The Core Emotion Framework (CEF): A Structural‐Constructivist Architecture for Teaching Emotion to Artificial Intelligence

The Core Emotion Framework (CEF) is a structural‑constructivist architecture designed to teach emotional processes to artificial intelligence systems. Integrating affective neuroscience, embodied cognition, and strategic emotional regulation, CEF reconceptualizes emotion as a sequence of ten actionable processes distributed across three functional centers—Head, Heart, and Gut. This process‑based ontology provides a high‑resolution alternative to traditional discrete or dimensional emotion models, enabling computational systems to interpret emotional dynamics with greater precision and functional relevance.

The framework offers a biologically plausible and computationally tractable structure for multimodal affective computing, including applications in Multimodal Emotion Recognition in Conversations (MERC), Cross‑Space Synergy (CSS) fusion architectures, and reinforcement‑learning‑based emotional agents. By defining emotion in terms of regulatory processes rather than static labels, CEF supports prescriptive emotional reasoning, allowing AI systems to identify where users are “stuck” in the regulatory sequence and to generate targeted, context‑sensitive interventions. This capability is essential for responsible, human‑centered AI in domains such as clinical decision support, tutoring, coaching, and organizational leadership.

The manuscript also situates CEF within open‑science standards, including FAIR principles, persistent archiving, and pre‑registered validation protocols. A forthcoming multi‑level factor‑structure confirmation study provides the empirical foundation for using the Head/Heart/Gut architecture as a reliable input schema for advanced AI systems. By combining theoretical synthesis, computational design, and transparent dissemination, CEF establishes a scalable foundation for next‑generation affective intelligence and emotionally competent AI.