Published conference paper on persuasive AI.

Designing Persuasive Artificial Intelligence for Mental Health: A Prioritization Framework to Enhance Trust and Engagement

• ZORA
• Springer
• Zotero
• PERSUASIVE

Abstract

In digital mental health, various persuasive applications have employed artificial intelligence (AI) to offer scalable support for vulnerable users. However, sustaining user trust and engagement remains a challenge, as existing persuasive design frameworks, such as the Persuasive Systems Design (PSD) framework, lack context sensitivity. This study addresses this gap by developing a novel framework for prioritizing persuasive design principles in AI-driven mental health interventions. Using a design science research (DSR) approach, we synthesized findings from a systematic literature review and a mixed-methods study (surveys and interviews) to identify user- and expert-driven design priorities. The primary result is a two-tiered prioritization framework that distinguishes between foundational “core principles” (e.g., Trustworthiness) and context-dependent “strategic enhancers” (e.g., Praise) within the PSD framework. We demonstrated its applicability in a proof-of-concept prototype. This framework provides researchers and practitioners with actionable, user-centered recommendations, mapping specific principles to a six-stage user journey to enhance trust and engagement.