Artificial intelligence (AI) has transformed the world and the relationships among humans as the learning capabilities of machines have allowed for a new means of communication between humans and machines. In the field of health, there is much interest in new technologies that help to improve and automate services in hospitals. This article aims to explore the literature related to conversational agents applied to health care, searching for definitions, patterns, methods, architectures, and data types. Furthermore, this work identifies an agent application taxonomy, current challenges, and research gaps. In this work, we use a systematic literature review approach. We guide and refine this study and the research questions by applying Population, Intervention, Comparison, Outcome, and Context (PICOC) criteria. The present study investigated approximately 4145 articles involving conversational agents in health published over the last ten years. In this context, we finally selected 40 articles based on their approaches and objectives as related to our main subject. As a result, we developed a taxonomy, identified the main challenges in the field, and defined the main types of dialog and contexts related to conversational agents in health. These results contributed to discussions regarding conversational health agents, and highlighted some research gaps for future study.

Survey of conversational agents in health

We are delighted to have the opportunity to talk with Tony about how his work touches on issues of imitation and contagion—a loaded term unpacked within his 2013 book.

An Interview with Tony David Sampson: Author of Virality: Contagion Theory in the Age of Networks

Looking for Information -- A Survey of Research on Information Seeking, Needs, and Behavior by Donald Case

Mobile internet, cloud computing, big data technologies, and significant breakthroughs in Artificial Intelligence (AI) have all transformed education. In recent years, there has been an emergence of more advanced AI-enabled learning systems, which are gaining traction due to their ability to deliver learning content and adapt to the individual needs of students. Yet, even though these contemporary learning systems are useful educational platforms that meet students’ needs, there is still a low number of implemented systems designed to address the concerns and problems faced by many students. Based on this perspective, a systematic mapping of the literature on AI-enabled adaptive learning systems was performed in this work. A total of 147 studies published between 2014 and 2020 were analysed. The major findings and contributions of this paper include the identification of the types of AI-enabled learning interventions used, a visualisation of the co-occurrences of authors associated with major research themes in AI-enabled learning systems and a review of common analytical methods and related techniques utilised in such learning systems. This mapping can serve as a guide for future studies on how to better design AI-enabled learning systems to solve specific learning problems and improve users’ learning experiences.

AI-enabled adaptive learning systems: A systematic mapping of the literature

Mixed Reality - Merging Real and Virtual Worlds

AIRA Chatbot for Travel: Case Study of AirAsia

In the education industry, lecturers are finding ways to improve students’ concentrations and grades by using smart devices to track students’ assignment or tutorial progress. One of the few possible and attractive solution is by using the gamification technique. This paper proposes an educational application called Questionify that implements the gamification elements and allow users to collect points, gain achievements, increase motivation and engagement towards students’ coursework in Software Engineering subject. Questionify is developed using C# and Java language has been evaluated using questionnaire among 24 respondents. The findings showed that the respondents believe that gamification can do better in education as compare to the traditional method of teaching the students. In the future, this gamification approach will be tested on more technical subjects such as programming and networking subjects to help students engage in a different learning approach.

Questionify: Gamification in Education

This paper discusses an adaptive learning system for both lecturers and students, designed to optimize learning based on unique learning pace across the classes. The proposed system is user-friendly and has the ability to analyze the level of understanding among students according to the cognitive level as required in the module. The system also allows students to use the web-application as and when required, therefore enabling them to progress on the module even after class hours. In addition, the system is equipped with reporting module so the lecturers are able to monitor and make changes to the study method accordingly. The implementation of the proposed adaptive learning system was tested on Java Programming course during the trial run.

Adaptive learning system for higher learning

With the inauguration of Big Data Analytics initiative nationally, many nations have participated and paved way for BDA ecosystem. The initiative is a catalyst to further encourage economic growth in Public Sectors. Some of the common key deliverables identified are increasing productivity involving information communications technology, cost savings, shared benefits, and encourage innovation. The objectives can be further elaborated by driving big data analytics demands in various public sectors agency, adopting big data analytics framework supporting the building of big data industry. This has encouraged talents and startup companies inspiring their capabilities by developing various technology platform, collaborate and innovate amongst public and private sectors, and further strengthen data governance by creating policy and procedures. With the establishment of big data analytics framework, performance measurement can be enforced effortlessly using the principles of business intelligence maturity model and the technological stack comes with it. Various data sources can be used to benchmark service quality using advanced analytics and data science techniques.

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Business intelligence addressing service quality for big data analytics in public sector

This paper presents an expert system for detecting and diagnosing possible illnesses among patients using a natural language interface of a chatterbot The Intelligent Healthcare Chatterbot (HECIA) takes input from patients in the form of texts, extract their keywords, and assign the patients' queries to a particular department for further examination and treatment HECIA also helps in promoting the facilities and departments of the medical center under study through human-machine conversation

Vinothini Kasinathan

Papers

AIRA Chatbot for Travel: Case Study of AirAsia

Questionify: Gamification in Education

Adaptive learning system for higher learning

Business intelligence addressing service quality for big data analytics in public sector

Intelligent Healthcare Chatterbot (HECIA): Case study of medical center in Malaysia