Category: K-12 Education

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Higher Education K-12 Education

AI and predictive analytics for student learning and retention

A conceptual review by Raji and colleagues examines how artificial intelligence (AI) and predictive analytics could support student achievement and retention through intelligent tutoring systems (ITS). Drawing on published literature, the authors explore personalised learning, early intervention and responsible implementation in school and higher education settings. They propose a framework that brings these functions together to support students with different learning needs.

The review discusses three main AI techniques: machine learning to predict performance and identify students at risk, natural language processing to interpret responses and provide conversational feedback, and deep learning to analyse complex learning patterns and adapt content. Information such as attendance, assessment results and online activity can inform these systems. The authors also connect ITS design with learning theories that emphasise knowledge construction, manageable cognitive demands and self-regulated learning.

The authors highlight the potential of immediate feedback, adaptive learning pathways and early warning systems to identify knowledge gaps and provide timely support. Their proposed framework combines learner profiles, predictive analytics, adaptive instruction and intelligent feedback, with explainable AI intended to make recommendations understandable to teachers and students. Implementation includes curriculum alignment, infrastructure preparation, pilot testing and ongoing evaluation. Expected benefits include stronger engagement, improved learning and retention, and more equitable access to support.

The article identifies data privacy, algorithmic bias and unequal access to devices, connectivity and digital skills as major challenges. It recommends clear guidelines, secure data practices, teacher development and collaboration among educators, policymakers and developers. Testing with diverse learners and explaining system decisions are also priorities. However, this is a conceptual synthesis: it presents no new student data, pooled effect sizes or experimental evaluation of the proposed framework. Its anticipated benefits should therefore be interpreted as possibilities requiring further evaluation.

Source (Open Access): Raji, B., Iyer, S. S., & Yaseen, M. M. N. M. (2026). AI-enabled predictive analytics in education: Enhancing student success and retention through intelligent tutoring systems. Journal of Digital Educational Technology, 6(1), 1-15.

https://doi.org/10.29333/jdet/18330… Read the rest

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K-12 Education Social and Motivational Outcomes

The Relationship Between Teacher Burnout, Absenteeism, Teacher–Student Interactions, and Student Motivation and Achievement

Wartenberg and colleagues used meta-analysis and systematic review methods to comprehensively examine the relationship between teacher burnout, teachers’ professional functioning, and student outcomes. The study integrated 86 studies, 90 independent samples, and a total of 38,457 teachers, systematically analyzed the associations between three core burnout symptoms—emotional exhaustion, depersonalization, and reduced personal accomplishment—and teacher absenteeism, the quality of teacher–student interactions, and student motivation and academic achievement.

The results showed that teacher burnout was consistently related to multiple professional and student outcomes, although the strength of the associations varied. First, in terms of teacher absenteeism, emotional exhaustion was positively but weakly related to absenteeism (r = .18, 95% CI [.08, .29]), depersonalization was also weakly positively related to absenteeism (r = .14, 95% CI [.04, .24]), and reduced personal accomplishment showed an association close to zero but still significant (r = .08, 95% CI [.03, .12]). Second, regarding teacher–student interactions, all three burnout symptoms were negatively related to interaction quality, with depersonalization (r = −.21, 95% CI [−.33, −.09]) and reduced personal accomplishment (r = −.21, 95% CI [−.37, −.05]) showing stronger links than emotional exhaustion (r = −.12, 95% CI [−.17, −.07]). Third, regarding student outcomes, emotional exhaustion was weakly negatively associated with student motivation (r = –.23, 95% CI [–.28, –.17]), but its overall association with student academic achievement was close to zero (r = –.05, 95% CI [–.11, .01]). Although fewer studies examined depersonalization and reduced personal accomplishment in relation to student motivation and achievement, the overall pattern was also negative.

Further meta-regression analyses showed that the source of ratings was an important moderator, and teacher self-reports usually produced stronger negative associations than external ratings, with the association between reduced personal accomplishment and instructional support was r = –.41 in teacher self-reports, but no longer significant in external reports (r = –.03; β = –.35, p < .001). This suggests that relying only on teacher self-ratings may overestimate the relationship between teacher burnout and teaching performance.

Overall, this study shows that teacher burnout is related not only to teachers’ own risk of absenteeism, but also consistently to poorer teacher–student interaction quality and lower student motivation, while its direct association with student academic achievement is relatively weak. The study also highlights that the different dimensions of burnout do not operate in the same way, with depersonalization and reduced personal accomplishment often showing stronger links to teacher–student interactions than emotional exhaustion. Based on these findings, the authors suggest that future teacher support and intervention programs should not focus only on reducing stress or emotional exhaustion, but should also pay attention to maintaining the quality of teacher–student interactions and teachers’ sense of professional competence. Methodologically, future research should combine observations, student reports, and longitudinal designs to capture more accurately the actual effects of teacher burnout on teaching and student development.

Source (Open Access): Wartenberg, G., Aldrup, K., Grund, S., & Klusmann, U. (2026). How strongly is teachers’ burnout related to teacher absenteeism, teacher–student interactions, and student motivation and achievement: A meta-analysis and systematic review. Review of Educational Research, 00346543261455273.

https://doi.org/10.3102/00346543261455273… Read the rest

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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