An Overview of Emotion in Artificial Intelligence
Abstract
The field of artificial intelligence (AI) has gained immense traction over the past decade, producing increasingly successful applications as research strives to understand and exploit neural processing specifics. Nonetheless emotion, despite its demonstrated significance to reinforcement, social integration, and general development, remains a largely stigmatized and consequently disregarded topic by most engineers and computer scientists. In this article, we endorse emotion’s value for the advancement of artificial cognitive processing, as well as explore real-world use cases of emotion-augmented AI. A schematization is provided on the psychological-neurophysiologic basics of emotion in order to bridge the interdisciplinary gap preventing emulation and integration in AI methodology, as well as exploitation by current systems. In addition, we overview three major subdomains of AI greatly benefiting from emotion, and produce a systematic survey of meaningful yet recent contributions to each area. To conclude, we address crucial challenges and promising research paths for the future of emotion in AI with the hope that more researchers will develop an interest for the topic and find it easier to develop their own contributions.
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Current tools are constrained by basic, static, and discrete emotional representations, failing to meet participants' expectations for continuous and dynamic emotional simulation.
“Current tools are constrained by basic, static, and discrete emotional representations [6, 36], failing to meet participants' expectations for continuous and dynamic emotional simulation.”
From Understanding Screenwriters' Practices, Attitudes, and Future Expectations in Human-AI Co-Creation (2025) · cites this paper as [6]
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Understanding Screenwriters' Practices, Attitudes, and Future Expectations in Human-AI Co-Creation
Tang, Y., Li, H., Lan, M., Ma, X., & Qu, H. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714120 · Cited by 18 · see citing works
“We pro-pose three key dimensions for acquiring data: social media feedback, high-quality works, and crowdsourced observations.”
- Authors
- Assunção, G., Patrão, B., Castelo-Branco, M., & Menezes, P.
- Affiliations
- University of Coimbra (PT)
- Venue
- IEEE Transactions on Artificial Intelligence
- Published
- 2022-12
- Volume
- 3
- Issue
- 6
- Pages
- 867-886
- ISSN
- 2691-4581 (ISSN-L)
- OpenAlex
- W4225463334
- Cited by
- 103
- References
- 14
- Citations by year
- 2026: 28 · 2025: 42 · 2024: 24 · 2023: 8 · 2022: 1
- FWCI
- 6.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Artificial Intelligence (publishedVersion)
- Funders
- FCT (2020.05620.BD)
- OE - National funds of FCT/MCTES (UIDB/00048/2020)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- emotion recognition
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