Assessing AI receptivity through a persuasion knowledge lens

Area of Science:

• Human-Computer Interaction
• Artificial Intelligence Studies
• Persuasion Science

Background:

• Human-AI interaction is a rapidly expanding research area.
• Existing literature lacks a unified framework for understanding AI acceptance and aversion.
• Conceptualizing AI as a persuasion agent offers a novel perspective.

Purpose of the Study:

• To review and synthesize current research on human-AI interactions.
• To analyze AI acceptance and aversion through the lens of persuasion knowledge.
• To identify key factors influencing user interaction with AI systems.

Main Methods:

• Systematic literature review of human-AI interaction studies.
• Conceptualization of AI as a persuasion agent.
• Categorization of research findings based on AI properties, user characteristics, and interaction context.

Main Results:

• AI acceptance and aversion are influenced by AI attributes (e.g., anthropomorphism, usability).
• Individual differences in user judgments and task performance impact AI interaction.
• Contextual factors, including decision type and domain, significantly shape AI engagement.

Conclusions:

• A persuasion knowledge framework provides valuable insights into human-AI dynamics.
• Understanding AI as a persuasion agent can explain user acceptance and rejection.
• Future research should further explore these factors to optimize human-AI collaboration.