In-Depth Exploration of The Multifaceted Landscape of Emotion AI: From Education and Workplace to Psychiatry

The review examines various aspects of Emotion AI across different domains, focusing on the challenges of educational implementation and unintended harms. The analysis emphasizes the impact of Emotion AI on surveillance, emotional labor, and privacy in the workplace. Additionally, the exploration includes the creation of AI-generated art for self-expression and a comparison of Emotion AI models, particularly machine learning versus deep learning approaches. The role of Emotion AI in education is also considered, especially in the context of translators. The review envisions a transition from Emotion AI to Cognitive AI, integrating both emotions and cognition. The discussion further covers the commercial, mental health, and educational implications of emotion recognition technology. The review concludes by highlighting the ethical considerations for integrating Emotion AI into educational technology, emphasizing its potential for societal benefit.

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Publication Details

Published
2024-11-23
DOI
https://doi.org/10.1109/icicat62666.2024.10922913
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
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article

In-Depth Exploration of The Multifaceted Landscape of Emotion AI: From Education and Workplace to Psychiatry

M S Nidhishree, Anupama Girish, S. Amutha, Ramesh Babu D R
Emotion and Mood Recognition
article

In-Depth Exploration of The Multifaceted Landscape of Emotion AI: From Education and Workplace to Psychiatry

M S Nidhishree, Anupama Girish, S. Amutha, Ramesh Babu D R
article en

Abstract

The review examines various aspects of Emotion AI across different domains, focusing on the challenges of educational implementation and unintended harms. The analysis emphasizes the impact of Emotion AI on surveillance, emotional labor, and privacy in the workplace. Additionally, the exploration includes the creation of AI-generated art for self-expression and a comparison of Emotion AI models, particularly machine learning versus deep learning approaches. The role of Emotion AI in education is also considered, especially in the context of translators. The review envisions a transition from Emotion AI to Cognitive AI, integrating both emotions and cognition. The discussion further covers the commercial, mental health, and educational implications of emotion recognition technology. The review concludes by highlighting the ethical considerations for integrating Emotion AI into educational technology, emphasizing its potential for societal benefit.

Dr. Hari Singh Gour University (IN)
Openalex Percentile: Top 67%
Emotion and Mood Recognition
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