Emotion-Aware Adaptive Learning System: Integrating Reinforcement and Affective Computing
Mrs. P. Sireesha¹, C. Taruni², K. Srinivas3, G. Abhishek4
Assistant Professor, 1Department of Computer Science and Engineering,
Methodist College of Engineering and Technology Abids, Hyderabad, Telangana, 500001, India.
2,3,4Department of Artificial Intelligence and Data Science,
Methodist College of Engineering and Technology Abids, Hyderabad, Telangana, 500001, India.
Abstract— An innovative adaptive and intelligent e-learning system named the Emotion-Aware Adaptive Learning System (EAALS) takes into account a learner’s emotional (feeling) and cognitive (thinking) state when requesting educational materials. Traditional e-learning systems only look at a learner’s academic performance (outcomes) to determine how well they did, and deliver content uniformly to all learners (static). Because the learner’s mood or emotional state can have a large impact on how they learn, traditional e-learning systems do not consider the emotional state of a learner when writing educational content. With the objective of creating an adaptive and dynamic learning system with emotional state recognition for personalized content generation, the proposed EAALS combines the power of AI, affective computing, and reinforcement learning. Multimodal emotion detection systems that utilize voice-based emotion detection, text-based sentiment analysis using NLP, and facial expression analysis (using OpenCV) will be included in the EAALS. A smart and adaptive system, empowered with language model technology, designs a personalized learning experience. Adjustments to explanations, quizzes, and even graphics are made in accordance with a user’s emotional state and preferences. To reinforce or optimize the learning strategies of each student, reinforcement learning will be used in conjunction with a unique state-action-reward model related to the individual learning style and the individual level of difficulty for each learner. Additional system features will include text-to-speech interactive features, individualized learning materials based on each student's areas of interest, and an advanced administrative dashboard for monitoring and managing the performance and progress of each individual student.
Keywords: Emotions in Learning, Adaptive Learning Systems, Reinforcement Learning, Affective Computing, Multimodal Emotion Recognition, Artificial Intelligence, NLP, Sentiment Analysis, Personalized Learning Experiences, Intelligent Tutoring Systems, Human-Computer Interaction, New Educational Technologies
