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Bibliometric and Systematic Review of AI-Assisted Adaptive Learning Applications in Vocational Education (2018-2023) Harleni Harleni; M. Giatman; Nurhasan Syah; Ganefri Ganefri; Nizwardi Jalinus; Ridwan Ridwan; Krismadinata Krismadinata
AL-ISHLAH: Jurnal Pendidikan Vol 17, No 4 (2025): DECEMBER 2025
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v17i4.8019

Abstract

The integration of Artificial Intelligence (AI) into adaptive learning systems has gained traction in vocational education due to its potential to personalize instruction and enhance competency-based learning. However, research on this intersection remains fragmented, particularly in the context of vocational education and training (TVET). This study conducts a Systematic Literature Review (SLR) using the PRISMA protocol, combined with bibliometric analysis using VOSviewer, to map research trends on AI-assisted adaptive learning in vocational education from 2018 to 2023. Data were sourced from Scopus, Semantic Scholar, and Google Scholar, resulting in 41 eligible articles. The findings reveal a sharp increase in publications after 2020, reflecting growing interest in AI-driven innovations, particularly during the COVID-19 pandemic. Bibliometric mapping identified three dominant thematic clusters: AI-enabled personalization, competency-based vocational education, and pedagogical innovation. Geographically, most research originates from technologically advanced countries such as the United States, India, and the United Kingdom. The study highlights the strategic role of AI-assisted adaptive learning in supporting individualized pathways and skills alignment in vocational education. It also identifies gaps in longitudinal evaluation, pedagogical integration, and research representation from developing regions. These insights provide practical implications for policymakers, educators, and curriculum developers aiming to modernize vocational training systems through AI.
Unveiling The Impact of Inquiry-Based Learning: A Meta-Analysis of Student Learning Outcomes Harleni Harleni; Ganefri Ganefri; Asmar Yulastri; M Giatman; Dedy Irfan; Hansi Effendi
AL-ISHLAH: Jurnal Pendidikan Vol 17, No 3 (2025): SEPTEMBER 2025
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v17i3.7061

Abstract

Inquiry-based learning (IBL) has been increasingly adopted in education to foster students’ conceptual understanding and critical thinking. However, existing research shows varying results regarding its effectiveness, ranging from significant positive effects to no impact. This study aims to systematically evaluate the impact of IBL on student learning outcomes through a meta-analytic approach. A meta-analysis was conducted on empirical studies published between 2020 and 2024. The study followed the PRISMA protocol, including three stages: identification, screening, and inclusion. A total of 21 relevant studies were selected and analyzed using JASP software to ensure accuracy in calculating effect sizes and interpreting findings. The meta-analysis revealed that inquiry-based learning has a small but significant overall effect on student learning outcomes (effect size: d = 0.444). The impact was particularly evident in science and mathematics education. Additionally, moderating variables such as educational level, instructional design, and student engagement were found to influence the effectiveness of IBL. While the overall effect size is modest, the findings confirm that IBL can positively contribute to student achievement. These results support the continued use and further development of inquiry-based approaches in modern educational contexts. The study provides evidence-based insights for educators, researchers, and policymakers seeking to optimize teaching strategies for 21st-century learning environments.