Creating intervention messages for smoking cessation is a labor-intensive process. Advances in Large Language Models (LLMs) offer a promising alternative for automated message generation. Two critical questions remain: 1) How to optimize LLMs to mimic human expert writing, and 2) Do LLM-generated messages meet clinical standards? We systematically examined the message generation and evaluation processes through three studies investigating prompt engineering (Study 1), decoding optimization (Study 2), and expert review (Study 3). We employed computational linguistic analysis in LLM assessment and established a comprehensive evaluation framework, incorporating automated metrics, linguistic attributes, and expert evaluations. Certified tobacco treatment specialists assessed the quality, accuracy, credibility, and persuasiveness of LLM-generated messages, using expert-written messages as the benchmark. Results indicate that larger LLMs, including ChatGPT, OPT-13B, and OPT-30B, can effectively emulate expert writing to generate well-written, accurate, and persuasive messages, thereby demonstrating the capability of LLMs in augmenting clinical practices of smoking cessation interventions.

Public speaking anxiety is one of the most common social phobias. We explore the feasibility of using a conversational agent to reduce this anxiety. We developed a public-speaking tutor on the Amazon Alexa platform that enables users to engage in cognitive reconstruction exercises. We also investigated how the sociability of the agent might affect its performance as a tutor. A user study of 53 college students with fear of public speaking showed that the interaction with the agent served to assuage pre-speech state anxiety. Agent sociability improved the sense of interpersonal closeness, which was associated with lower pre-speech anxiety. Moreover, sociability of the agent increased participants' satisfaction and their willingness to continue engagement. Our findings, thus, have implications not only for addressing public speaking anxiety in a scalable way but also for the design of future conversational agents using smart speaker platforms.