Vision-and-Language Navigation (VLN) agents typically predict actions end-to-end, lacking explicit awareness of their spatial state, task progress, or alignment with natural language instructions. AwareVLN introduces structured, self-aware reasoning that is selectively triggered at key navigation waypoints rather than consuming computation at every timestep. The model maintains an internal belief over task progress and spatial consistency, monitoring its own state for signs of uncertainty or misalignment with the instruction. When divergence is detected—such as entering the wrong room or missing a referenced landmark—AwareVLN invokes an explicit reasoning module that analyzes possible corrections and selects the most promising recovery action. This selective approach reduces unnecessary computation while improving navigation success rates. Evaluated on standard VLN benchmarks including R2R and REVERIE, AwareVLN achieves higher success and lower trajectory lengths than baseline end-to-end models. Importantly, the reasoning traces produced by AwareVLN are human-interpretable, making it easier to diagnose failure modes and build trust in embodied agents. The framework is compatible with existing VLN architectures and can be inserted as a lightweight reasoning head with minimal architectural changes.