@inproceedings{0e14d14d174449188f1f429eedd8c724,
title = "Mirror Hearts: Exploring the (Mis-)Alignment between AI-Recognized and Self-Reported Emotions",
abstract = "Previous research has found that learners' reflections on their own emotions can improve their learning experience, and AI-based technologies can be used to automatically recognize learners' emotions. We conducted user studies involving 32 participants to investigate the relationship between AI-recognized emotions and their self-reported emotions using emojis and text comments. We found that, even though AI recognized a similar amount of positive-high-arousal and negative-low-arousal emotions, participants self-reported more positive-high-arousal emojis. Participants explained the causality and temporality of the self-reported emojis using text comments. Our findings suggest ways of using AI to capture learners' emotions and support their reflections.",
keywords = "Emoji, Facial Recognition, Reflection, Self-Regulated Learning",
author = "Si Chen and Haocong Cheng and Jason Situ and Yun Huang",
note = "This material is based upon work supported by the National Science Foundation under Grant No. 2119589. The authors would like to thank the anonymous reviewers for their eforts and valuable comments.; Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023 ; Conference date: 23-04-2023 Through 28-04-2023",
year = "2023",
month = apr,
day = "19",
doi = "10.1145/3544549.3585607",
language = "English (US)",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
booktitle = "CHI 2023 - Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems",
address = "United States",
}