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All Journal Journal of Advanced Sciences and Mathematics Education Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education)
Reni Marlina
Universitas Tanjungpura, Indonesia
Chemistry Education Mathematics Physics Social Sciences

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Urban–Rural differences in teachers’ acceptance of artificial intelligence for teaching and learning: Evidence from indonesia using the technology acceptance model Lusiwati Iriani Butar-Butar; Afandi; Reni Marlina; Eny Enawaty; Eva Faja Ripanti
Journal of Advanced Sciences and Mathematics Education Vol. 6 No. 2 (2026): Journal of Advanced Sciences and Mathematics Education
Publisher : CV. FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/jasme.v6i2.1280

Abstract

Background: Artificial Intelligence (AI) offers significant opportunities to enhance teaching and learning through instructional support, automated assessment, and learning analytics. However, teachers’ acceptance of AI may vary according to perceived usefulness, ease of use, and differences in technological access across geographical settings. Aim: This study aimed to compare teachers’ acceptance of AI for teaching and learning between urban and rural schools in Melawi Regency, Indonesia. Method: A quantitative comparative design was employed using the Technology Acceptance Model (TAM), encompassing Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Using (ATU), and Behavioral Intention (BI). Data were collected from 100 teachers through an online Likert-scale questionnaire. Content validity was established through expert review, and the data were analyzed using descriptive statistics and an independent samples t-test. Results: The findings indicated that teachers in both settings demonstrated high levels of AI acceptance. However, urban teachers reported higher acceptance (M = 4.22) than rural teachers (M = 4.05). The difference was statistically significant (t = 7.35, p < 0.001) with a very large effect size (Cohen’s d = 2.32), suggesting a substantial influence of geographical context on AI acceptance. Conclusion: Urban teachers exhibit significantly greater acceptance of AI than rural teachers. Infrastructure availability, digital literacy, and institutional support appear to be key factors influencing this disparity. Strengthening digital capacity and improving technological infrastructure are essential to promote equitable AI integration in education.
Exploring Publication Trends, Geographic Contributions, and Research Collaborations in Deep Science Learning: Systematic Literature Review Hamdani; Reni Marlina; Dedeh Kurniasih; Chokchai Yuenyong; Zulfahmi
Jurnal Pendidikan Sains Indonesia Vol. 14 No. 1: JANUARY 2026
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jpsi.v14i1.103

Abstract

Deep learning has become a trend in current learning objectives and has become a hot topic in the context of computing, and is widely applied in various application fields including science known as deep science learning (DSL). This study aims to reveal an overview and systematic review of research hotspots and emerging trends in DSL studies. This study raises the reputation of bibliometric applications that are proven to be able to analyze articles published between 2015 and 2025. We extracted articles with the words “deep AND science AND learning” and generated 251 articles by limiting them to English-language articles published in reputable international journals. The results reported that China is the country that has published the most articles and occupies the top position in collaborative article writing with other countries. Other findings reported that based on the WordCloud overview, the keywords that are often used in expressing DSL are deep learning and machine learning. The urgency of this research among others can be fundamental research in the development of science learning media and methods and approaches that are in accordance with the development of revolution 4.0. This study is able to present a breakthrough in future research designs for science education practitioners, science education lecturers, science teachers, science education observers, prospective teachers, and also students to analyze the findings and develop according to aspects that have not been disclosed in this study.
Preparing Future Indonesia Science Teachers through Microlearning: Effects on Pedagogical Competence and Instructional Readiness Reni Marlina; Hamdani; Chokchai Yuenyong
Jurnal Pendidikan Sains Indonesia Vol. 14 No. 1: JANUARY 2026
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jpsi.v14i1.123

Abstract

Preparing prospective science teachers with strong pedagogical competencies and instructional readiness remains an important challenge in teacher education. This study aims to evaluate the effectiveness of microlearning-based learning in improving the pedagogical competencies and instructional readiness of prospective science teachers. The study used a pretest–posttest quasi-experimental design with a control group. Participants consisted of 204 prospective science teacher. The experimental group received microlearning-based learning. The control group followed conventional learning methods. Data were collected using validated test instruments to measure pedagogical competence and instructional readiness, both of which had high reliability (Cronbach's alpha > 0.80). Data analysis was performed using independent and paired t-tests, analysis of variance, and effect size. The results showed that the experimental group had higher posttest scores for pedagogical competence (M = 4.31; SD = 0.41) than the control group (M = 3.62; SD = 0.47) with a large effect size (d = 0.86). Similarly, the instructional readiness scores of the microlearning group (M = 4.28; SD = 0.39) were significantly higher than those of the control group (M = 3.58; SD = 0.44) with a large effect size (d = 0.83; p < 0.001). These findings provide empirical evidence that microlearning-based learning is effective in improving the pedagogical competence and instructional readiness of prospective science teachers. This study confirms the potential of microlearning as a pedagogical approach that supports the strengthening of professional preparation in science teacher education and provides direction for the design of future teacher education.
Co-Authors Afandi Chokchai Yuenyong Dedeh Kurniasih Eny Enawaty Eva Faja Ripanti Hamdani Lusiwati Iriani Butar-Butar Zulfahmi