Learning of languages through Computer Assisted technologies & IOT enabled devices

The smart, multi-layered system in this work uses multimodal data fusion, IoT-enabled contextual feedback, and flexible reinforcement learning to improve language acquisition. The system records speech, motion, and text inputs to tailor learning. These are converted to appropriate feature representations and assembled using attention-based weighting. These coupled vectors assess student skill and recommend learning tasks. Based on input quality and learner response, these exercises are altered live. The system uses IoT sensors to measure skin temperature, heart rate variability, and background sounds in addition to multimodal interpretation. These indications instruct context-aware feedback to provide it, which adapts to the learner’s mood and state. When the system senses outside distractions or internal fatigue, it modifies input and difficulty to make things clearer and more intriguing. A third level uses reinforcement learning. Learning success is tracked across sessions and stored in a memory bank. This information is used to adjust how the system reinforces skills for long-term memory and skill progress. Past trends inform future feedback. These trends combine rapid change and long-term growth. In this closed-loop approach, the system may adapt to provide more tailored assistance. The suggested method is easier, more flexible, aware of the context, and immerses the learner better than smart language learning tools. The technology is expandable, mobile-friendly, and understands emotions. It advances computer-assisted language learning for many sorts of students in structured and unstructured settings.