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Journal of Artificial Intelligence in Education & Learning Innovation (JAIELI)
Published by CV Rezki Media
ISSN : -     EISSN : 31100856     DOI : https://doi.org/10.56003/jaieli
Core Subject : Science, Education,
Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics
The Journal of Artificial Intelligence in Education & Learning Innovation (JAIELI) publishes papers concerned with the application of AI to education and learning innovation. It aims to help the development of principles for the design of computer-based learning systems. Its premise is that such principles involve the modeling and representation of relevant aspects of knowledge, before implementation or during execution, and hence require the application of AI techniques and concepts. JAIELI has a very broad notion of the scope of AI and of a computer-based learning system. Coverage extends to agent-based learning environments, architectures for Artificial Intelligence in Education & Learning Innovation, statistical methods, cognitive tools for learning, computer-assisted language learning, distributed learning environments, educational robotics, human factors and interface design, intelligent agents on the internet, natural language interfaces for instructional systems, real-world applications of Artificial Intelligence in Education & Learning Innovation, tools for administration and curriculum integration, and more.
Arjuna Subject : Umum - Umum
Articles 18 Documents
 1   2 
Themes and values in pre-service teachers’ digital storybooks: A content analysis for elementary English education Najma Macatanto; Desiery Maghinay; John Dale Pendon; Carla Marie Rubio; Esmeraldo Sarad; Kim Silva
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 2 No. 1 (2026): Journal of Artificial Intelligence in Education & Learning Innovation
Publisher : CV Rezki Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56003/jaieli.v2i1.701

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.
ChatGPT as a coping mechanism: Postgraduate use under supervisory gaps in South-South Nigerian tertiary institutions Uka Nwagbara; Daramfon Okon; Precious Ukeje
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 2 No. 1 (2026): Journal of Artificial Intelligence in Education & Learning Innovation
Publisher : CV Rezki Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56003/jaieli.v2i1.711

Abstract

Background: Generative AI tools like ChatGPT are common in higher education, especially where students lack timely academic support. In many South-South Nigerian universities, gaps in postgraduate supervision may influence how students use such tools. Objectives: This study examines how postgraduate students become aware of ChatGPT, how much they use it, and why they rely on it when supervision is limited. Methods: A qualitative exploratory design was used. Twenty-one postgraduate students were purposively selected from universities in South-South Nigeria. Data were collected through online semi-structured interviews using a validated guide. Inductive thematic analysis, using Braun & Clarke’s framework, was applied to identify patterns in the data. Results: Findings show that awareness of ChatGPT is mostly informal, driven by peers, social media, and personal trial and error. Many students use ChatGPT frequently, especially during proposal writing, data analysis, and thesis development. Its use is strongly linked to delays or gaps in supervision. Conclusions: ChatGPT is widely used as a support tool and coping strategy, rather than merely a convenience. Its role reflects gaps in supervision and the need for clearer institutional guidance and training.
Effects of Chat-GPT (Generative Pre-Trained Transformer) in the Writing Skills of Undergraduate Students: A Scoping Review Hamdoni Pangandaman; Mirella Veras; Samiel Macalaba; Abolbashar Mangontawar; Sittie Ainah Mai-Alauya; Saliha Janine Gubaten; Maslainie Aliola; Norhanie Lininding; Paida Abdulmalik; Sittie Hainieh Taratingan
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 2 No. 1 (2026): Journal of Artificial Intelligence in Education & Learning Innovation
Publisher : CV Rezki Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56003/jaieli.v2i1.712

Abstract

Background: 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.
Themes and values in pre-service teachers’ digital storybooks: A content analysis for elementary English education Najma Macatanto; Desiery Maghinay; John Dale Pendon; Carla Marie Rubio; Esmeraldo Sarad; Kim Silva
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 2 No. 1 (2026): Journal of Artificial Intelligence in Education & Learning Innovation
Publisher : CV Rezki Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56003/jaieli.v2i1.701

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.
ChatGPT as a coping mechanism: Postgraduate use under supervisory gaps in South-South Nigerian tertiary institutions Uka Nwagbara; Daramfon Okon; Precious Ukeje
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 2 No. 1 (2026): Journal of Artificial Intelligence in Education & Learning Innovation
Publisher : CV Rezki Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56003/jaieli.v2i1.711

Abstract

Background: Generative AI tools like ChatGPT are common in higher education, especially where students lack timely academic support. In many South-South Nigerian universities, gaps in postgraduate supervision may influence how students use such tools. Objectives: This study examines how postgraduate students become aware of ChatGPT, how much they use it, and why they rely on it when supervision is limited. Methods: A qualitative exploratory design was used. Twenty-one postgraduate students were purposively selected from universities in South-South Nigeria. Data were collected through online semi-structured interviews using a validated guide. Inductive thematic analysis, using Braun & Clarke’s framework, was applied to identify patterns in the data. Results: Findings show that awareness of ChatGPT is mostly informal, driven by peers, social media, and personal trial and error. Many students use ChatGPT frequently, especially during proposal writing, data analysis, and thesis development. Its use is strongly linked to delays or gaps in supervision. Conclusions: ChatGPT is widely used as a support tool and coping strategy, rather than merely a convenience. Its role reflects gaps in supervision and the need for clearer institutional guidance and training.
Effects of Chat-GPT (Generative Pre-Trained Transformer) in the Writing Skills of Undergraduate Students: A Scoping Review Hamdoni Pangandaman; Mirella Veras; Samiel Macalaba; Abolbashar Mangontawar; Sittie Ainah Mai-Alauya; Saliha Janine Gubaten; Maslainie Aliola; Norhanie Lininding; Paida Abdulmalik; Sittie Hainieh Taratingan
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 2 No. 1 (2026): Journal of Artificial Intelligence in Education & Learning Innovation
Publisher : CV Rezki Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56003/jaieli.v2i1.712

Abstract

Background: 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.
Artificial intelligence-supported motor skill performance in physical education and sport: A systematic review and meta-analysis informed by motor learning theory Yulingga Nanda Hanief; Vera Septi Sistiasih; Ferdinando Cereda; Spyridon Plakias
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 2 No. 1 (2026): Journal of Artificial Intelligence in Education & Learning Innovation
Publisher : CV Rezki Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56003/jaieli.v2i1.753

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.
Sports technology acceptance as a predictor of academic commitment among undergraduate physical education students in China Yingxue Su
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 2 No. 1 (2026): Journal of Artificial Intelligence in Education & Learning Innovation
Publisher : CV Rezki Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56003/jaieli.v2i1.748

Abstract

Background: 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.

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