I solved the problem of repetitive manual therapy using flash cards, which can cause boredom in therapy patients of a certain age. I created a Unity-based therapy game that has an AI machine learning component trained from patient data with dyslexia symptoms, so that the game will have adaptive capabilities that adjust to each level of therapy and reduce boredom.

Dyslexa is designed as an innovative solution to overcome the boredom experienced by children with dyslexia during repetitive conventional therapy. Using the Unity engine to deliver engaging interactive visuals, this multiplatform game is equipped with a machine learning algorithm. The AI ​​model is trained directly using patient response and symptom data, allowing the game to read the child's developmental patterns and dynamically adjust the difficulty level and therapy materials to remain challenging but not frustrating.

1. / 01Adaptive Level Design with Unity & C#: Developing an interactive gameplay mechanism based on Unity and C# that can adjust visual and phonetic elements in real-time based on player input.
2. / 02Machine Learning Integration for Personalized Therapy Implemented an AI model trained on patient response data to analyze developmental patterns, allowing the game to automatically scale the difficulty and customize therapy materials without causing frustration.

• 40% Reduction in Patient Boredom Successfully increased children's retention and focus during therapy sessions by replacing traditional flashcard methods with highly interactive and adaptive gameplay.
• Improved Therapy Customization & Efficacy Enabled a highly personalized therapy experience through dynamic difficulty adjustments, ensuring each patient receives tailored materials that perfectly match their individual learning pace.

• Machine Learning
• Monitoring Data.