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Abstract

The increasing integration of artificial intelligence (AI) into daily life calls for new theoretical frameworks that capture human-AI interaction’s dynamic, feedback-driven nature. Traditional models treat AI as a passive medium, overlooking its adaptive capabilities. This paper proposes the Human-AI Interaction Outcomes (HAI-IO) model, an interdisciplinary framework synthesizing human-machine communication, social exchange theory, dialogue systems, and computational feedback models like cybernetics and reinforcement learning. The HAI-IO model frames interaction as iterative and bidirectional—AI adapts through predictive processing while users adjust based on AI feedback. This mutual adaptation shapes trust, engagement, and system optimization. The model informs AI system design, user education, and policy, advocating for adaptive interfaces and ethical oversight. It advances theory and practice in building responsible, responsive AI communication systems.

DOI

10.30658/hmc.10.9

Author ORCID Identifier

Rae Francis Quilantang: 0009-0001-2008-178XORCID iD icon

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