Personalized Educational Agents with Adaptive Cognitive Modes: Reinforcement Learning for Fast Feedback and Deep Reasoning
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 infrastructureAbstract
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.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
