AI Powered Academic Advisor

Academic advising often faces challenges with manual course planning, prerequisite tracking and schedule optimization. This project presents an AI powered mobile application designed to streamline academic planning for students. The application addresses the complexity of managing degree requirements, prerequisite chains and course scheduling constraints.
The system employs a hybrid approach combining rule-based [4] algorithms and artificial intelligence. The mobile frontend is built using Flutter for cross platform compatibility, while a Python FastAPI backend handles data processing and intelligent decision making. The application parses student contract sheets through PDF text extraction, automatically distinguishing completed courses from remaining requirements based on grade patterns and enrollment status. A scheduling algorithm generates optimal course schedules by validating prerequisites, detecting time conflicts between Monday-Wednesday and Tuesday-Thursday course patterns and respecting credit limits.
The AI component provides personalized academic predictions including graduation timeline estimation, credit velocity analysis, and bottleneck course identification. The system calculates progress percentages, identifies courses that block multiple requirements and prioritizes course selection based on prerequisite dependencies. An integrated chatbot powered by OpenAI’s language model offers conversational academic guidance, answering student queries about course selection, career paths and degree planning.
The implementation achieved measurable success across all major components. The PDF parser attained a 90% test pass rate for AUST contract sheet, successfully parsing and categorizing student progress across all validation scenarios. The scheduling algorithm passed all six core test cases, correctly validating prerequisites, detecting meeting pattern conflicts and enforcing credit constraints. Schedule generation completed successfully supporting real-time user interactions.
These results demonstrate that the application effectively reduces manual planning effort while providing data driven insights for academic success. This tool represents a practical implementation of AI in educational technology, offering scalable solutions for academic advising automation in higher education institutions.