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All Journal Jurnal Ilmiah Profesi Pendidikan JURNAL BIOSHELL Education Journal: Journal Educational Research and Development
Noviana, Evy
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Education Other

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Validation of Pre-Service Science Teacher Artificial Intelligence Competence Self-Efficacy (AICS): Rasch Model Analysis Adam, Wahyuni; Qudratuddarsi, Hilman; Ningthias, Dyah Puspitasari; Rahmadhani, Aulia; Noviana, Evy
Jurnal Ilmiah Profesi Pendidikan Vol. 10 No. 2 (2025): Mei
Publisher : Fakultas Keguruan dan Ilmu Pendidikan, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jipp.v10i2.3662

Abstract

Artificial Intelligence (AI) is transforming science education through virtual labs, intelligent tutoring, and adaptive assessments. However, pre-service teachers often lack formal training in AI integration. This study aims to validated the Artificial Intelligence Competence Self-Efficacy (AICS) instrument using Rasch model, covering AI knowledge (AIK), AI Pedagogy (AIP), AI Assessment (AIA), AI Ethics (AIE), Human-Centred Education (HCE), and Professional Engagement (PEN). This study used a quantitative survey with 338 third-year pre-service science teachers selected through convenience sampling. Data were collected via Google Forms where ethical considerations and back-translation ensured data integrity. Data were analyzed through reliability, separation, item fit statistics, unidimensionality and Differential Item Functioning (DIF). The findings indicate that the AICS instrument is psychometrically sound, with high reliability (person reliability = 0.94, item reliability = 0.95) and excellent separation indices. The Wright Map showed that item difficulty was well-aligned with participant ability, effectively capturing various levels of AI self-efficacy. Item fit statistics confirmed all items functioned within acceptable ranges, and unidimensionality analysis supported the measurement of a single, coherent construct. DIF analysis showed minimal gender bias, though one item (AIP1) favored males. Overall, the instrument is valid and reliable for being used to assess AI competence self-efficacy among pre-service science teachers.
Innovativeness and Optimism as Predictors of Generative AI Acceptance Among Pre-service Elementary School Teachers Meivawati, Eli; Noviana, Evy; Pratama, I Putu Yogi; Qudratuddarsi, Hilman; Indriyanti, Nor
Education Journal : Journal Educational Research and Development Vol. 10 No. 1 (2026)
Publisher : LPPM Universitas PGRI Argopuro Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31537/ej.v10i1.2958

Abstract

This study investigates Generative Artificial Intelligence (Gen AI) acceptance among Generation Z pre-service elementary school teachers by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model with innovativeness and optimism as additional psychological predictors. As Gen AI becomes increasingly embedded in educational practice, understanding how future elementary school teachers perceive and accept this technology is critical for informing teacher preparation and digital transformation initiatives. Using a quantitative, cross-sectional survey design, data were collected from 563 pre-service teachers across four universities in Indonesia through an online questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS was used for data analysis. The measurement model demonstrated satisfactory reliability, convergent validity, and discriminant validity, while model fit indices indicated that the proposed model adequately represented the data. Results show that both innovativeness and optimism significantly predict performance expectancy, effort expectancy, facilitating conditions, habit, and hedonic motivation, with innovativeness emerging as the stronger personality-based determinant. Regarding behavioral intention to use Gen AI, performance expectancy, effort expectancy, facilitating conditions, and habit exhibit significant positive relationships, whereas hedonic motivation does not significantly influence intention. The structural model explains 75.9% of the variance in behavioral intention, indicating strong explanatory power within the context of Gen AI adoption in teacher education. The findings highlight the dual importance of cognitive evaluation (e.g., perceived usefulness and ease of use) and personal traits (e.g., innovativeness and optimism) in shaping Gen AI acceptance among pre-service elementary school teachers. Theoretically, the study extends UTAUT2 by demonstrating the value of incorporating personality-related constructs into contemporary AI acceptance studies. Practically, the results suggest that teacher education programs should provide structured opportunities for meaningful engagement with Gen AI tools while fostering mindsets that encourage experimentation and openness to technological change. These efforts may better prepare future teachers to integrate Gen AI responsibly and effectively in elementary education settings.
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 Adam, Wahyuni Aulia Rahmadhani Cinthami, Ratih Dhamayyana Dwi Meivawati, Eli Ningthias, Dyah Puspitasari Nor Indriyanti Pratama, I Putu Yogi Putra, Ammar Ahadi QUDRATUDDARSI, HILMAN