Personalized AI Tutor An Intelligent Adaptive Learning System for Early Education

Personalized learning is increasingly gaining importance in the field of educational technology, particularly among young learners who require interactive tools and personalized content based on their learning speeds. Most learning platforms currently available offer identical content to all learners irrespective of differences in their comprehension, attention, and revision capabilities. This paper introduces an innovative AI Tutor designed for students in classes 1 to 5 by integrating adaptive learning, quizzes, speech communication, and analytical features in one application. The system uses HTML, CSS, JavaScript, Firebase Authentication, Firebase Cloud Firestore, and web browser speech recognition technologies. The learning content includes topic-specific lessons related to alphabets, numbers, shapes, colors, animals, fruits, transport, and objects. Student participation is monitored based on quiz scores, progress level, errors made, pronunciation practice, and completion status of topics. The system recommends relevant next topics for learning, modifies the practice flow, and facilitates smooth learning progression based on user data. Experimental observations indicate enhanced engagement, efficient topic tracking, and valuable personalization suggestions for early learners.

Personalized learning, artificial intelligence, adaptive learning, educational technology, Firebase, speech recognition

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