Emotion Aware AI for Learning and Organizations: An Affective Computing Framework for Adaptive Human AI Interaction

Abstract

Artificial Intelligence is dramatically changing how we learn at school and work. While many artificial intelligence applications exist today, none can sense or react to the emotional and motivational states that underlie all human learning, engagement and decision-making. This study formally defines Emotion-Aware and Affective AI Systems as a new paradigm for personalized Human – AI Interaction and Adaptive Experience Intelligence in Digital Ecosystems. Drawing on Constructivist Theory, Sociocultural Theory, Experiential Learning Theory, Self-Determination Theory, Cognitive Load Theory, and Flow Theory, the framework views emotions as an active agent in shaping meaning-making, engagement and knowledge-building. To develop emotion-aware AI as a strategic organizational capability, these pedagogical theories are strategically combined with Resource-Based View, Dynamic Capabilities Theory, Knowledge-Based View, Transaction Cost Economics, Technology Acceptance Model, and Theory of Planned Behavior. The proposed Emotion-Aware Learning and Decision Framework (EALDF) is a layered architecture designed to enable: Perception; Cognitive Interpretation; Adaptive Decisions; and Experience Modulation Layers. To achieve this, the proposed framework processes multimodal signals (textual, vocal, facial, behavioral and contextual) using transformer based language models (e.g., BERT and RoBERTA), Convolutional and Recurrent Neural Networks (CNN/RNN), Speech Emotion Models (MFCC-LSTM Pipelines), and Multimodal Fusion Architectures (Cross-Attention Network and Graph Neural Network). Using Multidimensional Valence-Arousal-Dominance Vectors Embedded Within Markov Decision Processes and Partially Observable MDPs, emotional dynamics are mathematically modeled. Reinforcement Learning Algorithms (Deep Q-Networks, Proximal Policy Optimization, and Actor-Critic) govern adaptive responses to user emotional states. This framework has been situated within Adaptive Learning Systems, Intelligent Tutoring Platforms, Leadership Development Tools, and Digital Enterprise Environments, to provide Real-Time Personalization, Enhanced Engagement and Optimize Decision Outcomes. In addition, the study establishes Quantitative and Qualitative Evaluation Models to address Bias, Transparency, Governance and Ethical Constraints. Overall, this study introduces a Theoretically Grounded, Computationally Robust and Strategically Scalable Blueprint for Next Generation Emotion Aware AI Systems, which will enhance both Educational Practice and Organizational Intelligence.