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  • Assessing academic integrity patterns among pre-service teachers using AI-based plagiarism detection

    Irish Arroyo1, Sittie Nor Aisha Rusiana2, Daniere Maryje Tabada3, Kim Silva4, Carla Marie Rubio5, Esmeraldo Sarad6, Cathy Mae Toquero7
    1. College of Education, Mindanao State University-General Santos , PH
    2. College of Education, Mindanao State University-General Santos , PH
    3. College of Education, Mindanao State University-General Santos , PH
    4. College of Education, Mindanao State University-General Santos , PH
    5. College of Education, Mindanao State University-General Santos , PH
    6. College of Education, Mindanao State University-General Santos , PH
    7. Faculty of Psychology, Malang State University , ID
    2026-04-06
    doi https://doi.org/10.56003/jaieli.v1i2.685
    Published Online: 06 April 2026
    |
    page 148-163
    Abstract

    Background: The advent of artificial intelligence has intensified concerns about academic dishonesty among students, particularly in written outputs. Plagiarism, a common form of misconduct, involves using others’ ideas without proper attribution.

    Objectives: This study aimed to determine the degree and patterns of academic integrity in the pre-service teachers’ book reviews.

    Methods: Employing a descriptive research design through document analysis, the study used purposive sampling to collect 40 book reviews, applying set inclusion and exclusion criteria. An AI-based plagiarism detection tool, Grammarly, was used to identify instances of plagiarism and assess the level of academic integrity reflected in the outputs. Descriptive statistical methods were applied to examine plagiarism levels across different sections of the book reviews.

    Results: Results showed that the majority of pre-service teachers demonstrated a very high level of academic integrity in their book reviews, scoring 97% or interpreted as students committing 3% plagiarism. Furthermore, sectional analysis showed that the introduction and conclusion exhibited higher integrity, while the body contained the most instances of plagiarism. This suggests that students struggled more with sections requiring critical thinking, original insights, and proper citation. Most plagiarism cases were linked to failure to cite sources and unintentional misuse of references.

    Conclusions: Teacher Education Institutions integrate AI-supported evaluation tools and plagiarism detection systems into instruction and assessment. Embedding academic integrity modules and discussions on AI ethics is also encouraged. Future research should involve larger and more diverse samples and utilize multiple AI detection tools to enhance the reliability and validity of findings through cross-verification.

    APA Citation Style
    Arroyo, I. , Rusiana, S. N. A. , Tabada, D. M. , Silva, K. , Rubio, C. M. , Sarad, E. , & Toquero, C. M. (2026). Assessing academic integrity patterns among pre-service teachers using AI-based plagiarism detection. Journal of Artificial Intelligence in Education & Learning Innovation, 148-163. https://doi.org/10.56003/jaieli.v1i2.685

  • AI-driven learning in physical education: A bibliometric analysis of trends, knowledge structure, and future research directions

    Amelia Larassary1
    1. State Junior High School 1 Bunguran Tengah, Natuna Regency, Riau Islands , ID
    2026-04-06
    doi https://doi.org/10.56003/jaieli.v1i2.673
    Published Online: 06 April 2026
    |
    page 102-118
    Abstract

    Background: Although research on AI-driven learning in PE has expanded rapidly, existing studies remain fragmented across disciplines, journals, and methodological approaches, limiting a comprehensive understanding of the field's development and intellectual structure.

    Objectives: This study aimed to systematically map the evolution, influential contributors, intellectual structure, dominant themes, and future research directions of AI-driven learning research in physical education from a bibliometric perspective.

    Methods: A quantitative bibliometric analysis was conducted using data retrieved exclusively from the Scopus database. A total of 284 eligible documents published between 2020 and early 2026 were analyzed. Descriptive statistics were applied to examine publication trends, authorship, sources, institutions, and country contributions. Network and thematic analyses were performed using VOSviewer (version 1.6.20) and the Bibliometrix package in R to identify co-authorship patterns, keyword co-occurrence networks, and thematic clusters.

    Results: The results indicate a sharp growth in AI-driven learning research in physical education after 2022, with publication output increasing more than fivefold from 2020 to 2025. China emerged as the leading contributing country, accounting for nearly half of the total publications, while institutional productivity was concentrated in several Russian universities. Keyword co-occurrence analysis revealed five major thematic clusters shaping the intellectual structure of the field, integrating pedagogical frameworks, computational intelligence, institutional contexts, and physical training models. Dominant research themes centered on pedagogical design, student engagement, adaptive learning systems, and the integration of educational technology. Emerging themes included virtual reality, advanced machine learning techniques, and immersive learning environments.

    Conclusions: This study provides a structured and quantitative overview of AI-driven learning research in physical education, highlighting its interdisciplinary nature and rapid expansion.
    APA Citation Style
    Larassary, A. (2026). AI-driven learning in physical education: A bibliometric analysis of trends, knowledge structure, and future research directions. Journal of Artificial Intelligence in Education & Learning Innovation, 102-118. https://doi.org/10.56003/jaieli.v1i2.673

  • 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 Plakias4
    1. Department of Sport Education, Faculty of Sports Science, Universitas Negeri Semarang , ID
    2. Department of Sport Education, Faculty of Sports Science, Universitas Negeri Semarang , ID
    3. Department of Education, The Catholic University of the Sacred Heart in Milan, Italy , IT
    4. Department of Physical Education and Sport Science, University of Thessaly , GR
    2026-08-30
    doi https://doi.org/10.56003/jaieli.v2i1.753
    Published Online: 30 August 2026
    |
    page 44-71
    Abstract

    Background: 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 Style
    Hanief, 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

  • Themes and values in pre-service teachers’ digital storybooks: A content analysis for elementary English education

    Najma Macatanto1, Desiery Maghinay2, John Dale Pendon3, Carla Marie Rubio4, Esmeraldo Sarad5, Kim Silva6
    1. College of Education, Mindanao State University-General Santos , PH
    2. College of Education, Mindanao State University-General Santos , PH
    3. College of Education, Mindanao State University-General Santos , PH
    4. College of Education, Mindanao State University-General Santos , PH
    5. College of Education, Mindanao State University-General Santos , PH
    6. College of Education, Mindanao State University-General Santos , PH
    2026-07-02
    doi https://doi.org/10.56003/jaieli.v2i1.701
    Published Online: 02 July 2026
    |
    page 32-43
    Abstract

    Background: Digital storytelling has emerged as an innovative pedagogical approach that promotes meaningful learning experiences and enhances student engagement.

    Objectives: This study examined the themes and values embedded in digital storybooks created by pre-service teachers and explored their implications for teaching English in elementary education.

    Methods: Using a qualitative content analysis design, ten digital storybooks were purposively selected based on predetermined inclusion criteria. Data were analyzed through directed content analysis using an adapted thematic analysis framework.

    Results: Findings revealed nine major themes. The most prominent were exploration-related themes (3 out of 10 storybooks, 30%) and family-sharing themes (3 out of 10 storybooks, 30%), followed by community-oriented themes (2 out of 10 storybooks, 20%). Other themes included parent-child communication and financial responsibility. The digital storybooks also embodied core values such as respect, compassion, humility, responsibility, tolerance, forgiveness, and commitment to the common good.

    Conclusions: These findings suggest that digital storybooks can serve as effective resources for integrating language development, values education, and socio-emotional learning in elementary English classrooms. The study is limited by its small sample size and context-specific dataset. Further research should include a larger dataset and cross-cultural analysis to strengthen the generalizability of findings.

    APA Citation Style
    Macatanto, N. , Maghinay, D. , Pendon, J. D. , Rubio, C. M. , Sarad, E. , & Silva, K. (2026). Themes and values in pre-service teachers’ digital storybooks: A content analysis for elementary English education. Journal of Artificial Intelligence in Education & Learning Innovation, 32-43. https://doi.org/10.56003/jaieli.v2i1.701
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