Categories
Language Development Primary School Education

Exploring the impact of a GenAI-supported writing program on primary students’ writing motivation, engagement, and performance: A mixed methods study

The integration of Generative Artificial Intelligence (GenAI) into second language (L2) writing has attracted growing interest, yet its value in primary education remains unclear. Lu et al. (2026) used a mixed-methods design with 301 Grade 5 and Grade 6 students from a primary school in Eastern China. Eight classes were randomly assigned to a GenAI-supported writing group or a conventional writing control group. Writing motivation, engagement, and performance were measured at pre-test (Week 1) and post-test (Week 9), and 12 experimental-group students were interviewed.

MANCOVA showed significant multivariate group effects on writing motivation (F(3, 294) = 3.067, p = .028, partial η² = 0.030), engagement (F(4, 292) = 8.392, p < .001, partial η² = 0.103) and performance (F(3, 294) = 2.841, p = .038, partial η² = 0.028). The experimental group scored higher on ideal L2 writing self, academic buoyancy, behavioral and emotional engagement, and language use, while opinion and content was only marginal (p = .054). There were no group differences in growth mindset, cognitive or metacognitive engagement, organization, or total scores. Paired t-tests showed small gains in the experimental group for ideal L2 writing self (d = 0.26), academic buoyancy (d = 0.24), behavioral engagement (d = 0.22), emotional engagement (d = 0.24), language use (d = 0.27) and overall writing performance (d = 0.28), but not cognitive (d = 0.12) or metacognitive (d = 0.07) engagement. The control group showed no significant gains after Bonferroni correction. Interviews highlighted GenAI’s multifaceted roles in shaping these outcomes. Although effect sizes were small, the study suggests GenAI can support motivation, engagement, and performance in primary-level L2 writing.

Teachers should position GenAI as a supportive tool rather than a substitute for students’ thinking, using activities that emphasize challenge, effort, and reflection to reduce over-reliance. Given the limited gains in cognitive and metacognitive engagement, explicit instruction in self-regulated writing strategies (planning, monitoring, and revision) is needed, supported by teacher modeling and student reflection. Collaborative GenAI activities should be structured so that students generate ideas independently, critically evaluate AI suggestions, and decide how to use them.

Source (Open Access): Lu, Q., Wang, M., Yao, Y., Zhu, X., Xiao, L., & Yin, H. (2026). Exploring the impact of a GenAI-supported writing program on primary students’ writing motivation, engagement, and performance: A mixed methods study. Learning and Instruction, 106, 102451.

https://doi.org/10.1016/j.learninstruc.2026.102451… Read the rest

Categories
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

Categories
Primary School Education Social and Motivational Outcomes

The Effects of Interventions for Promoting Elementary School Students’ Engagement

Goulet and colleagues conducted a systematic review and three-level meta-analysis to examine the effects of different intervention strategies on elementary school students’ engagement and to compare which approaches were most effective in promoting behavioral, affective, and cognitive engagement. Following PRISMA guidelines, the study included 53 studies covering 46 interventions and a total of 38,225 students. Of these interventions, 39 used randomized controlled trials and 7 used quasi-experimental designs. The interventions were classified into three main categories: teacher support, mainly involving teacher professional development; student-directed interventions, which directly targeted students; and multimodal interventions, which simultaneously supported students and their family or school environments. The meta-analysis used a three-level random-effects model with robust variance estimation to account for dependencies among multiple effect sizes within the same study, and methodological bias was further examined as a moderator.

The results showed that interventions had the most consistent positive effect on elementary students’ behavioral engagement, with an overall effect size of g = .386. All three intervention types produced significant effects. Teacher support showed the largest effect (g = .503, 95% CI [.105, .901]), followed by multimodal interventions (g = .385, 95% CI [.202, .569]) and student-directed interventions (g = .370, 95% CI [.198, .542]), although there were no significant differences among the three approaches (F = 0.22, p = .808). Further comparisons among student-directed strategies showed that learning strategy development had the largest effect (g = .801, 95% CI [.318, 1.285]) and was significantly more effective than social-emotional learning (SEL; g = .171, 95% CI [.027, .314]; p = .003). Classroom physical activity also showed a relatively large effect (g = .478), but its confidence interval included zero (95% CI [–.100, 1.056]), indicating that the result was less stable.

Overall, the study indicates that elementary school students’ engagement is malleable, but different dimensions of engagement respond differently to intervention. The strongest evidence currently concerns behavioral engagement. In particular, improving teachers’ instructional and classroom management practices through workshops, coaching, feedback, and reflective activities, as well as directly developing students’ learning strategies such as planning, organization, self-monitoring, and reflection, appear especially promising. In comparison, interventions focusing only on students’ social-emotional skills have more limited effects, while the evidence for physical activity remains uncertain. The authors therefore suggest that schools prioritize investment in teacher professional development and student learning strategy training when designing engagement interventions, while avoiding the assumption that a single approach can improve behavioral, affective, and cognitive engagement equally. Future research should also include more measures of affective and cognitive engagement and adopt higher-quality research designs to build a stronger evidence base.

Source (Open Access): Goulet, J., Archambault, I., Maltais, C., Gilbert-Blanchard, O., & Janosz, M. (2026). Promoting engagement in elementary school: A systematic review and meta-analysis of intervention effects. Educational Psychology Review, 38(1), 107.

https://doi.org/10.1007/s10648-026-10204-w… Read the rest

Categories
Educational Administration and Leadership Higher Education

International student mobility and retention: evidence from discrete choice experiments

A recent quantitative study by Peeters and De Witte investigates the determinants of international students’ university choices and their decisions to remain in the host country after graduation, focusing on the Flemish region of Belgium.

This study draws on 652 usable survey responses from international students at KU Leuven, collected between February 10 and March 19, 2025, after removing 96 Belgian respondents from an initial 748 completed surveys. Roughly 15% of respondents were reached through digital channels and 85% through lecturing moments. The authors conducted two discrete choice experiments (DCEs): Deck 1 on university choice (7,824 choice observations) and Deck 2 on the post-graduation stay decision (11,736 observations), plus an embedded information experiment on perceived English use in Brussels firms.

Variables were operationalized as choice attributes. Deck 1 comprised tuition fee, university ranking, proximity to home, labor prospects, and guaranteed accommodation; Deck 2 comprised gross salary, ease of obtaining a visa or work permit, ease of social integration, employment opportunities, and quality of life. Intention-to-stay was captured through a direct survey question and linked to a visa-requiring dummy and GDP per capita. Data were analyzed using conditional and mixed logit models, willingness-to-pay measures, latent class analysis, and subgroup analysis.

The findings show that tuition fees are the most influential determinant of university choice, with students willing to forgo up to €8,597 in annual fees for a higher-ranked institution (range €4,260 to €8,597, versus €2,989 for many employment opportunities). Easing immigration procedures raised stay probability by 11.62 to 23.99 percentage points, alongside substantial preference heterogeneity across student groups. Students would give up to €761 in gross monthly salary for an easier visa process and €754 for high quality of life. Visa-requiring students had a 9 to 10 percentage-point higher intention to stay, and each €10,000 of home-country GDP per capita lowered it by 1.92 percentage points. Master’s students were 9 to 10.2 percentage points more likely to intend to stay than Bachelor’s students, and students with a student or part-time job were 4.75 to 8.12 percentage points more likely. Latent class analysis found two groups for university choice (55% cost-conscious and 45% career-oriented) and two for staying (52% quality-of-life seekers and 33% instrumental optimizers). Fast-track visas for skilled graduates were estimated to yield €27.7 to €83.0 million a year for the Flemish economy, and better labour market conditions €40.4 to €172.7 million. This study offers actionable insights for policymakers seeking to attract and retain international talent.

Source: Peeters, E., & De Witte, K. (2026). International student mobility and retention: evidence from discrete choice experiments. Studies in Higher Education, 1-18.

https://doi.org/10.1080/03075079.2026.2692621… Read the rest

Categories
Effective Teaching Approach Secondary School Education

When AR Makes Learning Enjoyable but Not Necessarily More Effective

Olea-Ibarra et al. (2025) examine how augmented reality (AR) affects middle school students’ enjoyment, epistemic emotions, and knowledge acquisition in science learning. Although AR is often promoted as an immersive tool that can make abstract content more engaging, the study addresses an important unresolved issue: whether greater enjoyment and emotional stimulation during AR learning actually translate into stronger learning outcomes, especially for younger learners. The study focuses on seventh-grade students learning about the solar system and compares AR-based learning with a non-immersive static multimedia condition.

Using a quasi-experimental field design, the study involved 47 seventh-grade students from an international middle school in Mexico. Students were assigned to either an AR condition or a control condition by class group. In the AR condition, students used the Jigspace app on tablets to spatially explore solar system content, including planets’ positions and characteristics. In the control condition, students learned the same content through an interactive PowerPoint presentation with static images and text on tablets. Knowledge was assessed through pre- and post-tests, while enjoyment and epistemic emotions were measured repeatedly before, during, and after the learning activity.

Results show a mixed and somewhat counterintuitive pattern. Students in the AR condition reported significantly higher enjoyment than those in the control condition. The averaged enjoyment scores during and after learning were higher in the AR group (M = 3.86, SD = 0.84) than in the control group (M = 2.84, SD = 0.66), with a large effect size (d = 1.37). AR also elicited stronger positive epistemic emotions, including surprise (d = 1.09), curiosity (d = 1.05), and excitement (d = 1.07). However, the control group achieved greater knowledge gains than the AR group. Knowledge gain was substantially higher in the control condition (M = 12.20%, SD = 6.90) than in the AR condition (M = 4.66%, SD = 6.41), with a large effect size (d = 1.13). A repeated measures ANOVA further showed a significant interaction between condition and measurement time, indicating that knowledge gain reached statistical significance only in the control condition, F (1,45) = 15.049, p < .001. The mediation analysis also indicated that enjoyment did not significantly mediate the relationship between AR use and learning gains (b = 1.857, SE = 1.356, p = .171). These findings suggest that while AR can make learning more emotionally engaging, this affective advantage does not automatically lead to better knowledge acquisition.

Overall, the study provides a valuable cautionary perspective on immersive technologies in school science learning. AR may enhance enjoyment and stimulate positive epistemic emotions, but it may also distract learners or create affective overload when emotional engagement competes with task-focused cognitive processing. The findings highlight the need to design AR learning environments that carefully balance immersion, emotion, and instructional focus. Rather than assuming that more engaging media will automatically improve learning, the study suggests that educators should use AR with clear pedagogical guidance, especially when working with younger learners.

Source (Open Access): Olea‐Ibarra, D., Hartmann, C., & Bannert, M. (2025). The role of enjoyment and epistemic emotions in middle school AR learning: A quasi‐experimental field study. Journal of Computer Assisted Learning, 41(2), e70016.

https://doi.org/10.1111/jcal.70016… Read the rest

Categories
Higher Education Social and Motivational Outcomes

Unveiling the hidden key: can help-seeking behaviors bridge the gap between doctoral supervision relationship and research self-efficacy of PhD candidates?

A recent cross-sectional study by Jeanne Boisselier examines the relationship between the Doctoral Supervision Relationship (DSR) and Research Self-Efficacy (RSE) among PhD candidates, with a particular focus on the mediating role of Help-seeking behaviors. Specifically, the study investigates whether adaptive and non-adaptive help-seeking act as mechanisms that bridge the gap between supervisory support and students’ confidence in their research abilities. The sample comprised 178 PhD candidates enrolled in French universities and research institutes.

The authors employed ANOVAs, linear regressions, and mediation analyses to examine the associations among the study variables. Key constructs were measured using multi-dimensional 5-point Likert scales. The DSR was assessed across five dimensions: Autonomy Support, Competence Support, Relatedness Support, Communication Quality, and Supervisor Involvement. Help-seeking behaviors were categorized as Adaptive Help-seeking, reflecting proactive strategies to gain mastery, and Non-adaptive Help-seeking, reflecting avoidance of support due to perceived threats to self-esteem. Research Self-Efficacy was operationalized as students’ subjective belief in their ability to perform research-related tasks and processes successfully.

Results indicate that all five dimensions of the DSR significantly and positively predict RSE: autonomy support (β = .20, p < .01), competence support (β = .27, p < .01), relatedness support (β = .29, p < .01), communication quality (β = .31, p < .01), and supervisor involvement (β = .28, p < .01). Mediation analyses reveal that Adaptive Help-seeking partially mediates the relationship between DSR and RSE and fully mediates the effect of Autonomy Support. In contrast, Non-adaptive Help-seeking functions as a negative mediator: effective supervision reduces avoidant behaviors, thereby preventing a decline in research confidence. Despite the significance of these behavioral pathways, the direct influence of supervisory support on RSE remains stronger, emphasizing the primary importance of the quality of the supervision relationship itself.

The findings highlight the need for supervisors and institutions to attend to the multifaceted nature of doctoral guidance, particularly by enhancing communication quality and autonomy support to encourage adaptive help-seeking among students.

Source (Open Access): Boisselier, J. (2026). Unveiling the hidden key: can help-seeking behaviors bridge the gap between doctoral supervision relationship and research self-efficacy of PhD candidates? Higher Education Research & Development, 45(1), 33-48.

https://doi.org/10.1080/07294360.2025.2526816… Read the rest