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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 10 Documents
 1 
Enhancing basketball technique learning using I-spring application: A study in high school physical education Saputro, Hadi; Kurniawan, Rama; Saputra, Surya Adi; Wahyudi, Usman
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 1 No. 1 (2025): 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.v1i1.522

Abstract

Background: High school students feel less able to master the subject of Physical Education, Sports, and Health (PJOK) Basketball material in a relatively short meeting, so it needs innovative learning. Objectives: This research was conducted to develop learning media for basketball game material based on the I-Spring application to facilitate students' learning. Methods: This research uses the Research and Development (R&D) method using the ADDIE model, which refers to Lee and Owens. Data collection was done through direct observation and questionnaire distribution. The research subjects were the students of Senior High School 1 Gondanglegi, Malang Regency, Indonesia, which amounted to 866 students. The data analysis method used is descriptive qualitative using Likert scale calculation. Results: The observation results showed that 95.5% of students had participated in basketball learning in class or on the field, 71.2% of students had difficulty learning the basic techniques of basketball games, and 92.4 students needed application-based learning media to understand more deeply and be able to practice basic basketball techniques. Small group trials on 10 students obtained results of 85.2% and 86.4% for large group trials with 30 students. Conclusions: The developed product is feasible for PJOK learning, especially basketball material at State Senior High School 1 Gondanglegi. Future studies are encouraged to expand the material coverage and evaluate long-term learning outcomes using the developed application.
Development of KAREBU (cultural diversity card game) to enhance curiosity in fourth-grade elementary students Mahanani, Etyka Rina; Ahdhianto, Erif; Barus, Yohannes Kurniawan
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 1 No. 1 (2025): 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.v1i1.526

Abstract

Background: Learning media is one of the important components of education. Objectives: The purpose of this research and development was to develop the KAREBU (Cultural Diversity Card) learning media to strengthen the curiosity character of fourth-grade students in the subject of Natural and Social Sciences with local cultural diversity material that is valid, practical, and effective. Methods: Using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation), this study developed KAREBU cards integrated with augmented reality (AR) technology. This research instrument is in the form of interviews, observations, and questionnaires given to instrument experts, material experts, media experts, and users, including teachers and students. Data collection stages were carried out through observation, interviews, needs questionnaires, validation questionnaires, practicality questionnaires, and curiosity questionnaires before and after using the media. Results: The results showed that the KAREBU learning media met the valid, practical, and effective criteria. KAREBU media received a validity score from material experts of 3.72 with a valid category, a score from media experts of 4.91 with a very valid category, and a score from users of 4.73 with a very valid category. The KAREBU learning media received a practicality score from teachers of 4.49 with a convenient category and a practicality score from students of 98.5% with a convenient category. The level of media effectiveness in strengthening students' curiosity was obtained from the results of the questionnaire before and after using the media, which was calculated using the N-Gain Score test with a value of 0.76 and high effectiveness criteria. Conclusions: Therefore, it was concluded that the KAREBU learning media effectively strengthens students' curiosity. Further studies could explore the long-term impacts of KAREBU on cross-cultural empathy.
Academic goals and learning strategies of pre-service teachers during emergency online learning in the Philippines Acebes, Swen Joshryll; Malitante, Jaizel; Tuble, Nikki; Bongolto, Rowena
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 1 No. 1 (2025): 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.v1i1.569

Abstract

Background: The COVID-19 pandemic has lasted an extended period, during which many students have reported being adversely affected by the escalating global crisis. Objectives: This study examined the academic goals, learning barriers, facilitative strategies, and ways forward of pre-service teachers during emergency online learning. Methods: This qualitative case study employed content analysis to examine data gathered through focus group discussions. To explore the implications of survey findings and gain deeper insights into students' perspectives on remote learning, the researchers conducted Focus Group Discussions (FGDs) with twelve purposively selected pre-service teachers via Google Meet. The discussion transcripts were analyzed to construct a compelling narrative, supported by literature and prior research, to explain emerging themes. Results: Among the twelve participants, ten reported experiencing psychosocial stress, eight cited recurring technical issues, and seven mentioned financial constraints as significant learning barriers. Despite these challenges, the participants remained motivated to complete their coursework, viewing academic achievement as a stepping stone toward improving their socio-economic conditions. They employed various strategies, including scheduling, note-taking, goal-setting, optimistic thinking, parental scaffolding, and systematic planning, to adapt to online learning. To ensure credibility, the researchers used member checking, peer debriefing, and FGD and survey data triangulation. Conclusions: These findings highlight the resilience and adaptability of pre-service teachers in navigating the challenges of emergency online learning, underscoring the need for supportive institutional policies and resources. Future research may consider comparing student experiences in public and private institutions or examining the long-term impact of emergency remote learning on teaching efficacy. Pedagogical and academic implications are further discussed.
The future of teaching: Analyzing the interplay between AI literacy and TPACK among BEED pre-service teachers Aglibot, Karl Alvin; Rito, Chelsea Lorraine; Villalon, Larence; Gampal , Nelvin
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 1 No. 1 (2025): 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.v1i1.570

Abstract

Background: As artificial intelligence (AI) continues transforming the educational landscape, pre-service teachers must develop theoretical understanding and practical skills in AI integration. Objectives: This study explored the relationship between AI literacy and TPACK among 139 BEED pre-service teachers. Methods: Using descriptive statistics, Spearman's correlation, and multiple linear regression analysis, the study investigated how components of AI literacy relate to TPACK. Results: Results revealed that the pre-service teachers demonstrated high levels of AI literacy, particularly in ethical awareness and intrinsic motivation. Pre-service teachers expressed strong interest in AI's educational potential and a commitment to its responsible use. While their foundational AI knowledge was high, many reported moderate self-efficacy and lacked confidence in executing AI-related tasks. Gaps were also noted in applying AI concepts to real-world teaching scenarios and designing AI-driven solutions. Furthermore, the results showed high TPACK proficiency, with strength in pedagogical and technological knowledge, though weaknesses were observed in content areas like mathematics and technical troubleshooting. A moderate positive correlation (Spearman's ρ = 0.48, p < 0.05) between AI literacy and TPACK indicates a meaningful association. Regression analysis revealed that AI literacy components explained 25.5% of the variance in TPACK. Cognitive learning emerged as the only significant predictor. Conclusions: The findings underscore the need for teacher education programs to provide hands-on, cognitive-focused AI training. Hence, enhancing pre-service teachers' cognitive and practical skills is essential to thoroughly preparing them for AI-enhanced teaching environments' demands.
Navigating the infodemic through social media: A qualitative case study of Filipino university students’ learning experiences during the COVID-19 polycrisis Talaman, Janel; Necosia, Mc Denver; Villalon, Larence; Tomines, Edrian Mark
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 1 No. 2 (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.v1i2.571

Abstract

Background: The COVID-19 pandemic has triggered a complex crisis in higher education that has not only disrupted formal learning but also given rise to an infodemic through the rapid spread of misinformation on digital platforms, making social media both a source of learning and a source of epistemic risk for students. Objectives: This study aims to explore how Filipino university students navigated learning experiences through social media during the COVID-19 polycrisis, focusing on preferred platforms, perceived educational relevance, and strategies for evaluating information credibility amid an infodemic. Methods: A qualitative case study design was employed. Six Filipino university students enrolled in a Bachelor of Elementary Education program participated in this study through purposive sampling. Data were collected via online semi-structured interviews, group chat discussions, document analysis of shared social media content, and follow-up interviews. Thematic analysis was conducted following Braun and Clarke's framework to identify recurring patterns across data sources. Results: Findings reveal that social media—particularly Facebook—served as a supplementary learning space, enabling students to access real-time information, engage with societal issues, and sustain their learning during the suspension of formal instruction. Students demonstrated active information-seeking behaviors, including cross-checking sources and exercising caution in sharing content. However, persistent exposure to misinformation highlighted their vulnerability within infodemic environments, especially in the absence of structured institutional guidance. Conclusions: Social media can support authentic and socially relevant learning during educational disruption, but its pedagogical value depends on students' critical and digital literacy capacities.
Using children’s digital picture books to teach disease preventive behaviors: A visual analysis of the Duma Says series Sarad, Esmeraldo; Cabatuan, Charmen; Tionson, Cleo; Subong, April Grace
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 1 No. 2 (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.v1i2.572

Abstract

Background: During health emergencies such as the COVID-19 pandemic, children require accessible and developmentally appropriate resources to understand disease prevention and health-related behaviors. Children’s digital picture books have emerged as a promising medium for conveying public health messages through visual storytelling and age-appropriate narratives. Objectives: This study aimed to examine how children’s digital picture books represent disease preventive behaviors and moral values, using the Duma Says series as a case study. Methods: A qualitative visual and textual analysis was conducted on four digital picture books from the Duma Says Collector’s Edition. The study employed qualitative visual analysis and a critical discourse approach to examine representations of hygiene practices, social responsibility, and community engagement embedded in both images and narratives. Results: The findings indicate that the Duma Says series effectively integrates key COVID-19 preventive behaviors—such as handwashing, mask-wearing, social distancing, and sanitation—within child-friendly language and culturally contextualized visuals. In addition to promoting health practices, the books emphasize values of hope, responsibility, cooperation, and service, portraying children as active contributors to community well-being during a crisis. Conclusions: Children’s digital picture books, such as the Duma Says series, function as effective educational tools for promoting disease prevention awareness and values-based learning. Incorporating culturally grounded digital picture books into early childhood and health education may strengthen children’s health literacy, emotional resilience, and sense of social responsibility during public health emergencies.
Factors influencing teachers’ attrition in public basic schools in Techiman Municipality, Ghana: An embedded mixed methods study Kyeremeh, Joseph; Adom, Dickson; Essuman, Michael Ato; Pek, Lim Seon
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 1 No. 1 (2025): 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.v1i1.578

Abstract

Background: 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.
AI-driven learning in physical education: A bibliometric analysis of trends, knowledge structure, and future research directions Larassary, Amelia
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 1 No. 2 (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.v1i2.673

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.
Exploring the use of SIBIKU multimedia to enhance learning participation among deaf students in a special school context Ayuningtyas, Nurina; Lutfi, Ahmad Fajri
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 1 No. 2 (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.v1i2.676

Abstract

Background: Observations at SLB C YPALB Perwari Kuningan indicate low motivation and limited participation among deaf students during learning activities. Interactive multimedia such as SIBIKU is expected to address these challenges. Objectives: This study aims to (1) examine the implementation of SIBIKU multimedia in learning, (2) analyze students’ responses, and (3) evaluate changes in students’ learning participation and vocabulary acquisition. Methods: This study employed a descriptive qualitative design with a field-based approach. Participants were selected using purposive sampling, consisting of one teacher and deaf students involved in computer learning activities. Data were collected through participatory observation, semi-structured interviews, and documentation. The main instrument was the researcher, supported by observation and interview guidelines. Data were analyzed using thematic analysis following Miles and Huberman’s model, including data reduction, data display, and conclusion drawing, with triangulation to ensure validity. Results: The findings show that SIBIKU multimedia enhances student participation, as indicated by (1) increased attention to learning media, (2) active imitation of sign language movements, (3) higher frequency of student interaction and turn-taking, and (4) greater engagement in learning tasks such as word-guessing activities. Students also demonstrated improved motivation and more independent learning behavior. Conclusions: SIBIKU multimedia provides a visually rich, interactive learning environment that effectively increases participation and engagement among deaf students, making it a promising tool for inclusive education.
Assessing academic integrity patterns among pre-service teachers using AI-based plagiarism detection Arroyo, Irish; Rusiana, Sittie Nor Aisha; Tabada, Daniere Maryje; Silva, Kim; Rubio, Carla Marie; Sarad, Esmeraldo; Toquero, Cathy Mae
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 1 No. 2 (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.v1i2.685

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 moderate level of academic integrity in their book reviews, scoring 82% or interpreted as students committing 15% 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.

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