The growing adoption of digital education has transformed how children interact with learning environments. However, traditional e-learning platforms often lack engagement, interactivity, and physical involvement for young learners. This review focuses on the integration of computer vision technologies such as OpenCV and MediaPipe to create interactive, gesture-based educational systems. It highlights existing works like Virtual Canvas for Interactive Learning using OpenCV and Virtual Air Canvas using OpenCV and MediaPipe, examining how vision-based gesture tracking can support handwriting, drawing, and motion-controlled interaction. The proposed system, KidoVision, aims to enhance learning experiences through gamified content, real-time gesture detection, and story-based modules, promoting creativity and active participation. The review concludes that combining computer vision with gamified pedagogy can significantly improve cognitive development and motivation among children.

This paper reviewed the evolution of computer-vision-based interactive learning systems and proposed KidoVision, a gesture-driven e-learning framework aimed at children. Unlike conventional platforms that rely on touch or keyboard inputs, KidoVision employs OpenCV and MediaPipe to recognize real-time gestures, enabling intuitive and playful interaction. The system aligns technological innovation with educational theory, fostering creativity, focus, and inclusivity. By bridging computer vision, gamified pedagogy, and child psychology, KidoVision contributes to a new era of experiential learning where knowledge is both seen and felt. Its modular design allows future integration of AI tutors, emotion sensing, and AR environments, ensuring adaptability for the next generation of educational systems. Ultimately, KidoVision demonstrates that technology, when thoughtfully designed, can restore the human element of joy, curiosity, and motion in digital learning.

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