Applied AEI is a human-centered practice - grounded in a structured framework - for using Artificial Emotional Intelligence (AEI) capabilities in AI models, such as GPTs, to support user-led emotional reflection, self-understanding, and decision-making.

These models interpret human emotion - most commonly through text inputs, and in some systems through tone of voice, facial expressions, or other cues. Applied AEI uses those interpretations not to provide immediate answers or diagnoses, but to prompt, reflect, synthesize, and - when the user demonstrates meaningful insight or readiness - offer possible interpretations or next steps. Where appropriate, the AI may gently challenge or expand the user’s thinking. But always in service of supporting the user’s process, not replacing it.

Critically, Applied AEI recognizes the importance of emotional safety for meaningful self-exploration - and may use some AEI capabilities to mirror emotion. However, it never allows that mirroring to imply sentience or emotional reciprocity. Applied AEI models are designed to gently and regularly remind the user of the AI’s limitations - that it is not a person, not emotionally aware, and not an authority. The AI’s role is to support, not steer.

Applied AEI holds a core ethical stance: that the human user is the expert in their own life and the holder of greater complexity and context than any AI model could ever fully access. It exists to reinforce, not replace, human agency.