Human–Machine Cooperation in Environmental Education: Experimental Evidence from AI-Supported Learning in Higher Education

Faed Mahmoud Buojaylah Fayid, Askin Kiraz

Higher education institutions are under increasing pressure to strengthen environmental education (EE) due to critical environmental challenges, while also addressing learner support, engagement, and instructional resource constraints. Recent advances in conversational artificial intelligence (AI), particularly generative AI systems based on large language models such as ChatGPT, enable new forms of human–machine cooperation and provide opportunities for interactive guidelines and individualized feedback. This study evaluates AI-supported EE compared with conventional classroom instruction using a quasi-experimental pre-test/post-test research design. Forty undergraduate students from a Libyan university were recruited and assigned to either the AI-supported EE group (n = 20) or a conventional classroom control group (n = 20). Both groups followed the same EE curriculum over eight weeks. Learning outcomes were assessed across environmental knowledge, attitudes, and environmentally responsible behavior using structured instruments. Paired-samples t-tests indicated statistically significant improvements within the AI-supported group across all outcomes (p < 0.05). However, between-group comparisons did not show statistically significant differences. Analysis controlling for baseline differences indicated a statistically significant group effect for knowledge (p < 0.05), while attitudes and behavior remained non-significant. These findings suggest that AI-supported learning may support EE learning for higher education.