Why We Need a New Framework for Emotional Intelligence in AI

• URL: http://arxiv.org/abs/2512.23163v1
• Date: Mon, 29 Dec 2025 03:05:05 GMT
• Title: Why We Need a New Framework for Emotional Intelligence in AI
• Authors: Max Parks, Kheli Atluru, Meera Vinod, Mike Kuniavsky, Jud Brewer, Sean White, Sarah Adler, Wendy Ju,
• Abstract summary: We develop the position that current frameworks for evaluating emotional intelligence in artificial intelligence (AI) systems need refinement.<n>Several benchmark frameworks specialize in evaluating the capacity of different AI models to perform some tasks related to emotional intelligence (EI)<n>We evaluate the available benchmark frameworks, identifying where each falls short in light of the account of EI developed in the first section.
• Score: 8.028801532150695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we develop the position that current frameworks for evaluating emotional intelligence (EI) in artificial intelligence (AI) systems need refinement because they do not adequately or comprehensively measure the various aspects of EI relevant in AI. Human EI often involves a phenomenological component and a sense of understanding that artificially intelligent systems lack; therefore, some aspects of EI are irrelevant in evaluating AI systems. However, EI also includes an ability to sense an emotional state, explain it, respond appropriately, and adapt to new contexts (e.g., multicultural), and artificially intelligent systems can do such things to greater or lesser degrees. Several benchmark frameworks specialize in evaluating the capacity of different AI models to perform some tasks related to EI, but these often lack a solid foundation regarding the nature of emotion and what it is to be emotionally intelligent. In this project, we begin by reviewing different theories about emotion and general EI, evaluating the extent to which each is applicable to artificial systems. We then critically evaluate the available benchmark frameworks, identifying where each falls short in light of the account of EI developed in the first section. Lastly, we outline some options for improving evaluation strategies to avoid these shortcomings in EI evaluation in AI systems.

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