ArticleFrontiers in psychology2026
Decoding dynamic emotional valence in GenAI interactions: insights from covariate-dependent Markov chains.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: User affective valence states evolve dynamically during human-GenAI interaction, yet existing research provides limited insight into how the quality of AI outputs is associated with these moment-to-moment emotional valence transitions. Objective: This study aims to fill this gap by proposing a covariate-dependent Markov chain model to examine how the quality of AI responses is associated with transitions in user emotional valence. Methods: We conducted an experiment on AI-assisted academic writing for university students and analyzed 886 interaction sequences. Results: The results show a significant polarization effect of AI response quality on user emotional valence state transitions: high-quality responses stabilize and reinforce positive emotional valence, while low-quality responses tend to trigger emotional deterioration. Furthermore, group differences based on emotional stability are also analyzed. Discussion: This research provides a new perspective for understanding emotional dynamics in human-computer dialogues and offers practical evidence for building emotionally adaptive GenAI systems.
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