Designing Ethical Learning for Agentic AI: Toegye Yi Hwang's Ethical Emotion Regulation Framework

TL;DR

This paper introduces a novel ethical emotion regulation framework for agentic AI, inspired by Toegye Yi Hwang's philosophy, aiming to improve moral-emotional alignment in autonomous learning systems.

Contribution

It proposes the Ethical Emotion Feedback System (EEFS), a five-stage architecture for normative emotion regulation in agentic AI, along with an evaluation instrument.

Findings

EEFS provides a structured approach to moral-emotional regulation.

The evaluation instrument enables systematic assessment of AI moral-emotional alignment.

Framework aligns with agentic decision cycles for better ethical behavior.

Abstract

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.