The aim of this paper is to assess the usability of a chatbot for mental health care within a social enterprise. Chatbots are becoming more prevalent in our daily lives, as we can now use them to book flights, manage savings, and check the weather. Chatbots are increasingly being used in mental health care, with the emergence of "virtual therapists". In this study, the usability of a chatbot named iHelpr has been assessed. iHelpr has been developed to provide guided self-assessment, and tips for the following areas: stress, anxiety, depression, sleep, and self esteem. This study used a questionnaire developed by Chatbottest, and the System Usability Scale to assess the usability of iHelpr. The participants in this study enjoyed interacting with the chatbot, and found it easy to use. However, the study highlighted areas that need major improvements, such as Error Management and Intelligence. A list of recommendations has been developed to improve the usability of the iHelpr chatbot.

The aim of this paper is to outline the design of a chatbot to be used within mental health counselling. One of the main causes of the burden of disease worldwide is mental health problems. Mental health contributes to 28% of the total burden of disease, compared to 16% each for cancer and heart disease in the UK. Stress, anxiety or depression accounted for 15.8 million days of sickness absence across the UK in 2016. By 2020, the gap between the demand for mental health care and the resources the National Health Service (NHS) can provide is likely to widen, therefore providers are increasingly needing to find more cost-effective ways to deliver mental health care. Digital Interventions have been created to help with these issues, for example anxiety, stress and depression. Chatbots can be incorporated into digital interventions, or used as standalone interventions. Chatbots can be a more interactive experience for the user to receive information, or complete diagnostic tools, or to even be used for counselling. A demo chatbot was created using interactive emoji's and GIFs to improve the user experience when searching for online self-help tips. This chatbot will be further developed and incorporated into a full web based programme for mental health in the workplace. It is envisaged that the chatbot will be able to provide initial counselling, and lead users into the correct services or self-help information.

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.

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.