About the Project Inspiration
The idea for Manabi came from observing how differently students understand the same topic. Some grasp a concept quickly, while others need visual support, analogies, or step-by-step guidance. I realized that most learning platforms still follow a one-style-fits-all model, which leaves many learners behind, especially those with ADHD, autism, or sensory impairments. This gap inspired me to build a system that adapts to the learner instead of forcing the learner to adapt to the system.
What I Learned
Building this project taught me how deeply cognitive differences affect learning. I explored learning styles, accessibility needs, instructional design, and how AI models can restructure content in different formats. I also learned how adaptive systems use feedback loops to evolve with user behavior.
How I Built It
The project starts with a short assessment designed to identify a student’s learning style. Based on the responses, the system classifies the learner into a preferred category using pattern-based logic. After this, lessons are generated dynamically using predefined templates and adaptive rules.
Challenges Faced
-Designing assessments that accurately capture learning preferences -Creating lesson structures that adapt meaningfully to different learning types -Ensuring the experience is inclusive for ADHD, autistic, visually impaired, and hearing-impaired learners -Balancing simplicity and depth in content generation
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