classroom management self-efficacy and strategies, and analyzed 673 valid classroom video recordings, totaling 461.74 hours. The research team developed an AI-supported multimodal classroom management behavior analysis tool that automatically identified teachers’ praise statements, criticism statements, discipline-related statements, positive tone of voice, proportion of proximity to students, and proportion of visual attention to students through text, audio, and image data. This allowed the researchers to test whether teachers’ beliefs, reported strategies, and actual behaviors were consistent.

The results showed that teachers with higher classroom management self-efficacy reported using all types of classroom management strategies more frequently, and these differences were all statistically significant. For example, in praise strategies, the high self-efficacy group had a mean rank of 214.43, significantly higher than the low self-efficacy group’s 128.84 (Z = –8.74, p < .001). In corrective feedback strategies, the high group had a mean rank of 206.27, compared with 137.54 in the low group (Z = –6.67, p < .001). In preventive management strategies, the high group had a mean rank of 221.84, whereas the low group had 120.95 (Z = –9.81, p < .001). In commands/transition strategies, the high group had a mean rank of 220.69, compared with 122.17 in the low group (Z = –9.68, p < .001). However, when actual classroom videos were analyzed through AI, no significant differences emerged between the high and low self-efficacy groups on most observable classroom management behaviors. The only finding was a marginal tendency for teachers with lower self-efficacy to use more discipline-related statements (p = .07), suggesting that they may rely more on disciplinary language to maintain order.

Regarding the relationship between self-reported strategies and AI-observed behaviors, the results showed that consistency existed only in some domains. Teachers’ self-reported praise strategies were significantly positively related to AI-detected praise statements and positive tone of voice, although the effect sizes were relatively small. In contrast, corrective feedback and preventive management strategies were not significantly associated with their corresponding AI-based behavioral indicators. Notably, self-reported commands/transition strategies were significantly negatively related to AI-observed discipline-related statements, meaning that the more often teachers reported using clear instructions and transition management, the less frequently discipline-related statements appeared in their classrooms. Overall, teachers’ reported use of strategies only partially corresponded to their actual classroom behaviors, and more concrete and observable strategies, such as praise, were more likely to be validated by AI observation.

Overall, this study suggests that teachers’ beliefs, strategies, and behaviors in classroom management are not always highly aligned. Although teachers with higher self-efficacy reported using more effective strategies, they did not necessarily demonstrate clearly different classroom management behaviors in practice. Only specific and easily identifiable strategies, such as praise, were more readily confirmed through AI observation. The study therefore highlights a misalignment between teachers’ beliefs and their actual teaching behaviors, while also demonstrating the considerable potential of AI for large-scale, non-intrusive, and evidence-based research on classroom management. For educational research and teacher development, the study serves as a reminder that relying solely on teachers’ self-report questionnaires may overestimate the consistency between reported strategies and real classroom behavior. Future work should combine self-report data with more objective methods such as AI observation to gain a fuller understanding of actual classroom management practices.

Source (Open Access): Shi, Y., Wang, Z., Chen, Z., Ren, D., Liu, H., & Zhang, J. (2026). Do teachers … Read 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 Learning, 10(1), 29.

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

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Achievement K-12 Education Maths and Science Learning

The Impact of Mathematics and Science Professional Development on Teacher Knowledge, Instruction, and Student Achievement

A recent meta-analysis by Lynch and colleagues examined the effectiveness of professional development (Professional Development) interventions for mathematics and science teachers in grades PK-12. Analyzing 200 effect sizes for teacher outcomes and 126 effect sizes for student achievement from 46 experimental studies published from 2001 to 2024, the authors investigated how PD programs affect teachers’ knowledge and classroom instruction, and whether these changes translate into improved student learning.

The authors employed Hedges’s g as the effect size metric, using randomized controlled trial designs to ensure causal inference. PD interventions were categorized by their focus areas: improving teacher knowledge (content knowledge and pedagogical content knowledge), content-specific and content-general instructional strategies, and content-specific formative assessment. The researchers also examined contextual factors such as intervention duration, inclusion of curriculum materials, and school demographics.

The results revealed a significant positive impact of PD on teacher outcomes (pooled average: +0.52 SD). Specifically, teacher knowledge improved by +0.52 SD and classroom instruction by +0.49 SD. Importantly, programs with larger impacts on teacher outcomes also demonstrated significantly larger effects on student achievement. A 1 SD improvement in teacher-level outcomes was associated with a +0.18 SD gain in student achievement. Notably, improvements in classroom instruction showed a stronger link to student learning (+0.24 SD) than knowledge gains (+0.08 SD, not statistically significant). PD programs explicitly focusing on teacher knowledge development (effect size difference: +0.18 SD) and content-specific formative assessment (+0.27 SD) showed significantly stronger impacts on classroom instruction. Interestingly, intervention duration and the inclusion of curriculum materials did not significantly moderate outcomes.

The findings underscore that the quality and specific focus of professional development matter more than duration. Schools should prioritize PD programs that explicitly target both teacher knowledge and instructional practices, particularly emphasizing formative assessment strategies. The strong link between improved instruction and student achievement validates investments in high-quality professional development as a lever for enhancing educational outcomes in mathematics and science.

Source (Open Access): Lynch, K., Gonzalez, K., Hill, H., & Merritt, R. (2025). A meta-analysis of the experimental evidence linking mathematics and science professional development interventions to teacher knowledge, classroom instruction, and student achievement. AERA Open, 11, 23328584251335302.https://doi.org/10.1177/23328584251335302… Read the rest