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July 5, 2026Sustainability0 citationsOpen Access

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

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IRIvana RestovićJJJosipa JurićNKNives Kević

Key Points

  • This study aims to evaluate the attitudes and readiness of pre-service teachers toward integrating AI in sustainable science education.
  • Mixed-methods study involving 251 students from the University of Split.
  • Data analysis included exploratory factor analysis and ANOVA with post hoc tests, along with multiple linear regression.
  • Qualitative content analysis was employed to assess perceptions towards AI integration.
  • Perceived usefulness of AI was identified as a key driver of acceptance among all groups of pre-service teachers.
  • Science students showed high ethical sensitivity but low confidence in AI's practical application, voicing caution about reliability.
  • Behavioral analysis indicated that initial exposure to AI tools enhanced students’ perceptions of its utility.

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.

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Cite This Study

Restović et al. (2026) studied this question.

synapsesocial.com/papers/6a49f70df5d1d45b288012bbhttps://doi.org/10.3390/su18136786
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