# Toward personalized persuasive social robots for behavior change in healthcare: a conceptual framework

## TL;DR

This paper proposes a framework for designing personalized social robots to help people change behaviors in healthcare settings.

## Contribution

A novel conceptual framework integrating behavioral models, personalization, and persuasive language for healthcare behavior change.

## Key findings

- Personalization of conversational agents is crucial for adapting to individual user characteristics and contexts.
- Persuasive language generation can enhance the effectiveness of behavior change interventions.
- A layered framework is proposed to connect behavioral theories with practical design elements in social robots.

## Abstract

This paper presents a conceptual framework for the design of personalized persuasive conversational agents to support positive behavior change. This paper leverages key theoretical models to understand the determinants of behavior change and explores how these models can inform the design of personalized conversational agents to enhance their effectiveness in healthcare interventions. The role of personalization in dialogue-based intervention is discussed, emphasizing the importance of adaptation to individual characteristics, preferences, and contexts. The potential of persuasive language generation is also examined, highlighting its ability to create more engaging and impactful behavior change strategies. Finally, the paper proposes a layered framework that explicitly links behavioral models, user personalization, and persuasive language generation, and discusses future research directions for integrating this framework in social robots’ interventions for behavior change in healthcare.