ARTIFICIAL INTELLIGENCE TOOLS, EDUCATIONAL APPROACHES, AND LEARNING OUTCOMES IN ENVIRONMENTAL EDUCATION
ARTIFICIAL INTELLIGENCE TOOLS, EDUCATIONAL APPROACHES, AND LEARNING OUTCOMES IN ENVIRONMENTAL EDUCATION

Summary/Abstract: Artificial intelligence (AI) technologies are increasingly being used in environmental education, but the empirical evidence base in this field remains fragmented. The aim of this study was to systematically map empirical research on the use of AI in environmental education between 2022 and 2025. The study employed a scoping review design based on the PRISMA-ScR protocol. A total of 419 records retrieved from the Scopus and Web of Science databases were screened, and 16 studies met the inclusion criteria. Data were extracted using a structured coding framework based on four research questions and synthesized through thematic analysis. The findings indicated that large language models, particularly ChatGPT, are the most frequently used AI tools. Nine studies aimed to change attitudes or behaviors. Pedagogical approaches included content creation, personalized learning, narrative-based empathy development, and gamification. Only one study did not specify a clear theoretical framework. Standardized effect sizes were reported in only four studies. The geographical distribution was concentrated in East Asia. One study included a delayed post-test, leaving the long-term sustainability of the reported effects largely uncertain. Longitudinal designs, comparative tool evaluations, and culturally embedded AI designs from underrepresented regions have been identified as priority research needs for the field's advancement.

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