Article Per Year (5 Year)
p-Index From 2021 - 2026
1.745
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Pendidikan Fisika Jurnal Tarbiyah : Jurnal Ilmiah Kependidikan Bubungan Tinggi: Jurnal Pengabdian Masyarakat Journal of Advanced Sciences and Mathematics Education Online Learning in Educational Research Lambda: Jurnal Ilmiah Pendidikan MIPA dan Aplikasinya Journal of Mathematics Science and Computer Education SERIBU SUNGAI: Journal of Research and Community Service Gawi: Journal of Action Research Journal of Current Studies in SDGs Journal of Current Studies in SDGs
Qamariah Qamariah
Universitas Lambung Mangkurat
Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Mathematics Physics Public Health Social Sciences Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Enhancing High School Physics Teachers’ Knowledge through Training in Deep Learning Design Based on Artificial Intelligence and Wetland Contexts Qamariah Qamariah; Suyidno Suyidno; Saiyidah Mahtari; Misbah Misbah; Zainuddin Zainuddin; Muhammad Arifuddin; Mastuang Mastuang
Bubungan Tinggi: Jurnal Pengabdian Masyarakat Vol 8, No 3 (2026): AUGUST 2026
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/btjpm.v8i3.20501

Abstract

This community service program aimed to strengthen physics teachers’ capacity to design deep learning by integrating artificial intelligence (AI) and wetland contexts. The partner was the Physics Subject Teachers’ Association (MGMP) of Tanah Laut Regency, South Kalimantan. The activity used a participatory, practice-oriented training format consisting of needs mapping, conceptual strengthening, guided practice in pedagogical prompting and AI-output verification, contextual physics learning design, and evaluation. Knowledge achievement was measured using 10 multiple-choice items, while participant satisfaction was collected using a 10-item five-point questionnaire. Descriptive analysis showed an average knowledge mastery of 97.27%; seven items reached 100% correct responses, while three items—deep learning principles, effective prompting, and AI ethics—reached 90.91%. Satisfaction averaged 4.59 out of 5 (91.78% of the maximum score). Overall satisfaction received the highest mean (4.78), whereas understanding of AI utilization obtained the lowest, but still positive, mean (4.33). These findings indicate high post-activity mastery and strong participant acceptance while highlighting the need for continued hands-on mentoring in pedagogical AI use, prompt design, output verification, and responsible AI practices.
Mapping the Integration, Impacts, and Determinants of Artificial Intelligence in Science Education: A Systematic Literature Review Misbah; Qamariah Qamariah; Zainuddin Zainuddin; Mastuang Mastuang
Lambda: Jurnal Ilmiah Pendidikan MIPA dan Aplikasinya Vol. 6 No. 3 (2026): Lambda
Publisher : Lembaga Bale Literasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58218/lambda.v6i3.5118

Abstract

Background: AI is increasingly examined as a tool for science-specific instruction, feedback, inquiry and teacher preparation, but research designs and claimed outcomes vary. Methods: This systematic literature review applied PRISMA 2020 reporting to a title-only Scopus search through Watase Uake on 5 September 2026. Two phrases retrieved 123 and 114 raw records, respectively (237 total). Searches were restricted to Q1 journal publications dated 2023–2026. The initial selection retained 49 articles; eight were removed in a subsequent science-education relevance audit. Two further Q1-indexed Scopus reports identified outside the Watase export were assessed but excluded as unrelated plant-science research. The final corpus comprised 41 articles, synthesized descriptively and thematically with study designs differentiated. Results: The literature describes AI as a scaffold for inquiry, dialogic science reasoning, multimodal representation, feedback, learning analytics and science-teacher preparation. Some primary studies report context-dependent benefits for measured learning outcomes, but findings cannot be generalized across heterogeneous designs. Conclusions: Appropriate science-specific pedagogy, human–AI interaction, teacher competence and ethical oversight are central considerations.
Artificial Intelligence Integration in Science Education: A Meta-Synthesis of Pedagogical Design, Human-AI Interaction, and Learning Outcomes Sarah Miriam; Misbah Misbah; Qamariah Qamariah; Surya Haryandi; Erwina Oktavianty
Journal of Mathematics Science and Computer Education Vol 6, No 2 (2026): NOVEMBER 2026
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jmscedu.v6i2.21022

Abstract

Artificial intelligence (AI) is increasingly integrated into science education as instructional support and a partner in dialogue. This Scopus-based systematic review and thematic meta-synthesis examined AI roles, pedagogical design, human–AI interaction, reported learning outcomes, and implementation conditions in literature dated 2023–2026. From 237 identified database records, 49 science-education reports met the substantive inclusion criteria; two additional plant-science reports were documented as contextual boundary cases but excluded from the core synthesis. The synthesis identified AI roles as a tutor, scaffold, content generator, dialogic partner, adaptive system, multimodal resource, and analytical tool. Selected empirical interventions reported gains in achievement, problem posing, retention, transfer, or self-efficacy, whereas evidence for critical thinking and problem solving was mixed or limited. Teacher capacity, pedagogical design, interaction quality, institutional readiness, and ethical safeguards shaped the interpretation of these outcomes.  Science educators should therefore use AI to support—not replace—learners’ scientific reasoning, verification, and teachers’ professional judgment.
Co-Authors Ahmad Bakharzi Hakam Ahmad Syarkowi Erwina Oktavianty Erwina Oktavianty Erwina Oktavianty Ismail Ismail Lasiani Lasiani M Arifudin M. Arifuddin Mastuang Mastuang Misbah Misbah Misbah Misbah Misbah Misbah Misbah Misbah Misbah Muhammad Arifuddin Muhdi Harto Muhdi Harto Mustika Wati Noor Izzati Pratiwi Nur Afifah Azzahra Nurlaela Muhammad Parham Saadi Rossy Arlinda Saiyidah Mahtari Sarah Miriam Surya Haryandi Suyidno Suyidno Suyidno Suyidno Wardatul Humairah Zainuddin Zainuddin