Category: Educational Administration and Leadership

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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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Educational Administration and Leadership Secondary School Education

Vocational–General Pathways and Student Futures: Evidence from England’s UTCs

Using a two-stage least squares (2SLS) causal inference design with linked administrative and tax data, Machin and colleagues (2025) evaluate the effects of University Technical Colleges (UTCs) on student achievement, higher education enrolment, and labour market outcomes in England. UTCs, introduced in 2010, combine academic and vocational curricula and allow entry at ages 14 or 16, creating a natural contrast for studying the timing of specialization.

The findings show starkly different consequences by entry age. Students joining UTCs at 14 perform substantially worse in GCSE exams, particularly in English, maths, and science, leading to projected lower lifetime earnings. By contrast, students entering at 16 benefit from stronger vocational outcomes, a higher likelihood of pursuing STEM university degrees, and improved early employment rates and earnings. This divergence highlights the risks of premature specialization and the advantages of aligning vocational opportunities with natural transition points in the education system.

The study contributes to the debate on hybrid schooling models by showing that while early diversion harms academic progression, later vocational engagement can enhance STEM capacity and labour market matches. Policymakers are advised to design vocational routes with caution, ensuring pathways support both academic progression and technical skill development.

 

Source (Open Access): Machin, S., McNally, S., Terrier, C., & Ventura, G. (2025). Closing the Gap Between Vocational and General Education?: Evidence from University Technical Colleges in England. Journal of Human Resources.

https://doi.org/10.3368/jhr.0223-12768R1… Read the rest

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Educational Administration and Leadership Higher Education

Predicting program-specific first-year persistence in higher education using a person–environment fit perspective

A recent predictive modeling study by de Vries et al. (2025) examined how different conceptualizations of person–environment (PE) fit predict program-specific persistence among first-year undergraduates. Data were collected from 1305 first-year  students across five non-selective bachelor’s programs at a large research-intensive university (Biomedical Sciences, Business Administration, Health & Life Sciences, Law, and Movement Sciences), with at least 200 students per programme.

Using logistic regression with LASSO regularization and 10-fold cross-validation, the authors compared five clusters of predictors: interest fit, ability fit, prior achievement/ personality/ motivation, choice process, and background characteristics. Both profile correlations and polynomial regression approaches were applied to operationalize interest fit. High school exam grades and self-reported abilities captured ability fit.

Results showed that models predicted persistence with moderate accuracy: 67–77% in training samples and 50–75% in testing samples. Models performed better in correctly classifying persisters than non-persisters. Interest fit was the most consistent predictor, with polynomial regression-based measures retained in four of the five disciplinary models (small-to-moderate effect sizes). Profile correlation indices also contributed in some programs, but less strongly. Ability fit through subject-specific high school grades (e.g., biology for Biomedical Sciences, mathematics for Business Administration) emerged as another robust predictor. In contrast, traditional indicators such as HSGPA, personality traits, and motivational measures had limited additional value once interest and subject-specific grades were included. Choice process variables (e.g., depth of exploration) and background characteristics showed only marginal contributions.

The study highlights the importance of discipline-specific interests and ability matches in predicting first-year persistence. While the models are more effective at forecasting persistence than dropout, the findings stress the need for program-specific approaches to student success and suggest that admissions and advising practices should focus more on interest and subject fit rather than broad indicators like GPA or personality.

 

Source (Open Access): de Vries, N., Merkle, B., Meeter, M., Janke, S., Bakker, T. C., & Huizinga, M. (2025). Predicting program-specific first-year persistence in higher education using a person-environment fit perspective. European Journal of Higher Education, 1-22.

https://doi.org/10.1080/21568235.2025.2502536… Read the rest

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Achievement Educational Administration and Leadership Primary School Education

Unlocking student potential: How self-regulated learning boosts elementary success

A recent systematic review examined the impact of self-regulated learning (SRL) interventions in elementary classrooms, where foundational learning habits are established. SRL refers to students’ ability to set goals, monitor progress, and adjust strategies. These skills are associated with increased focus, persistence, and academic achievement.

The review synthesized findings from 10 studies published between 2008 and 2022 in Germany, Portugal, Spain, and Estonia. Nearly 3,000 students in grades 3 to 6, ages 8 to 16, participated. Researchers assessed both SRL skill development and academic outcomes, particularly in math and writing.

All 10 studies reported improvements in students’ self-regulation skills, with effect sizes ranging from +0.20 to +0.67. Four of the six studies that measured academic performance found gains in math or writing, though effect sizes were generally smaller than those observed for SRL outcomes. Effective programs were typically delivered by classroom teachers and incorporated guided practice, reading, and peer discussion. Motivation played an important role, with more engaged students benefiting the most. The findings suggest that embedding SRL strategies into daily instruction may be a valuable way to support student success.

 

Source (Open Access): Olid-Luque, M., Ayllón-Salas, P., Arco-Tirado, J. L., & Fernández-Martín, F. D. (2025). Impact of self-regulated learning programs in primary education: A systematic review. Psychology in the Schools, 62(3), 734–755. https://doi.org/10.1002/pits.23352… Read the rest

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Educational Administration and Leadership Effective Teaching Approach Secondary School Education

Leveraging AI to predict young learners’ online learning engagement

With many schools rushing to adopt Generative AI, it is important to consider the real learning gains (or lack thereof) that these tools offer. A 2023 study by Pardos & Bhandari examined the use of AI-generated hints as a scaffolding mechanism with Algebra students.

Seventy-seven participants (high school graduates selected via Amazon’s MTURK system) were assigned to a control group (which provided human-generated hints) or an experimental group (which provided AI-generated hints). The researchers wanted to learn the rate of “low quality” AI-generated hints, as well as if the hints produced learning gains compared to the control group. The questions from the lesson were fed, verbatim, to ChatGPT in order to generate the hints. Quality checks were performed manually to ensure that all AI-generated hints were correct and showed the proper steps. This was then contrasted with the control group, whose hints were generated by undergraduate tutors. Pre and post tests were administered to check for learning gains between the two groups.

The results showed that 70% of the hints generated by ChatGPT were considered to be good quality, and that there was a statistically significant learning gain in the control group. A major limitation of the study is that the researchers did not prompt the AI to use any scaffolding strategies. Therefore, the quality of the hints between groups not only differed by human or AI creator, but also by pedagogical theory. Human tutors were probably more likely to employ Vygotsky-esque scaffolds, while ChatGPT was more likely to provide an immediate answer. Future work could improve upon the prompts used in this study and create a multi-tiered approach with less consequential hints being revealed at first.

 

Source (Open Access): Pardos, Z. A., & Bhandari, S. (2023). Learning gain differences between ChatGPT and human tutor generated algebra hints (No. arXiv:2302.06871). https://doi.org/10.48550/arXiv.2302.06871… Read the rest

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Educational Administration and Leadership Higher Education

Metacognitive support for self-regulated learning in GenAI environment

A recent study by Xu and colleagues investigated the impact of metacognitive support on self-regulated learning (SRL) and learning experiences within generative AI (GenAI) environments. The study addressed concerns about students’ reliance on ChatGPT and the challenges they face in sustaining SRL without guidance.

Participants included 68 sophomore students in a university in China, aged 19–21, majoring in Educational Technology, who had no prior experience with GenAI tools in classroom tasks. The students were randomly divided into two groups: the experimental group received a metacognitive support framework, while the control group did not. Delivered on paper to reduce distractions, the framework encouraged planning, monitoring, and reflecting during tasks, with prompts like setting clear goals, evaluating ChatGPT’s feedback, and reflecting on task performance. Over a four-week period, the participants completed interdisciplinary tasks that integrated knowledge from multiple subjects.

Based on self-report questionnaires, the experimental group demonstrated significant improvements in two of six SRL abilities: in task strategies (ES = +0.69) and self-evaluation (ES = +0.53), while the control group exhibited declines in these areas. Students with metacognitive support also reported lower cognitive load and perceived AI tools as more useful, although these findings were not statistically significant. Academic performance gains were higher in the experimental group (ES = +0.36) but the difference was not statistically significant.

This study suggests that metacognitive support helps students engage with AI tools and develop active learning. Without such guidance, students experience a decline in self-regulation skills which could undermine learning autonomy.

 

Source: Xu, X., Qiao, L., Cheng, N., Liu, H., & Zhao, W. (2025). Enhancing self-regulated learning and learning experience in generative AI environments: The critical role of metacognitive support. British Journal of Educational Technology, 0(0). https://doi.org/10.1111/bjet.13599… Read the rest