INSPIRATION
Every student learns differently, but traditional education often provides the same learning materials and assessments to everyone. Some students understand concepts quickly, while others struggle with specific topics or lack the prerequisite knowledge needed to progress. We were inspired to address this problem by building an AI-powered system that personalizes learning based on each student's understanding, identifies knowledge gaps, and recommends suitable learning resources aligned with Course Outcomes (COs). Our goal is to make learning more adaptive, effective, and outcome-oriented.
WHAT IT DOES
The AI-Based Learning Path Generator creates personalized learning paths based on a student's selected subject, learning level, assessment performance, and identified knowledge gaps.
Personalized assessment: Evaluates students' understanding through questions related to the selected subject. Course Outcome mapping: Connects concepts and assessments with the relevant Course Outcomes (COs). Knowledge gap detection: Identifies concepts that students have not understood properly, including missing prerequisite knowledge. Adaptive learning resources: Recommends relevant videos, tutorials, and learning materials to address identified gaps. Reassessment and progress tracking: Evaluates students again after they study the recommended resources and compares their performance. The system aims to close the learning loop by using reassessment results to guide the next learning steps rather than simply recommending resources once.
HOW WE BUILT IT
We designed the project as a student platform with a structured learning workflow. Students select a subject and learning level, complete an initial assessment, receive a personalized learning path, study recommended resources, and complete a reassessment.
The system organizes assessment results around concepts and Course Outcomes to identify learning gaps and recommend appropriate interventions.
We developed the user interface with a focus on simplicity and ease of navigation. The project was designed to demonstrate the complete workflow, from initial assessment to adaptive recommendations.
Our planned technical approach combines Python for core logic, Streamlit for the interface, an LLM API and an AI agent framework for intelligent recommendations, Pandas and NumPy for data processing, and SQLite or Firebase for storing learning and assessment data.
CHALLENGES WE RAN INTO
One of our main challenges was designing a meaningful personalized learning workflow instead of providing the same resources to every student.
Another challenge was mapping learning activities to Course Outcomes and identifying prerequisite gaps rather than treating every incorrect answer as an isolated mistake.
Balancing personalization, usability, and the limited time available for development was another important challenge. We addressed these challenges by breaking the workflow into clear stages and focusing on the connection between assessment, gap detection, targeted learning, and reassessment.
ACCOMPLISHMENTS THAT WE ARE PROUD OF
Designed an end-to-end workflow for personalized and adaptive learning. Connected knowledge gap identification with targeted learning resources and reassessment. Incorporated Course Outcome alignment to support Outcome-Based Education. Developed a project that demonstrates how AI can support students. WHAT WE LEARNED
We learned that effective AI-powered education involves more than generating questions or recommending content. A useful system must understand what a student knows, identify what is missing, recommend the right intervention, and verify whether learning has improved.
We also gained a better understanding of how AI, assessment analytics, prerequisite mapping, and Outcome-Based Education can work together.
WHAT'S NEXT FOR AI LEARNING PATH GENERATOR FOR OUTCOME-BASED EDUCATION (OBE)
Support more subjects with subject-specific assessments, concept mappings, and relevant learning materials. Build a question bank with varied initial assessments and reassessments that measure the same concepts without different questions. Integrate reliable learning resources, including educational videos and tutorials. Improve prerequisite gap detection and dynamically adapt learning paths based on reassessment performance. Develop faculty dashboard for tracking student progress and Course Outcome attainment. Implement secure authentication, persistent data storage, and deployment for real-world testing. Evaluate the system using learning improvement, assessment performance, recommendation relevance, and CO attainment metrics. Our long-term vision is to make personalized, measurable, and outcome-oriented learning accessible to students.