This paper reviews different types of conversational agents used in health care for chronic conditions, examining their underlying communication technology, evaluation measures, and AI methods. A systematic search was performed in February 2021 on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library. Studies were included if they focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases, involved conversational agents, and tested the system with human users. The search retrieved 1087 articles. Twenty-six studies met the inclusion criteria. Out of 26 conversational agents (CAs), 16 were chatbots, seven were embodied conversational agents (ECA), one was a conversational agent in a robot, and another was a relational agent. One agent was not specified. Based on this review, the overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users’ feedback shows helpfulness, satisfaction, and ease of use in more than half of included studies. Although many users in the studies appear to feel more comfortable with CAs, there is still a lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions due to the insufficient reporting of technical implementation details.

Type 2 diabetes (T2D) has emerged as a significant catalyst for various health conditions. The availability and cost of health professionals pose challenges, limiting access to personalised lifestyle support. To address this issue, utilising Conversational Agents (CAs) presents an opportunity to improve scalability and adoption by improving efficiency and engagement. Thus, this study focuses on the potential impact and response to T2D. This paper aims to develop a framework for designing CA functions that support individuals at risk of T2D to be more active and raise their awareness. The study involves a mixed methods approach, including a survey conducted among 30 participants in Sydney and Jeddah, followed by semi-structured interviews conducted with 10 participants. While descriptive statistics were used to analyse the survey, the interviews were analysed using thematic analysis. Drawing upon the interviews and relevant literature, this study proposes developing a preliminary framework to help design CAs that support individuals with prediabetes to adopt a more active lifestyle.

Conversational agents (CAs) are increasingly integrated into augmented reality (AR), yet evidence on how conversational capabilities are combined with AR contexts remains dispersed across application domains and device types. We conducted a systematic review of 44 peer-reviewed studies (2007–2025) using predefined search and screening procedures. Across the included systems, speech is the predominant input modality (95%), and head-mounted displays (HMDs) are most frequently used (66%). Many prototypes couple speech with contextual information such as location or object recognition (41%) to adapt conversational behaviour. We also observed a strong trend in dialogue management toward agent-based architectures powered by Large Language Models, with 12 of the 14 agent-based systems being published after 2023. Our analysis resulted in the development of a framework for AR-CA interaction, which positions systems along two dimensions: Presence Form and Context Awareness. The framework reveals an emerging trend toward dynamic, context-aware interaction. User experience reports are broadly positive, but limitations include challenges with long-term engagement, privacy risks, and hardware constraints. Our review yields four key design considerations: match an avatar’s embodiment with equally capable LLM dialogue; enrich interactions through haptics and spatial audio; preserve physical and social plausibility; and adopt user-centred, privacy-aware hardware choices. Together, these principles chart a path toward robust, contextually intelligent, and ethically responsible AR conversational agents.