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Project # 27
Mav-Wayfinder
| Team Lead: | Rosangela Caicedo |
| Other Team Members: | |
| Faculty Advisor(s): | Dr Lebboulluec |
| Department: | IMSE |
| Project Type: | Other Undergraduate |
| Title: | Mav-Wayfinder |
| Abstract: | Navigating university bureaucracy often forces students to spend 15+ minutes digging through fragmented websites, outdated PDFs, and phone queues for simple answers regarding parking rules or citation appeals. Mav-Wayfinder eliminates this friction by utilizing a Python-based smart campus assistant. By integrating AI language models with intelligent document retrieval, the system processes natural language queries to provide precise, cited answers directly from official sources. The framework utilizes specialized databases and semantic search to ground every response in verified campus knowledge, ensuring accuracy and eliminating AI hallucinations. The result transforms a frustrating "information scavenger hunt" into instant clarity, delivering verified facts in under 30 seconds. While optimized for UT Arlington, the architecture is designed for universal deployment, providing a blueprint for any institution to modernize student support through AI-driven search. |
| Poster: | View Poster #27 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |