An Evidence-Based Gamified Decision-Making System for Biodiversity Conservation Education Integrating Educational Engineering, AI-Driven Evidence Synthesis and Experiential Learning

Abstract

Biodiversity conservation education is confronted with a critical evidence emergency, charac-terized by the lack of accessible scientific evidence in teaching and a severe disconnect between theoretical learning and real-world decision-making practice. To address this dilemma, this study constructs an Evidence-Based Gamified Decision-Making System (EBGDMS) based on educational engineering, AI-driven evidence synthesis and experiential learning theory. The system comprises three core integrated modules: an AI-powered biodiversity evidence knowledge base, a gamified decision-making simulation platform, and a multi-dimensional evaluation system for evidence literacy and decision-making competence. A 12-week quasi-experimental study was conducted with 386 undergraduate students majoring in environmental science and education from 5 universities in China and the UK, with the experimental group adopting EBGDMS and the control group using traditional lecture-based teaching. Results show that: (1) EBGDMS significantly improves students’ evidence literacy (post-test score increased by 34.7%, p < 0.001), with the highest growth
in evidence application ability (+37.4%); (2) The system enhances conservation decision-making performance by 39.2% (p < 0.001); (3) AI-driven evidence synthesis reduces the evidence accessibility gap by 62.5% for students; (4) Gamified decision-making simulation exerts a significant partial mediating effect (β = 0.47 , p < 0.001) between AI evidence input and decision-making competence development, accounting for 47.0% of the total effect; (5) EBGDMS boosts students’ learning engagement by 31.5% (p < 0.001). This study expands the cross-innovation framework of educational engineering in biodiversity conservation education,
verifies the effectiveness of EBGDMS in bridging the evidence-practice divide, and provides a scalable evidence-based teaching model for cultivating conservation practitioners with scientific decision-making ability, which has important theoretical and practical significance for advancing Education for Sustainable Development (ESD) and addressing the global biodiversity evidence crisis.