Social-emotional learning (SEL) is vital for university students’ development, yet access to scalable support remains limited. While conversational AI presents a promising avenue, existing research often treats “AI support” as a monolithic intervention, overlooking critical distinctions between designs aimed at providing emotional comfort and those fostering competency growth. This study directly investigates how distinct AI companionship strategies-Affirmation, Empathic-Inquisitive, and General Support—differentially impact SEL outcomes. Through a 14-day longitudinal experiment with 134 university students, using systematically prompted Large Language Models (LLMs), we demonstrate a clear functional dissociation: the Affirmative strategy most effectively enhanced positive affect; the Empathic-Inquisitive strategy produced the greatest reduction in negative affect and the most substantial gains in social-emotional competencies; while the General Support strategy offered balanced, moderate benefits. These findings underscore that AI companionship is not pedagogically neutral—its effects are specialized and contingent on design. Our work shifts the focus from whether AI can support SEL to how its design directs learning toward specific affective or competency-based outcomes. This provides a principled framework for developing goal-aligned AI systems in higher education and calls for future research into adaptive, context-sensitive companions that can dynamically respond to learners’ evolving needs.
- Question: How do distinct AI companionship strategies differentially impact students' social-emotional learning outcomes?
- Setting and data: 134 university students over a 14-day longitudinal experiment.
- Method: Longitudinal experiment
Social-emotional learning is vital for university students, but scalable support is limited. Research often treats conversational AI as a monolithic intervention, overlooking critical differences between designs offering emotional comfort versus those fostering competency growth.
- An Empathic-Inquisitive AI strategy led to the greatest reduction in negative affect and the largest gains in social-emotional competencies.
- An Affirmative AI strategy was the most effective approach for enhancing students' positive affect.
- A General Support AI strategy provided balanced, moderate benefits across outcomes.
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