Designing Ethical Learning for Agentic AI: Toegye Yi Hwang's Ethical Emotion Regulation Framework
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Abstract (arXiv)
Agentic AI systems capable of autonomous goal setting and proactive intervention introduce new challenges for regulating moral-emotional processes in learning environments. Existing frameworks typically treat emotion as reactive feedback or engagement optimization, overlooking the need for normative regulation across autonomous decision cycles.This paper proposes an ethical emotion regulation framework for agentic AI learning design inspired by Toegye Yi Hwang's moral-emotional philosophy. The Ethical Emotion Feedback System (EEFS) is reconstructed as a five-stage architecture aligned with agentic cycles, articulating stage-specific design principles and scenario classifications.An EEFS Evaluation Instrument is introduced to enable systematic assessment of moral-emotional alignment in agentic AI systems.
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References · 5
- 2019Principles alone cannot guarantee ethical AIcited by 135
- 2025Why human-AI relationships need socioaffective alignmentcited by 47
- 2024Towards Multimodal Emotional Support Conversation Systemscited by 23
- 2025Evolution of AI in Education: Agentic Workflowscited by 13
- 2025From Passive Tool to Socio-cognitive Teammate: A Conceptual Framework for Agentic AI in Human-AI Collaborative Learningcited by 3
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- 2025Developmental Support Approach to AI's Autonomous Growth: Toward the Realization of a Mutually Beneficial Stage Through Experiential Learning
- 2024Informed AI Regulation: Comparing the Ethical Frameworks of Leading LLM Chatbots Using an Ethics-Based Audit to Assess Moral Reasoning and Normative Values
- 2025Addressing Moral Uncertainty using Large Language Models for Ethical Decision-Making
- 2026MoralityGym: A Benchmark for Evaluating Hierarchical Moral Alignment in Sequential Decision-Making Agents