Personalized Educational Agents with Adaptive Cognitive Modes: Reinforcement Learning for Fast Feedback and Deep Reasoning (Journal of Advanced Artificial Intelligence Research, 2026)
The convergence of reinforcement learning and cognitive science offers a transformative pathway for the design of personalized educational agents capable of dynamically adjusting their interaction modes to match learner needs. This paper presents a comprehensive framework for educational agents that employ adaptive cognitive modes, switching between fast, reflexive feedback loops and slow, deliberative reasoning processes based on real-time assessment of learner state and task complexity. We argue that a dual-process architecture, inspired by Kahneman’s model of fast and slow thinking, can be operationalized through reinforcement learning policies that optimize for both immediate engagement and long-term knowledge consolidation. The system integrates memory-augmented knowledge fusion, safety-aware decoding, and predictive models of response quality to ensure robustness and equity across diverse learner populations. Infrastructure considerations including cloud-edge deployment, continuous model updating, and interpretability mechanisms are discussed alongside governance challenges such as algorithmic bias, data privacy, and the ethical boundaries of automated feedback. Cross-domain comparisons with adaptive tutoring systems, intelligent tutoring systems, and large language model-based assistants highlight the unique trade-offs inherent in the proposed architecture. The paper concludes with forward-looking perspectives on the sustainability, fairness, and policy implications of deploying such agents at scale in formal and informal educational settings.
Keywords: personalized educational agents, adaptive cognitive modes, reinforcement learning, fast and slow thinking, intelligent tutoring systems, educational technology governance, fairness in AI, memory-augmented learning, safety-aware decoding, socio-technical infrastructure