This study presents a technically grounded design and implementation of an AI- and AR-enabled immersive learning environment for outdoor education. Moving beyond conceptual descriptions, the study develops an executable system framework that integrates adaptive navigation and positioning, context-aware virtual tours, task-driven scenario simulation, and real-time feedback mechanisms. Each functional module is explicitly linked to algorithmic implementations, including multi-sensor state estimation, constrained generative scene construction, and reinforcement-based adaptive control, enabling reproducible system behavior in real outdoor settings. A controlled field experiment was conducted using an experimental group and a control group under identical instructional conditions. Quantitative evaluation based on pre–post testing, behavioral logging, and statistical analysis demonstrates that the proposed system achieves statistically significant improvements in learning interest, participation, knowledge mastery, and problem-solving ability. Experimental conditions, data characteristics, and methodological limitations are explicitly reported to support result verification and generalizability. The findings indicate that the proposed immersive learning environment constitutes a validated system-level contribution rather than a purely conceptual framework, offering practical and scientific value for computer science–oriented educational technology research.
Chenguang Liu (Thu,) studied this question.