The objective of this study was to understand the attitudes of professionals who work in mental health regarding the use of conversational user interfaces, or chatbots, to support people’s mental health and wellbeing. This study involves an online survey to measure the awareness and attitudes of mental healthcare professionals and experts. The findings from this survey show that more than half of the participants in the survey agreed that there are benefits associated with mental healthcare chatbots (65%, p < 0.01). The perceived importance of chatbots was also relatively high (74%, p < 0.01), with more than three-quarters (79%, p < 0.01) of respondents agreeing that mental healthcare chatbots could help their clients better manage their own health, yet chatbots are overwhelmingly perceived as not adequately understanding or displaying human emotion (86%, p < 0.01). Even though the level of personal experience with chatbots among professionals and experts in mental health has been quite low, this study shows that where they have been used, the experience has been mostly satisfactory. This study has found that as years of experience increased, there was a corresponding increase in the belief that healthcare chatbots could help clients better manage their own mental health.

This paper outlines the design and development of a chatbot called iHelpr for mental healthcare that 1) administers self-assessment instruments/scales, 2) provides wellbeing and self-help guidance and information, all within a conversational interface. Chatbots are becoming more prevalent in our daily lives, with bots available to provide the user with daily weather forecasts, book holidays, and even converse with a virtual therapist. It is predicted that users may soon prefer to complete tasks using a conversational interface that are traditionally done through a webpage or mobile application. In the context of mental healthcare, demand exceeds supply, waiting lists are ever growing, and populations in rural communities still struggle to access mental healthcare. Chatbots can be utilised to improve and broaden access to mental healthcare. When designing chatbots for mental healthcare, there are further considerations, such as managing risk and ethical considerations. Furthermore, usability and the design of conversational flow are important factors to consider when developing chatbots for any domain. This paper outlines best practices and experiences extrapolated from developing the iHelpr chatbot.

Sharing data is often a risk in terms of security and privacy especially if the data is sensitive. Algorithms can be used to generate synthetic data from an original raw dataset in order to share data that are considered more 'privacy preserving', and that increase the level of anonymity. In this paper, we carry out an experiment to study the validity of conducting machine learning on synthetic data. We compare the evaluation metrics produced from machine learning models that were trained using synthetic data with metrics yielded from machine learning models that were trained using the corresponding real data.

This paper explores the area of conversational user interfaces and chatbot development, focusing on the methodological aspects of development. The domain in this paper for chatbot development is healthcare. An increasing issue in chatbot development relates to the difficulty in eliciting specific domain knowledge. As chatbots become more ubiquitous in our daily lives with more complex use cases, the process of eliciting and codifying the domain knowledge has become more complex. This is a problem revisited; in the 1980's, 'expert systems' grew rapidly in popularity and such systems required the same processes of elicitation and codification of human know-how or expertise as we now re-witness in modern chatbot development. A new area of 'knowledge engineering' developed from the expert systems or 'knowledge-based systems' field and from this several knowledge engineering methodologies emerged. The present paper revisits these methodologies and asks if there are lessons to be learned for chatbot design and development from such decades old knowledge engineering methods. The paper presents an amendment to a chatbot methodology, incorporating new stages of 'knowledge gathering' and 'usability testing' into the process.

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