Artificial intelligence technologies in the management of environmental educational projects: A pedagogical experiment

Despite growing empirical research on artificial intelligence (AI) in education, studies specifically addressing AI-driven management of environmental educational projects in higher education institutions (HEIs) remain scarce. This study addresses that gap by developing and validating the Integrated AI-driven Environmental Educational Project Management Framework (IAEPMF) – a four-level model combining adaptive machine learning (ML), generative AI, Retrieval-Augmented Generation (RAG), and cloud platforms to personalize and optimize environmental educational project workflows. A controlled pedagogical experiment was conducted over 18 weeks (2025) at three Ukrainian HEIs involving 120 students from environmental specialities (101 “Ecology”, 014.05 “Secondary Education: Biology and Human Health”, 017 “Physical Culture and Sports”). Participants were assigned to a control group (n=62, traditional instruction) and an experimental group (n=58, IAEPMF-based instruction). Data were collected through diagnostic tests, structured knowledge assessments, project evaluations, and student surveys covering nine performance indicators: academic performance, environmental competencies, task-completion speed, project work quality, learning self-reliance, motivation, digital literacy, critical thinking, and group work skills. Statistical analysis applied Student’s t-test and descriptive statistics using IBM SPSS Statistics 27. The experimental group demonstrated statistically significant improvements across all nine indicators (p<0,01). Academic performance increased by 25,0% (t=13,24), environmental competencies by 24,6% (t=10,78), digital literacy by 25,5% (t=6,45), group work skills by 24,7% (t=8,54), and critical thinking by 16,4% (t=6,71). The quality success rate rose from 34,5% to 60,3% in the experimental group, compared with 32,3% to 35,5% in the control group. The IAEPMF is confirmed as an effective, empirically validated instrument for transforming environmental higher education through AI-driven project management. The study identifies key systemic challenges and provides practical recommendations on phased integration, interdisciplinary team formation, external funding, faculty professional development, and cybersecurity compliance, contributing to the global evidence base for sustainable digital transformation in higher education.