Heuristic Evaluation of COVID-19 Chatbots

Höhn, Sviatlana; Bongard-Blanchy, Kerstin

doi:10.1007/978-3-030-68288-0_9

Cited by 18 publications

(11 citation statements)

References 21 publications

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“…This affected the user's control and freedom. This finding is similar to the studies by (Höhn and Bongard-Blanchy, 2020;Jeddi et al, 2020), where it was found that applications without backward and forward navigation buttons make it difficult for users to navigate between the pages. Most of the menu titles of the G-MoMo applications are not uniformly aligned.…”

Section: Discussionsupporting

confidence: 91%

“…This affected the applications' error diagnosis and recovery. It was well confirmed by other studies (Caro-Alvaro et al, 2018;Höhn and Bongard-Blanchy, 2020), who observed that the applications do not indicate the error occurrence and the error was not explained in plain language. (Eliseo et al, 2017) recommended that error messages be displayed clearly.…”

Section: Discussionsupporting

confidence: 83%

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Heuristic Evaluation and Usability Testing of G-MoMo Applications

Ali

2022

J INFORM SYSTEMS ENG

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Financial technology (FinTech) has swiftly revolutionized mobile money as one of the ways of accessing financial services in developing countries. Numerous mobile money applications were developed to access mobile money services but are hindered by severe authentication security challenges, thus, forcing the researchers to design a secure multi-factor authentication (MFA) algorithm for mobile money applications. Three prototypes of native mobile money applications (G-MoMo applications) were developed to confirm that the algorithm provides high security and is feasible. This study, therefore, aimed to evaluate the usability of the G-MoMo applications using heuristic evaluation and usability testing to identify potential usability issues and provide recommendations for improvement. Heuristic evaluation and usability testing methods were used to evaluate the G-MoMo applications. The heuristic evaluation was carried out by five experts that used the 10 principles proposed by Jakob Nielsen with a five-point severity rating scale to identify the usability problems. While the usability testing was conducted with forty participants selected using a purposive sampling method to validate the usability of the G-MoMo applications by performing tasks and filling out the post-test questionnaire. Data collected were analyzed in RStudio software. Sixty-three usability issues were identified during heuristic evaluation, where 33 were minor and 30 were major. The most violated heuristic items were "help and documentation", and "user control and freedom", while the least violated heuristic items were "aesthetic and minimalist design" and "visibility of system status". The usability testing findings revealed that the G-MoMo applications' performance proved good in learnability, effectiveness, efficiency, memorability, and errors. It also provided user satisfaction, ease of use, aesthetics, usefulness, integration, and understandability. Therefore, it was highly recommended that the developers of G-MoMo applications fix the identified usability problems to make the applications more reliable and increase overall user satisfaction.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 83%

Heuristic Evaluation and Usability Testing of G-MoMo Applications

Ali

2022

J INFORM SYSTEMS ENG

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“…The ongoing COVID-19 pandemic has further driven the rapid uptake and deployment of chatbots [ 6 ], many making use of commercial chatbot development platforms such as IBM’s Watson Assistant, Google Dialogflow, Yellow Messenger, and Turn.io to develop custom chatbots to help combat the disease. In the face of the burden placed upon health care systems by the pandemic, chatbots have enabled the automation of services toward addressing the need for physical distancing and helped disseminate information and relieve the pressure on medical services by public health systems around the globe [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

The Development and Use of Chatbots in Public Health: Scoping Review

Wilson¹,

Marasoiu²

2022

JMIR Hum Factors

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Background Chatbots are computer programs that present a conversation-like interface through which people can access information and services. The COVID-19 pandemic has driven a substantial increase in the use of chatbots to support and complement traditional health care systems. However, despite the uptake in their use, evidence to support the development and deployment of chatbots in public health remains limited. Recent reviews have focused on the use of chatbots during the COVID-19 pandemic and the use of conversational agents in health care more generally. This paper complements this research and addresses a gap in the literature by assessing the breadth and scope of research evidence for the use of chatbots across the domain of public health. Objective This scoping review had 3 main objectives: (1) to identify the application domains in public health in which there is the most evidence for the development and use of chatbots; (2) to identify the types of chatbots that are being deployed in these domains; and (3) to ascertain the methods and methodologies by which chatbots are being evaluated in public health applications. This paper explored the implications for future research on the development and deployment of chatbots in public health in light of the analysis of the evidence for their use. Methods Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines for scoping reviews, relevant studies were identified through searches conducted in the MEDLINE, PubMed, Scopus, Cochrane Central Register of Controlled Trials, IEEE Xplore, ACM Digital Library, and Open Grey databases from mid-June to August 2021. Studies were included if they used or evaluated chatbots for the purpose of prevention or intervention and for which the evidence showed a demonstrable health impact. Results Of the 1506 studies identified, 32 were included in the review. The results show a substantial increase in the interest of chatbots in the past few years, shortly before the pandemic. Half (16/32, 50%) of the research evaluated chatbots applied to mental health or COVID-19. The studies suggest promise in the application of chatbots, especially to easily automated and repetitive tasks, but overall, the evidence for the efficacy of chatbots for prevention and intervention across all domains is limited at present. Conclusions More research is needed to fully understand the effectiveness of using chatbots in public health. Concerns with the clinical, legal, and ethical aspects of the use of chatbots for health care are well founded given the speed with which they have been adopted in practice. Future research on their use should address these concerns through the development of expertise and best practices specific to public health, including a greater focus on user experience.

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“…Backpropagation [15] is used in this approach to learn Gradient decent, a is the learning rate which a = 0.01, where ∆w i refers to weights (y i -y i ) is the evaluation of error as specified in (4).…”

Section: Figure 2 Simulation Of Ann Of Our Approachmentioning

confidence: 99%

“…A chatbot is an automated computer program that fundamentally simulates human conversations such as the works of [1,2,3]. The evaluation of the chatbot's User Interface (UI) that was done for COVID-19 in [4] shows that the best approach is the interactive chatbot that can answer the user in conversational way and accept the free hand input, which depends on AI as this paper introduces. There is different architecture to classify text, and the word2vec embedding model [5] and GloVe [6] are embedding dictionaries, while a bag of the word (BoW) [7] and BoW TF-IDF [8] are vectorization methods that convert the textual data to numeric data in vectors shape.…”

Section: Introductionmentioning

confidence: 99%

Covıd-19 Faqs Chatbot Using Artificial Neural Network with Bag of Words

ALMASSRI¹,

AMMAR²,

KÜÇÜKALI³

2021

EURAS

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COVID-19 is a contemporary virus with a fatal syndrome that had not been seen in the last century. The virus evolves in people of all ages in all areas around the world. As the increment of cases is growing up rapidly the worry of people is increasing, which makes it very hard for healthcare departments and governments to solve people's queries. AI solution is suggested, where a simulation of frontdesk assistance. Chatbots are easy to use and simulate a human conversation through text via smartphones or personal computers. Chatbot applications can improve patient information, monitoring, or treatment adherence. The architecture is a simple neural network consisting of a single hidden layer and the sigmoid function is trained by textual data organized by multiple data organization methods. A simple GUI is provided to the classifier to be tested practically. Data used is a collection of questions and their answers about COVID-19. The approach has achieved acceptable results considering speed, and accuracy. Practical predictions were true with acceptable accuracy.

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Heuristic Evaluation of COVID-19 Chatbots

Cited by 18 publications

References 21 publications

Heuristic Evaluation and Usability Testing of G-MoMo Applications

Heuristic Evaluation and Usability Testing of G-MoMo Applications

The Development and Use of Chatbots in Public Health: Scoping Review

Covıd-19 Faqs Chatbot Using Artificial Neural Network with Bag of Words

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