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All Journal International Journal of Pedagogy and Teacher Education JKPK (Jurnal Kimia dan Pendidikan Kimia)
Harjono Harjono
Chemistry Education, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang, Semarang
Chemical Engineering, Chemistry & Bioengineering Chemistry Education Social Sciences

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Preservice Chemistry Teachers' Views on the Use of Artificial Intelligence in the Classroom Dimas Gilang Ramadhani; Sri Susilogati Sumarti; Harjono Harjono; Ella Kusumastuti
International Journal of Pedagogy and Teacher Education Vol 9, No 1 (2025): International Journal of Pedagogy and Teacher Education - April
Publisher : The Faculty of Teacher Training and Education (FKIP), Universitas Sebelas Maret, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijpte.v9i1.89534

Abstract

As Artificial Intelligence (AI) becomes increasingly integrated into education, understanding how future educators perceive its use is essential. This study explores the perceptions of 150 preservice chemistry teachers in Indonesia regarding the integration of AI in chemistry education. Participants completed a validated 12-item Likert-scale survey covering four dimensions: Pedagogical Benefit, Technical Benefit, Risk to Student, and Risk to Teacher. The data were analyzed using descriptive statistics, correlation, regression, clustering, and Principal Component Analysis (PCA). Results indicate that participants perceived AI as highly beneficial, particularly for simplifying material preparation and supporting understanding of abstract concepts. However, concerns also emerged, especially around potential declines in student motivation, critical thinking, and the teachers’ readiness to use AI effectively. Correlation analysis revealed that benefit and risk perceptions were evaluated independently. Regression models identified “real-life connection” and “AI knowledge gap” as significant benefit and risk perception predictors. Cluster analysis grouped respondents into three profiles: Cautious Adopters, Enthusiastic Supporters, and Selective Optimists, each reflecting different levels of acceptance and concern. These findings underscore the need for differentiated teacher training programs that address technical competence and pedagogical reflection. Limitations include the reliance on self-report data and a single-country sample. The study emphasizes the importance of preparing educators to critically and effectively integrate AI into science instruction.
Evaluating Chemistry Teacher’s Questioning Skills in Microteaching Based on Artificial Intelligence (AI) Using an Assessment Rubric Nala Izzul Muna; Sri Susilogati Sumarti; Harjono Harjono; Woro Sumarni; Dimas Gilang Ramadhani
International Journal of Pedagogy and Teacher Education Vol 9, No 2 (2025): International Journal of Pedagogy and Teacher Education - October
Publisher : The Faculty of Teacher Training and Education (FKIP), Universitas Sebelas Maret, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijpte.v9i2.106168

Abstract

This study evaluates the questioning skills of chemistry teachers during microteaching using an AI-assisted assessment rubric. A total of 200 publicly available YouTube videos (2019–2024) were selected using defined criteria: chemistry instruction, teacher–student questioning, Indonesian language, minimum audio clarity of 45 dB, and at least 5 minutes in duration. All videos featured pre-service teachers. Transcripts were generated using Otter.ai and segmented into discrete questioning episodes. Evaluation was performed using Gemini Flash 2.0 (build: 2025.03, temperature: 0.0), a large language model configured via prompt design and anchored exemplars to assess six pedagogical indicators: question type, content relevance, question complexity, wait time, teacher’s response, and student interaction. Each indicator was rated on a 4-point scale. Reliability checks against human-coded samples (n = 40) yielded strong agreement (Cohen’s κ = 0.78). Results showed that 25% of sessions were classified as high-performing, with open-ended and cognitively demanding questions, extended wait time, and rich student engagement. In contrast, 42% were low-performing, marked by factual recall, short pauses, and minimal interaction. Clustering analysis (Gower k-medoids) identified three distinct performance profiles (average silhouette = 0.41). This AI-based framework enables reliable, scalable, and interpretable evaluation of questioning practices. A prototype feedback tool was developed, providing per-indicator scores, question examples, and suggested improvements supporting formative teacher development. Ethical compliance was ensured through the exclusive use of public, anonymized content.
Unveiling Students' Understanding of Ammonia as a Weak Base through Scaffolding-Based Chemical Reasoning Assessment Yuda Faturohman; Endang Susilaningsih; Harjono Harjono; Murbangun Nuswowati; Ella Kusumastuti; Dimas Gilang Ramadhani
JKPK (Jurnal Kimia dan Pendidikan Kimia) Vol 10, No 1 (2025): JKPK (Jurnal Kimia dan Pendidikan Kimia)
Publisher : Program Studi Pendidikan Kimia FKIP Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jkpk.v10i1.100779

Abstract

Reasoning is a basic cognitive ability in science learning, especially in chemistry, in which students must connect macroscopic, symbolic, and microscopic levels. However, most students seem to have difficulty learning chemical reasoning, especially in the ionization of weak bases (examples: NH₃). This study uses a scaffold-based assessment to evaluate students' explanations for ammonia as a base. A paper-and-pencil test was applied to 91 first-year preservice chemistry students to test them on phenomenological, mechanical, and structural types of reasoning. Two raters rated responses, and scoring reliability was assessed using Cohen’s Kappa (0.925). The data analysis consisted of descriptive statistics, correlation analysis, clustering (K-Means and t-SNE), and regression prediction with XGBoost. The results demonstrate that structural reasoning exhibits the highest level, but phenomenological reasoning has the most variation. There appears to be a high correlation between phenomenological empirical generalization and structural reasoning (r = 0.35+). Clustering outputs show three categories of students: high (R3), moderate (R2), and low (R1) reasoning, and most of the students are categorized at the moderate reasoning level, indicating some misconceptions. The XGBoost model performs well in predicting high-reasoning students but not in the moderate-reasoning classification. This paper indicates the power of scaffolding-embedded assessment for deducing reasoning patterns and misconceptions in ammonia ionization. The results can guide adaptive learning approaches for improving students' chemical reasoning.
Co-Authors Dimas Gilang Ramadhani Dimas Gilang Ramadhani Ella Kusumastuti Endang Susilaningsih Murbangun Nuswowati Nala Izzul Muna Sri Susilogati Sumarti Woro Sumarni Yuda Faturohman