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Artificial intelligence-supported motor skill performance in physical education and sport: A systematic review and meta-analysis informed by motor learning theory
Yulingga Nanda Hanief1, Vera Septi Sistiasih2, Ferdinando Cereda3, Spyridon Plakias41. Department of Sport Education, Faculty of Sports Science, Universitas Negeri Semarang , ID2. Department of Sport Education, Faculty of Sports Science, Universitas Negeri Semarang , ID3. Department of Education, The Catholic University of the Sacred Heart in Milan, Italy , IT4. Department of Physical Education and Sport Science, University of Thessaly , GR2026-08-30|page 44-71AbstractBackground: Artificial Intelligence (AI) has increasingly been integrated into physical education (PE) to support motor learning through technologies such as computer vision, motion tracking, intelligent tutoring systems, virtual reality, and generative AI. However, evidence regarding its effectiveness remains fragmented across different intervention types and learning contexts.
Objectives: This study aimed to evaluate the effects of AI-supported interventions on motor skill performance in physical education and sport settings and to synthesize complementary learning-related outcomes.
Methods: A systematic review and meta-analysis were conducted in accordance with PRISMA 2020. Scopus, PubMed, and ProQuest were searched through 22 June 2026. Eligible studies evaluated AI-supported, adaptive, or intelligent interventions in physical education, sport, or motor skill-learning contexts and reported learner-level motor performance; complementary learning-related outcomes were synthesized narratively. Risk of bias was assessed using RoB 2 and ROBINS-I, and standardized mean differences (SMDs) with 95% confidence intervals (CIs) were synthesized using a random-effects model.
Results: Fifteen studies were included in the qualitative synthesis; six contributed to the meta-analysis and nine were synthesized narratively. The pooled estimate favored the designated AI-supported experimental conditions over their comparators (SMD = 3.15, 95% CI 1.86–4.44; p < .001; I² = 98%). Given the small evidence base, very high heterogeneity, and variable risk of bias, the magnitude of this pooled effect is uncertain. Qualitative findings concerned short-term motor skill performance and complementary outcomes such as engagement, motivation, learning interest, and self-directed learning.
Conclusions: AI-supported interventions may improve short-term motor skill performance in some physical education and sport contexts and may support complementary learning-related outcomes. However, the evidence is preliminary and highly heterogeneous, and immediate post-intervention performance should not be interpreted as definitive evidence of durable motor learning, retention, or transfer. AI should be considered a complementary pedagogical tool rather than a replacement for teacher or coach expertise.
APA Citation StyleHanief, Y. N. , Sistiasih, V. S. , Cereda, F. , & Plakias, S. (2026). Artificial intelligence-supported motor skill performance in physical education and sport: A systematic review and meta-analysis informed by motor learning theory. Journal of Artificial Intelligence in Education & Learning Innovation, 44-71. https://doi.org/10.56003/jaieli.v2i1.753 -
Effects of Chat-GPT (Generative Pre-Trained Transformer) in the Writing Skills of Undergraduate Students: A Scoping Review
Hamdoni Pangandaman1, Mirella Veras2, Samiel Macalaba3, Abolbashar Mangontawar4, Sittie Ainah Mai-Alauya5, Saliha Janine Gubaten6, Maslainie Aliola7, Norhanie Lininding8, Paida Abdulmalik9, Sittie Hainieh Taratingan101. Mindanao State University, Marawi City , PH2. Department of Physical Therapy, College of Rehabilitation Sciences, Rady Faculty of Health Sciences, University of Manitoba , CA3. Mindanao State University, Main Campus, Marawi City , PH4. Mindanao State University, Marawi City , PH5. Mindanao State University, Marawi City , PH6. Mindanao State University, Marawi City , PH7. Mindanao State University, Marawi City , PH8. Mindanao State University, Marawi City , PH9. Mindanao State University, Marawi City , PH10. Mindanao State University, Marawi City , PH2026-07-02|page 14-31AbstractBackground: The integration of artificial intelligence (AI) in academic settings has gained increasing attention, particularly in improving students' writing skills. ChatGPT, a generative AI tool, has been explored as a potential aid in academic writing, providing instant feedback and enhancing students' ability to structure, revise, and refine their written work. However, there remains a need to systematically assess its effectiveness in undergraduate education
Objectives: This study aimed to evaluate the impact of ChatGPT on the writing skills of undergraduate students through a scoping review, analyzing its effectiveness in enhancing grammar, vocabulary, coherence, and overall writing proficiency.
Methods: A scoping review methodology was employed to identify relevant studies published between 2020 and 2024. A comprehensive search was conducted across seven academic databases: ScienceDirect, Scopus, ProQuest, EBSCOhost, Sage Journals, Taylor & Francis, and PubMed, using Boolean operators and predefined inclusion criteria. The studies were appraised using the Joanna Briggs Institute (JBI) Critical Appraisal Tool for quasi-experimental research and the ROBVIS checklist for randomized controlled trials (RCTs) to assess the risk of bias. The synthesis was conducted following the Synthesis Without Meta-Analysis (SWiM) guidelines, with PRISMA used for article selection and appraisal.
Results: A total of nine studies were included in the review, conducted in Saudi Arabia, China, Ecuador, Indonesia, Iran, Pakistan, and India, involving a total of 839 undergraduate students. The duration of interventions varied, with most lasting between 10 to 16 weeks. The overall risk of bias was low, indicating strong methodological reliability. The findings revealed that ChatGPT significantly improved students' writing proficiency, particularly in areas of grammatical accuracy, vocabulary use, organization, and argument structure. Also, students using ChatGPT exhibited increased engagement and motivation in writing tasks compared to traditional methods. However, some studies cautioned against over-reliance on AI-generated text and raised concerns about academic integrity and originality.
Conclusion: ChatGPT demonstrates promising potential as an AI-assisted writing tool, offering immediate feedback, structured learning support, and improved writing outcomes for undergraduate students. While its effectiveness is evident, further research is needed to establish best practices for integrating AI into academic writing instruction, ensuring that students develop critical thinking and independent writing skills while leveraging AI’s benefits responsibly.
APA Citation StylePangandaman, H. , Veras, M. , Macalaba, S. , Mangontawar, A. , Mai-Alauya, S. A. , Gubaten, S. J. , Aliola, M. , Lininding, N. , Abdulmalik, P. , & Taratingan, S. H. (2026). Effects of Chat-GPT (Generative Pre-Trained Transformer) in the Writing Skills of Undergraduate Students: A Scoping Review. Journal of Artificial Intelligence in Education & Learning Innovation, 14-31. https://doi.org/10.56003/jaieli.v2i1.712 -
Factors influencing teachers’ attrition in public basic schools in Techiman Municipality, Ghana: An embedded mixed methods study
Joseph Kyeremeh1, Dickson Adom2, Michael Ato Essuman3, Lim Seon Pek41. Kwame Nkrumah University of Science and Technology , GH2. Kwame Nkrumah University of Science and Technology , GH3. Kwame Nkrumah University of Science and Technology , GH4. INTI International University , MY2025-10-08|page 66-88AbstractBackground: The high teacher attrition rate in public primary schools in Techiman, Ghana, has become a serious problem that directly impacts the quality of education and the achievement of national education development goals.
Objectives: The study investigated factors that influence teachers' attrition in the Techiman Municipality.
Methods: One hundred ninety-seven teachers were sampled for the study using convenience and simple random sampling techniques. This study utilized the embedded mixed methods design with descriptive survey and phenomenology as research methods to collect quantitative and qualitative datasets using questionnaires and interviews as data collection tools. The qualitative data were analyzed using thematic analysis, while the quantitative data were analyzed using descriptive and inferential statistics using the SPSS software.
Results: It was found that teachers perceived salary as the major employer-related factor in teachers' attrition (relative importance index (RII) = 0.85). team member-related factors, such as the willingness of teachers to pursue another career outside education, were perceived as the primary factor of attrition (RII = 0.78). In contrast, an external-related factor, such as better opportunities in some other occupations, was ranked the first major contributor to teachers' attrition (RII = 0.88). The overall assessment of the factors of attrition based on the relative importance index revealed that Employer-related factors are the major contributor to teachers' attrition (RII = 0.70).
Conclusions: The study recommends that the Ghana government find innovative ways of increasing teachers' salaries as a source of motivation and offer them other incentive packages to aid in retaining them in the service.
APA Citation StyleKyeremeh, J. , Adom, D. , Essuman, M. A. , & Pek, L. S. (2025). Factors influencing teachers’ attrition in public basic schools in Techiman Municipality, Ghana: An embedded mixed methods study. Journal of Artificial Intelligence in Education & Learning Innovation, 66-88. https://doi.org/10.56003/jaieli.v1i1.578 -
Sports technology acceptance as a predictor of academic commitment among undergraduate physical education students in China
Yingxue Su11. Inner Normal Mongolia University , CN2026-09-08|page 72-84AbstractBackground: Sports analytics and digital learning technologies are increasingly incorporated into physical education and sport-related higher education. However, evidence regarding the relationship between students’ acceptance of sports-related technologies and their academic commitment remains limited, particularly among undergraduate Physical Education students in China.
Objectives: This study aimed to examine the association between Sports Technology Acceptance (STA) and Academic Commitment and to determine whether STA statistically predicts Academic Commitment within a cross-sectional structural model among undergraduate Physical Education students.
Methods: This quantitative study employed a cross-sectional correlational design involving 150 undergraduate students enrolled in the Bachelor of Physical Education program at Inner Mongolia Normal University, China, who were recruited using convenience sampling. Data were collected using the Higher Education Student Commitment Scale and an adapted Technology Use Tendency Scale in the Classroom to assess STA. Descriptive statistics, Pearson correlation analysis, and structural equation modeling (SEM) were performed using IBM SPSS Statistics 26 and IBM SPSS Amos 24.
Results: The mean Academic Commitment score was 3.68 (SD = 0.71), while the mean STA score was 4.01 (SD = 0.65). STA was moderately and positively correlated with Academic Commitment (r = .47, p < .01). The structural model showed a significant positive path from STA to Academic Commitment (β = .49, t = 6.81, p < .001), accounting for 24% of the variance in Academic Commitment (R² = .24). The reported model-fit indices indicated acceptable fit (CFI = .956, RMSEA = .057).
Conclusions: Sports Technology Acceptance was moderately and positively associated with Academic Commitment among undergraduate Physical Education students at one university in China. Within the cross-sectional structural model, STA accounted for 24% of the variance in Academic Commitment; therefore, the observed statistical prediction should not be interpreted as evidence of temporal or causal effects.
APA Citation StyleSu, Y. (2026). Sports technology acceptance as a predictor of academic commitment among undergraduate physical education students in China. Journal of Artificial Intelligence in Education & Learning Innovation, 72-84. https://doi.org/10.56003/jaieli.v2i1.748
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