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In an era where empathy feels unfamiliar, AI now translates emotions

June 19, 2025
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June 19, 2025

The GIST In an era where empathy feels unfamiliar, AI now translates emotions

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In an era where empathy feels unfamiliar, AI now translates emotions
The operation process of the AI agent EmoSync, which generates personalized analogy to elicit target emotions based on users' personal information. Credit: POSTECH

A research team at POSTECH (Pohang University of Science and Technology, South Korea) has developed AI technology that helps individuals deeply understand others' emotions by analyzing individual personality traits and values and generating personalized analogy.

This study was recognized with the "Popular Choice Honorable Mention Award," given to the top 5% of 74 Interactivity track demonstrations at ACM CHI 2025, the world's leading international conference in Human-Computer Interaction (HCI).

Society is a complex community where people with different identities and diverse backgrounds live together. While people strive to understand each other, even the concept of "empathy" can sometimes feel overwhelming—because even in the same situation, emotions can differ greatly from person to person.

Until now, computer-based empathy technologies have been operating on the assumption that showing the same experience would evoke similar emotions. However, reality is more complicated: emotional reactions vary widely depending on an individual's personality, past experiences, and values.

"EmoSync," an LLM-based agent, embraces and utilizes these individual differences. By meticulously analyzing each user's psychological traits and emotional response patterns, the LLM generates personalized analogical scenarios that allow people to understand others' feelings through the lens of their own experiences.

For example, if a user struggles to empathize with subtle discrimination or exclusion in the workplace, EmoSync analyzes the user's past experiences and creates a relatable connection, such as "a moment of feeling excluded by peers during school days." This approach helps users understand others' emotions more vividly and realistically by using the lens of familiar experiences.

Toward Affective Empathy via Personalized Analogy Generation: A Case Study on Microaggression. Credit: POSTECH

The research team conducted experiments involving over 100 participants from diverse backgrounds using this technology. The results showed that participants who used EmoSync demonstrated significantly improved emotional understanding and empathy compared to traditional methods. This scientifically demonstrates that personalized metaphorical experiences can genuinely enhance empathy.

Hyojin Ju, the first author of the study, said, "Our research demonstrates that AI can be used to facilitate genuine understanding and empathy among people," and added, "We will continue to develop AI technologies that help foster true understanding and empathy in real-life situations."

Professor Inseok Hwang of POSTECH commented, "This study is a successful example showing that generative AI can identify each user's unique emotional structure and generate personalized experiences that induce specific emotions. It represents a novel and meaningful approach—both academically and socially—to fostering empathy in ways that were not possible before."

This research was conducted by Professor Inseok Hwang and Ph.D. students Hyojin Ju, Jungeun Lee, and Seungwon Yang from POSTECH's Department of Computer Science and Engineering, in collaboration with Professor Jungseul Ok.

More information: Hyojin Ju et al, Toward Affective Empathy via Personalized Analogy Generation: A Case Study on Microaggression, Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (2025). DOI: 10.1145/3706598.3714122

Provided by Pohang University of Science and Technology Citation: In an era where empathy feels unfamiliar, AI now translates emotions (2025, June 19) retrieved 19 June 2025 from https://techxplore.com/news/2025-06-era-empathy-unfamiliar-ai-emotions.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

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Disclaimer: Information found on cryptoreportclub.com is those of writers quoted. It does not represent the opinions of cryptoreportclub.com on whether to sell, buy or hold any investments. You are advised to conduct your own research before making any investment decisions. Use provided information at your own risk.
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© 2023-2025 Cryptoreportclub. All Rights Reserved