The aim of the study reported here was to reflect and report on lessons learned from adapting HCI methods to fit into an agile development process delivering a digital intervention for people managing Long Covid. During the project, we maintained a record of all activities and interim design products. Subsequently, selected records were analysed qualitatively. Challenges included engaging patients managing this complex, debilitating health condition and fitting the digital intervention into different and evolving patient pathways provided by clinics. The key contributions of this paper are a description of the clinically supported self-management app, Living With Covid Recovery, and of adapted methods for user-centred design and testing; narratives on engaging diverse clinics integrating a digital health intervention in their care pathways; reflections on designing for diverse users; implications for design of future technologies for supported self-management; and insights into multidisciplinary working that are rarely discussed within HCI.

Conversational agents, also known as chatbots, are versatile tools that have the potential of being used in dialogical argumentation. They could possibly be deployed in tasks such as persuasion for behaviour change (e.g. persuading people to eat more fruit, to take regular exercise, etc.). However, to achieve this, there is a need to develop methods for acquiring appropriate arguments and counterargument that reflect both sides of the discussion. For instance, to persuade someone to do regular exercise, the chatbot needs to know counterarguments that the user might have for not doing exercise. To address this need, we present methods for acquiring arguments and counterarguments, and importantly, meta-level information that can be useful for deciding when arguments can be used during an argumentation dialogue. We evaluate these methods in studies with participants and show how harnessing these methods in a chatbot can make it more persuasive.

Much research in computational argumentation assumes that arguments can be obtained in some way. Yet, to improve and apply models of argument, we need methods for acquiring them. Current approaches include argument mining from text, hand coding of arguments by researchers, or generating arguments from knowledge bases. In this paper, we propose a new approach, which we call argument harvesting, that uses a chatbot to enter into a dialogue with a participant to get arguments and counterarguments from him or her. Because it is automated, the chatbot can be used repeatedly in many dialogues, and thereby it can generate a large corpus. We describe the architecture of the chatbot, provide methods for clustering arguments by their similarity and value, and an evaluation of our approach in a case study concerning attitudes of women to participation in sport.