Since the inception of human research studies, researchers often need to interact with participants on a set schedule to collect data. While some human research is automated, most is not; which costs researchers both time and money. Usually, user-provided data collection consists of surveys administered via telephone or email. While these methods are simplest, they are tedious for the survey administrators, which could incur fatigue and potentially lead to collection mistakes. A solution to this was the creation of "chatbots". Early developments relied on mostly rule-based tactics (e.g. ELIZA), which were suitable for uniform input. However, as the complexity of interactions increases, rule-based systems begin breaking down since there exist a variety of ways for a user to express the same intention. This is especially true when tracking states within a research study (or protocol). Recently, natural language processing (NLP) models and, subsequently, virtual assistants have become increasingly more sophisticated when communicating with users. Examples of these efforts range from research studies to commercial health products. This project leverages recent advancements in conversational artificial intelligence (AI), speech-to-text, natural language understanding (NLU), and finite-state machines to automate protocols, specifically in research settings. This application must be generalized, fully customizable, and irrespective of any research study. These parameters allow new research protocols to be created quickly once envisioned. With this in mind, I present SmartState, a fully-customizable, state-driven protocol manager combined with supporting AI components to autonomously manage user data and intelligently determine the intention of users through chat and end device interactions to drive protocols.

翻译：自人类研究兴起以来，研究人员通常需要按预定时间表与参与者互动以收集数据。尽管部分人类研究已实现自动化，但大部分仍依赖人工操作，这既耗费研究者的时间也消耗资金。通常，用户提供的数据收集通过电话或电子邮件管理的问卷调查进行。虽然这些方法最为简便，但对调查管理者而言却相当繁琐，容易导致疲劳并可能引发收集错误。为此，"聊天机器人"应运而生。早期开发主要依赖基于规则的策略（例如ELIZA），这类方法适用于统一化输入。然而，随着交互复杂度的提升，由于用户表达同一意图的方式多种多样，基于规则的系统开始失效。这在追踪研究（或协议）中的状态时尤为明显。近年来，自然语言处理模型及随后的虚拟助手在与用户沟通方面日益精进。从学术研究到商业健康产品，相关实践案例不胜枚举。本研究项目利用对话式人工智能、语音转文字、自然语言理解及有限状态机的最新进展，实现研究场景中协议的自动化。该应用需具备通用性、完全可定制性且不依赖具体研究内容。这些参数使得新研究协议一经构思即可快速创建。基于此，本文提出了SmartState——一种完全可定制的状态驱动协议管理器，其结合支持性人工智能组件，通过聊天和终端设备交互自主管理用户数据并智能识别用户意图，从而实现协议驱动。