Adaptive, interactive learning systems, as well as multimodal learning systems, are required in early childhood education so that it can provide development learning as the fundamental need of a child. Conventional ways are usually unengaging and unresponsive in real-time, more so to the non-reading students. An early learning assistant that incorporates speech recognition and concept mapping based on AI to mitigate the limitations. The system uses a child-specific automatic speech recognition (ASR) model trained on speech datasets that it curated and a dynamic concept mapping engine that can create knowledge graphs out of the speech given to it. With the help of deep learning powered intent recognition and answer feedback loops, the assistant keeps to the learner’s pace and knowledge level. Experimental tests indicate a Word Error Rate (WER) of 8.2% effects, a mapping concept accuracy of 92.6% and less than 284 ms response latency, and an engagement score of 4.6/5. A better performance in semantic understanding and personalization than previous systems. The proposed solution secures accessibility, responsiveness, and effectiveness of early education with an intelligent conversational learning.