Each day, individuals set behavioral goals such as eating healthier, exercising regularly, or increasing productivity. While psychological frameworks (i.e., goal setting and implementation intentions) can be helpful, they often need structured external support, which interactive technologies can provide. We thus explored how large language model (LLM)-based chatbots can apply these frameworks to guide users in setting more effective goals. We conducted a preregistered randomized controlled experiment ($N = 543$) comparing chatbots with different combinations of three design features: guidance, suggestions, and feedback. We evaluated goal quality using subjective and objective measures. We found that, while guidance is already helpful, it is the addition of feedback that makes LLM-based chatbots effective in supporting participants' goal setting. In contrast, adaptive suggestions were less effective. Altogether, our study shows how to design chatbots by operationalizing psychological frameworks to provide effective support for reaching behavioral goals.

翻译：个体每日都会设定行为目标，如健康饮食、规律锻炼或提升工作效率。尽管心理学框架（即目标设定与执行意图）能够提供帮助，但它们通常需要结构化的外部支持，而交互技术正可满足这一需求。因此，我们探究了基于大型语言模型（LLM）的聊天机器人如何运用这些框架来引导用户设定更有效的目标。我们开展了一项预注册的随机对照实验（$N = 543$），比较了具备三种设计特征（引导、建议与反馈）不同组合的聊天机器人。我们采用主客观相结合的方法评估了目标质量。研究发现，虽然引导本身已具助益，但反馈的加入才使得基于LLM的聊天机器人能够有效支持参与者的目标设定。相比之下，自适应建议的效果较弱。总体而言，本研究通过将心理学框架操作化，展示了如何设计聊天机器人为实现行为目标提供有效支持。