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The Intersection of AI and

Emotional Intelligence

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The Intersection of Artificial Intelligence (AI) and Emotional Intelligence (EI)
  • Artificial Intelligence explored.
  • Emotional Intelligence defined.
  • Intersection of the two – is it possible why it matters.

Artificial intelligence, intelligent machines, emotional intelligence; these are common terms you hear in use probably daily. You cannot move through the world today, 2025, without interacting with some version of one, if not all these phenomena. But what exactly is each one, what do they relate to, do they interact and if so when and how.

Artificial intelligence or AI may be the term most people feel most comfortable with. AI is merely a system or software that is coded to mimic or simulate human intelligence. The Encyclopedia Britannica defines AI as “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings,” such as reasoning, learning, and problem-solving. (1) It includes capabilities like learning from data, reasoning, understanding language, recognizing patterns, and making decisions.

In your home examples may include Alexa, Siri, or Google Assistant. On your personal devices you probably use Autocorrect and Predictive Text, Voice-to-Text and perhaps even Navigation services.

You may find it surprising to note the earliest refences to evaluating the possibility of machine learning goes back as far as the 1950’s. If you have seen the biographical movie of Alan Turin, The Imitation Game (2) you may already be familiar with the Turing Test. In 1950 Alan Turing proposed the Turing Test (2) to evaluate machine intelligence. Skipping forward a few decades to 2016 we come to OPEN AI with CHATGPT and in very recent years, (2023-2024) we see the advent of multimodal models, with advanced generations of GPT utilizing text, images, and reasoning in integrated responses. See Appendix page

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AI is no longer the stuff of science fiction, it is in phones, our alarm clock and even our playlists. While we live in a unique time, a time where our machines can even seem to understand human emotion and even seemingly understand and interact with us on a human level; but they are not human. The interaction with our machine is becoming so real that we can often forget they are not human.

Machines can’t feel emotion; they can only react based on a series of data points. AI is not wired for subtlety, a machine can’t raise an eyebrow, notice a tone or give a warm handshake or hug. Expectations are that AI will outpace human intelligence by 2045; but which human intelligence, humans function on a thinking level and an emotional level. This segues us to the concept of emotional intelligence.

The concept of emotional intelligence was first introduced by Daniel Goleman, a psychologist and scientific journalist, in 1964. It gained recognition and popularity within the workplace with the 1995 publication of his book, “Emotional Intelligence. Why it can Matter More Than IQ”. So, what is emotional intelligence and how does this affect my hiring process

Emotional intelligence (EI), also known as emotional quotient (EQ), is defined as the ability to recognize, understand, and manage your own and others' emotions. It involves perceiving, using, understanding, and regulating emotions to facilitate thoughts and actions, leading to more satisfying personal and professional relationships (3)

That last line, “more satisfying personal and professional relationships”. It is not specifying human relationship. AI can develop a set of rules or definitions around the concept and some facial recognition software has been coded to be able recognize and respond certain facial expressions, leading to a perceived connection. As technology improves this awareness will become even more refined. There are certainly situations where these interactions make sense and can provide value, but do we also run the risk of losing human connection.

To Consider:

Points of Connection
  • AI-enhanced EI tools: Sentiment analysis, emotion recognition software, and adaptive learning platforms.
  • Emotionally intelligent AI: Chatbots and virtual assistants designed to respond empathetically.
  • Leadership synergy: Leaders using AI for data-driven decisions while applying EI to interpret and act with empathy (4)
Points of Concern
  • Bias: Emotional norms are not consistent among cultures, misinterpretation and incorrect responses could be harmful
  • Manipulation: Emotionally Intelligent AI could be used to sway public opinion impacting marketing, politics and beyond
  • Privacy Concerns: does responding to AI become consent for data collection
  • Human emotions are nuanced and often culturally or contextually dependent
  • What are the costs to jobs in all fields as AI is able to take on more nuanced roles

We know that AI and EI are both here to stay, so finding, developing and utilizing the best strategies to unite the two is the way forward.

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References:

  • (1) Encyclopedia Britannica, wwwlbritannica.com Artificial intelligence (AI) | Definition, Examples, Types, Applications, Companies, & Facts | Britannica

  • (2) The Turing Test, www.wikipedia.com Turing test - Wikipedia; Medium: Read and write stories., Can a Machine Think?. In this article, Alan Turing, who first… | by Emine Bozkus, PhD | Medium

    a. The Imitation Game IMDB The Imitation Game (2014) - IMDb

  • (3) Emotional intelligence (EQ) Components & Examples www.simplypsychology.com

  • (4) Preserving Emotional Intelligence In The Age Of AI, www.forbes.com

APPENDIX

1950s-1970s: The Birth and Early Exploration
  • 1950: Alan Turing proposes the Turing Test to evaluate machine intelligence.
  • 1956: The term Artificial Intelligence is coined at the Dartmouth Conference.
  • 1966: ELIZA, the first chatbot, is developed to simulate human conversation.
  • 1969: Shakey the Robot becomes the first mobile robot to reason about its actions.
1970s-1980s: Al Winter and Modest Progress
  • 1974-1990: Funding and interest decline due to unmet expectations-known as the Al Winter.
  • 1980s: Expert systems gain traction in business, but limitations persist.
  • 1987: Backpropagation revives in interest in neural networks.
1990s-2000s: Foundations for Modern Al
  • 1997: IBM's Deep Blue defeats chess champion Garry Kasparov.
  • 2005: Stanford's autonomous vehicle Stanley wins the DARPA Grand Challenge.
  • 2006: Geoffrey Hinton popularizes deep learning, revolutionizing Al research.
2010s: The Deep Learning Revolution
  • 2012: AlexNet wins the ImageNet competition, showcasing the power of deep neural networks.
  • 2014: Google's DeepMind develops AlphaGo, which later defeats top Go players.
  • 2016: OpenAI is founded, accelerating Al development.
  • 2018: OpenAI releases GPT-2, a breakthrough in nature language generation.
2020s: Generative Al and Human-Level Tasks
  • 2020: GPT-3 revolutionizes text generation and conversational AI.
  • 2021: OpenAI’s Codex powers GitHub Copilot, automating code writing.
  • 2022: ChatGPT and DALL.E2  bring Al into mainstream creativity.
  • 2023-2024: Multimodal models like GPT-4 and Google Bard integrate text,image, and reasoning.
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Dawn Reider - Controller

Dawn Reider oversees all financial operations with a focus on accuracy, compliance, and strategic resource management. Her disciplined approach ensures clients benefit from transparent reporting, efficient budgeting, and fiscal accountability—delivering confidence that every project is managed with precision and integrity.

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