Persuasive AI for adherence.

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.

Chatbots are being introduced to fill the gap between consultations, with the motivation of supporting adherence to treatment, especially in the case of chronic diseases. Such a task requires chatbots to have a persuasive ability to influence patient attitudes or behaviour, which is achievable through the advancement of Large Language Models (LLMs). By means of prompting engineering, we aim to accomplish the design goal of persuasive chatbots powered by LLMs that can systematically apply persuasion strategies and dynamically adapt such strategies to the user. We have converted well-founded persuasion strategies into guidelines for prompt crafting to enhance the persuasiveness of LLM-based chatbots. This paper contributes to the design of persuasive chatbots by providing a list of guidance on prompt crafting, meta-prompts, and a proof-of-concept of the developed guidance from a digital health application. This paper also initiates the discourse on prompt guidance as a new type of design artefact within Design Science Research (DSR).

The advent of increasingly powerful language models has raised expectations for conversational interactions. However, controlling these models is a challenge, emphasizing the need to be able to investigate the feasibility and value of their application. We present PROMISE (Available at: https://github.com/zhaw-iwi/promise), a framework that facilitates the development of complex conversational interactions with information systems. Its use of state machine modeling concepts enables model-driven, dynamic prompt orchestration across hierarchically nested states and transitions. This improves the control of language models’ behavior and thus enables their effective and efficient use. We show the applications of PROMISE in health information systems and demonstrate its ability to handle complex interactions.