Leveraging Low Code Development of Smart Personal Assistants: An Integrated Design Approach with the SPADE Method
针对低代码平台开发智能个人助手时设计选择不直观的问题，提出了SPADE方法，帮助无编程背景的领域专家设计出更有效的对话式助手。
Smart personal assistants (SPAs), such as Alexa for example, promise individualized user interactions owing to their varying interaction possibilities, knowledgeability, and human-like behaviors. To support the widespread adoption and use of SPAs, organizations such as Google or Amazon provide low code environments that support the development of SPAs (e.g., for Google Home or Amazon’s Alexa). These so-called low code platforms enable domain experts (e.g., business users without programming skills or experience) to develop SPAs for their purposes. However, using these platforms alone does not guarantee a useful and good conversation with novel SPAs due to non-intuitive design choices. Following a design science research approach, we propose the Smart Personal Assistant for Domain Experts (SPADE) method to address the missing link. This method supports domain experts in the development and contextualization of sophisticated SPAs for various application scenarios and focuses especially on conversational and anthropomorphic design steps. Our proof of concept and proof of value results show that SPADE is useful for supporting domain experts to create effective SPAs in different domains beyond private set-ups.