AI-Driven Resource Optimization in Science Education: Assessing Pre-Service Teachers’ Readiness for Sustainable Teaching Practices and Environmental Literacy

Ivana Restović, Josipa Jurić and Nives Kević ()
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Ivana Restović: Teacher Education Department, Faculty of Humanities and Social Sciences, University of Split, 21000 Split, Croatia
Josipa Jurić: Teacher Education Department, Faculty of Humanities and Social Sciences, University of Split, 21000 Split, Croatia
Nives Kević: Centre for Transdisciplinary Promotion of Sustainable Development—OdRaST, Faculty of Humanities and Social Sciences, University of Split, 21000 Split, Croatia

Sustainability, 2026, vol. 18, issue 13, 1-22

Abstract: The ultimate goal of integrating artificial intelligence into education is to ensure the long-term stability, quality, and sustainability of the educational process, turning it into a tool that consistently improves teaching and learning. Yet its sustainable and responsible integration depends largely on a positive mindset and the pedagogical willingness of future teachers. This study examines the attitudes and readiness of pre-service teachers, specializing in preschool, primary, and subject-specific science education, toward AI integration, with a specific focus on sustainable science education and Green Lab concepts. A mixed-methods study was conducted on a sample of 251 students from the University of Split. Data were analyzed using exploratory factor analysis, standard and Welch ANOVA with Tukey’s HSD and Games–Howell post hoc tests, and multiple linear regression in IBM SPSS 20, and qualitative content analysis. The findings reveal perceived usefulness as a primary driver of AI acceptance across all groups. Science students demonstrated the highest levels of ethical and critical sensitivity but provided the lowest ratings for AI’s practical application in sustainable science education, expressing cautious attitudes and distinct concerns about system reliability. However, no significant difference was found between students with and without a science background in regard to AI’s potential to facilitate sustainable scientific concepts. Furthermore, behavioral analysis demonstrated that even initial, occasional exposure to AI tools significantly boosted students’ perceptions of its utility and sustainable application compared to non-users, whereas increasing the frequency of use resulted in no additional gains. The transition toward sustainable science education requires moving beyond technical literacy toward a comprehensive framework that integrates pedagogical usefulness with ethical responsibility and sustainable scientific application. Future studies should explore potential models that combine the methodological creativity of pre-service educators and teachers with the analytical rigor of science students. Ultimately, this research underscores that an educational policy must integrate digital advancements while strictly maintaining ethical standards and the essential role of human supervision.

Keywords: artificial intelligence; sustainable science education; pre-service teachers; ethical awareness; environmental literacy; resource optimization; Green Lab (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2026
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