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All Journal Mandalika Mathematics and Educations Journal JURNAL BIOSHELL Saqbe : Jurnal Sains dan Pembelajarannya
Cinthami, Ratih Dhamayyana Dwi
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Education Mathematics Other

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The Current Update of ChatGPT Roles in Science Experiment: A Systemic Literature Review Dewi, Hefi Rusnita; Qudratuddarsi, Hilman; Ningthias, Dyah Puspitasari; Cinthami, Ratih Dhamayyana Dwi
Saqbe: Jurnal Sains dan Pembelajarannya Vol 1 No 2 (2024): Saqbe : Sains dan Pembelalajarannya (Oktober 2024)
Publisher : Universitas Sulawesi Barat

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Abstract

Scientific experiments provide numerous benefits for school students, including enhancing their understanding of scientific concepts through hands-on experiences. By engaging in experiments, students can develop critical thinking, problem-solving, and data analysis skills. This study aims to analyze the role of ChatGPT in relation to science practicum activities. The study employs a systematic literature review method using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Data sources consist of scientific journals that were identified, screened, and evaluated, resulting in only seven journals meeting the inclusion criteria. These journals were then reviewed through thematic analysis to outline the key findings from the analyzed publications. The results show that the majority of the reviewed articles (85.71%) were published in the Journal of Chemical Education, with a focus on the field of chemistry (85.71%), and 57.14% of the first authors originated from the United States. Two main research themes were identified in this field: ChatGPT as a tool to assist teachers in preparing practicum sessions and analyzing ChatGPT’s ability to generate reports. The study also highlights opportunities for further research on ChatGPT’s capability to analyze practicum reports and evaluate practicum worksheet responses. This study provides a foundation for future researchers to identify topics and research gaps in the field.
Penerapan Pewarnaan Graf pada Pembagian Kamar Asrama Mahasiswi Universitas Mataram dengan Algoritma Welch Powell Cinthami, Ratih Dhamayyana Dwi; Prayitno, Sudi; Primajati, Gilang
Mandalika Mathematics and Educations Journal Vol 7 No 4 (2025): Desember
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v7i4.10001

Abstract

Room assignments at the female dormitory of Universitas Mataram are still done manually, often placing students from the same study program and semester in the same room. This condition limits academic diversity, which is essential to foster social interaction and enhance academic performance. This study aims to apply graph coloring theory to address this issue. Each student is represented as a vertex, and connections between students with the same academic background are represented as edges. The Welch Powell algorithm is applied to color the vertices so that no adjacent vertices share the same color, ensuring students with similar academic characteristics are not assigned to the same room. This applied research uses Visual Basic for Applications (VBA) Macro in Microsoft Excel to automate the creation of matrices and implementation of the algorithm. The results show that this approach is effective and efficient in grouping students into rooms while promoting academic heterogeneity. This method is expected to serve as a practical solution for dormitory management in other higher education institutions.
Factors Shaping Pre-service Biology Teachers’ Acceptance of Generative Artificial Intelligence Noviana, Evy; Putra, Ammar Ahadi; Cinthami, Ratih Dhamayyana Dwi; Qudratuddarsi, Hilman
JURNAL BIOSHELL Vol 15 No 1 (2026): April
Publisher : Universitas Islam Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56013/bio.v15i1.5552

Abstract

The rapid diffusion of generative artificial intelligence (GAI) has introduced new opportunities and challenges for teacher education, particularly within STEM disciplines, specifically biology education. This study investigates the determinants of generative AI acceptance among Generation Z pre-service biology teachers by integrating constructs from the Technology Acceptance Model and Diffusion of Innovation theory with pedagogically grounded variables. Using a quantitative cross-sectional survey design, data were collected from 318 pre-service biology teachers enrolled at two Indonesian universities. Partial least squares structural equation modeling (PLS-SEM) was employed to examine the relationships among trialability, relative advantage, perceived compatibility, trust, feedback quality, perceived assessment quality, subjective norms, perceived ease of use, perceived usefulness, attitude, behavioral intention, and acceptance of generative AI. The results indicate that trialability, relative advantage, and compatibility significantly predict perceived ease of use, while relative advantage and trust significantly influence perceived usefulness. Feedback quality and subjective norms positively shape attitudes toward generative AI, whereas perceived assessment quality shows no significant effect. Perceived ease of use and attitude emerge as key predictors of behavioral intention, which strongly determines acceptance. The findings highlight the central roles of affective, social, and trust-related factors in shaping generative AI adoption among future biology teachers. This study contributes to the emerging literature on AI in STEM teacher education and offers practical implications for designing pedagogically meaningful and responsible AI integration in teacher preparation programs.
Co-Authors Dewi, Hefi Rusnita Ningthias, Dyah Puspitasari Noviana, Evy Primajati, Gilang Putra, Ammar Ahadi QUDRATUDDARSI, HILMAN Sudi Prayitno