Tag: Artificial Intelligence

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Higher Education

Can AI feedback stand alone for fostering students’ presentation performance

A randomized controlled study by Sichterman and colleagues examined whether AI feedback could support students’ presentation performance without teacher interpretation. Sixty students in Dutch higher education were block-randomized to AI-only feedback (n = 30) or teacher-supported AI feedback (n = 30) after one five-minute rehearsal in an immersive virtual reality (IVR) classroom.

Both groups prepared the same five-minute talk and practised it before five virtual audience members. The system generated feedback on speaking pace, volume, intonation, and eye contact. The AI-only group reviewed the automated graphs and messages independently in IVR; the comparison group received the same AI data interpreted verbally by a trained teacher in a five-minute face-to-face session. Students then repeated the talk before a live audience. Two blinded experts rated six presentation skills, while questionnaires measured presentation perceptions, feedback utility, and anxiety.

No statistically significant between-group differences were detected in rated presentation skills (M = 7.06 vs. 7.50, p = .11), perceptions of the presentation activity (M = 3.57 vs. 3.40, p = .46), presentation anxiety (condition p = .67; condition-by-time p = .21), or perceived feedback utility (p = .12). Anxiety fell across time in both groups (p < .001), from 2.86 to 2.43 in the AI-only group and from 2.83 to 2.63 in the teacher-supported group. Under these conditions, teacher interpretation produced no statistically detectable short-term advantage.

The authors propose using structured AI feedback for formative practice of measurable non-verbal behaviours, leaving teachers to address content, structure, and argumentation. The study did not test statistical equivalence, so the absence of significant differences does not show that teacher support is unnecessary. It also lacked a no-feedback or practice-only group, measured most outcomes only after the intervention, involved one session and a small, concentrated sample, and did not blind participants. Larger longitudinal and cross-context studies are needed before drawing causal or general conclusions.

Source (Open Access): Sichterman, B., Banihashem, S. K., Verstappen, M., Schipper, M., Waisvisz, P., Noroozi, O., & van Ginkel, S. (2026). Can AI feedback stand alone for fostering students’ presentation performance? A comparison of AI-only versus teacher-supported AI feedback. Interactive Learning Environments, 34(3), 1280-1297.

https://doi.org/10.1080/10494820.2025.2521338… Read the rest

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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

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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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Effective Teaching Approach Higher Education

Supporting self-regulated learning through generative AI feedback in online higher education

A recent mixed-methods study by Yilmaz and colleagues examined whether feedback generated by artificial intelligence can help university students develop self-regulated learning skills (SRLs) in an online course, and how students’ perceptions of the feedback source shape their attitudes. The study was conducted within a nine-week distance-learning “Basic Statistics” module and involved 46 higher education students who were randomly and blindly assigned to either a GenAI feedback group (n = 23) or a tutor feedback group (n = 23), with participants unaware of which type of feedback they were receiving during the intervention itself.

The researchers drew on Nazaretsky and colleagues’ (2024) four-dimension feedback perception framework, covering objectivity, usefulness, genuineness, and provider credibility, together with Barnard and colleagues’ (2009) six-dimension SRL model, comprising goal setting, task strategies, environment structuring, time management, help-seeking, and self-evaluation. SRL was measured using trace-based indicators adapted from Ye and Pennisi’s (2022) framework, derived from roughly 48,000 MOODLE log records that were mined, standardised, and mapped onto SRL proxy scores before and after two feedback interventions. GenAI feedback was produced using GPT-4 through structured prompts fed with each student’s individual proxy z-scores, while tutor feedback was generated using a purpose-built support tool named RefleXED, based on the same underlying data.

The results showed that students rated GenAI-generated feedback more favourably than tutor-generated feedback across every perception dimension, with a statistically significant advantage found specifically for Genuineness (p = .036, r = .36). In terms of SRL development, the GenAI feedback group demonstrated a significant improvement in the Task Strategies dimension (p = .044, r = .35), and a near-significant trend emerged for Time Management (p = .057, r = .33), while no significant between-group differences were found for the remaining SRL dimensions. Qualitative analysis of open-ended responses from 17 treatment-group students revealed considerable variation in awareness of the feedback source: students who recognised the feedback as AI-generated generally reported no change in attitude, prioritising content quality over provenance, whereas a smaller number of unaware students indicated their views might have shifted had they known the source in advance.

The findings suggest that carefully designed, learning-analytics-informed GenAI feedback holds real potential to scale personalised support for self-regulation in online higher education, but the researchers caution against viewing GenAI as a replacement for tutors. Instead, they argue for a complementary model in which GenAI’s scalability and adaptability work alongside tutors’ pedagogical judgement, while institutions remain attentive to how students’ awareness and perceptions of the feedback source can influence its ultimate impact. The authors note that the modest sample size (n = 46) drawn from a single course limits generalisability, and they call for future research involving larger, more diverse cohorts, no-feedback control conditions, and longer-term tracking of SRL outcomes.

Source (Open Access): Yilmaz, M., Temur, H. B., Emmungil, L., Çelik, E., Gauthier, A., & Cukurova, M. (2026). Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback. International Journal of Educational Technology in Higher Education, 23(1), 16.

https://doi.org/10.1186/s41239-026-00592-y… Read the rest

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Higher Education Language Development

Task Offloading to GenAI in the Writing Feedback Process and Its Effects on Writing Development

Lai and colleagues used a quasi-experimental design to investigate whether offloading different tasks to GenAI in the “draft–assess–revise” workflow affects English-as-a-foreign-language (EFL) students’ writing development. Conducted over seven weeks, the study involved 101 Chinese university students and compared three conditions: (1) GenAI drafting, student assessment, student revision; (2) student drafting, GenAI assessment, student revision; and (3) student drafting, student self-assessment, student revision.

Results showed that the GenAI drafting group performed best in writing ability (M = 18.21, SD = 2.21), significantly higher than both the GenAI assessment group (M = 16.30, SD = 1.70) and the no GenAI group (M = 15.15, SD = 1.79). This indicates that including GenAI in the feedback workflow generally benefits writing, but offloading the drafting task rather than the assessment task produces the strongest effects.

Regarding cognitive processes, the GenAI drafting group showed significantly higher cognitive engagement, utilizing more total prompts (median 20.50 vs. 9.00, U = 195, p < .001) and more learning-oriented prompts (median 10.50 vs. 8.00, U = 398.50, p < .05) than the assessment group. They also outperformed the other two groups in metacognition in problem identification (M = 4.41), argumentation (M = 2.82), and providing constructive feedback (M = 4.97) during peer review.

In writing self-efficacy, the GenAI drafting group demonstrated the largest improvement (M = 4.74, SD = .55), significantly higher than the other groups, whereas the GenAI assessment group (M = 4.42, SD = .53) and no GenAI group (M = 4.45, SD = .78) did not differ significantly. Interviews revealed these students experienced a stronger sense of control and competence because they could critically evaluate and revise AI-generated drafts. In contrast, some students in the GenAI assessment group worried about overreliance on AI feedback, leading to “false confidence.”

Overall, the study highlights that the educational value of GenAI in writing feedback depends on which tasks are offloaded to AI. Offloading the drafting task to GenAI while having students handle assessment and revision promotes deeper cognitive engagement, stronger metacognitive understanding, and higher self-efficacy. The authors recommend that writing instruction should prioritize AI-generated drafts paired with student evaluation and revision, serving as a catalyst for critique and reflection rather than replacing student thinking.

Source (Open Access): Lai, C., Pan, M., Guo, K., & Cui, Y. (2026). What task to offload to GenAI in the writing feedback process? – Effects of task-offloading approaches on EFL learners’ writing skill development. Computers & Education, 252, 105675.

https://doi.org/10.1016/j.compedu.2026.105675… 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