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

arXiv: https://arxiv.org/abs/2604.26958 · HTML: https://mlsanity.com/paper/2604.26958/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [Why human-AI relationships need socioaffective alignment](https://mlsanity.com/paper/2502.02528/) (2025)
- [Towards Multimodal Emotional Support Conversation Systems](https://mlsanity.com/paper/2408.03650/) (2024)
- [Evolution of AI in Education: Agentic Workflows](https://mlsanity.com/paper/2504.20082/) (2025)
- [From Passive Tool to Socio-cognitive Teammate: A Conceptual Framework for Agentic AI in Human-AI Collaborative Learning](https://mlsanity.com/paper/2508.14825/) (2025)

## 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.