Patients, hospitals, sensors, researchers, providers, phones, and healthcare organisations are producing enormous amounts of data in both the healthcare and drug detection sectors. The real challenge in these sectors is to find, investigate, manage, and collect information from patients in order to make their lives easier and healthier, not only in terms of formulating new therapies and understanding diseases, but also to predict the results at earlier stages and make effective decisions. The volumes of data available in the fields of pharmacology, toxicology, and pharmaceutics are constantly increasing. These increases are driven by advances in technology, which allow for the analysis of ever-larger data sets. Big Data (BD) has the potential to transform drug development and safety testing by providing new insights into the effects of drugs on human health. However, harnessing this potential involves several challenges, including the need for specialised skills and infrastructure. In this survey, we explore how BD approaches are currently being used in the pharmacology, toxicology, and pharmaceutics fields; in particular, we highlight how researchers have applied BD in pharmacology, toxicology, and pharmaceutics to address various challenges and establish solutions. A comparative analysis helps to trace the implementation of big data in the fields of pharmacology, toxicology, and pharmaceutics. Certain relevant limitations and directions for future research are emphasised. The pharmacology, toxicology, and pharmaceutics fields are still at an early stage of BD adoption, and there are many research challenges to be overcome, in order to effectively employ BD to address specific issues.

Studies evaluating students’ UX of applications that will influence their continuance use of educational systems in Higher Educational Institutions (HEIs) have not been sufficiently addressed in the African region, specifically in Ghana. Thus, conducting a study on students’ UX of systems in HEIs will enhance students’ interest in continuing to use such systems. Therefore, this study examines students’ user experience (UX) and how it impacts their continued use of the Student Management Information System (SMIS). The study proposed a research model by integrating user experience questionnaire (UEQ) constructs with continuance intention to use. The study adopted an online questionnaire to collect data from 415 students at Koforidua Technical University (KTU). The partial least square-structural equation model (PLS-SEM) method was used to evaluate the proposed model’s reliability, validity, and relationship among the constructs. The UEQ data analysis tool was used in analysing the data. The study’s findings showed that attractiveness, perspicuity, efficiency, stimulation, and novelty significantly influenced students’ continuance intention to use the SMIS. However, dependability did not significantly affect students’ continuance intention to use the SMIS. Also, the study’s findings on the benchmark results showed that attractiveness, perspicuity, and stimulation were categorized as good, while dependability, efficiency, and novelty were categorized as excellent. These findings offer valuable insights for UX designers and developers aiming to create engaging and intuitive SMIS solutions. Also, it will provide students feedback on system UX that can be incorporated into future release updates. Furthermore, this research contributes significantly to the understanding of student UX with SMIS in developing country HEIs and its impact on continued usage.

Agile development methodology has become popular among software developers. Some non-agile developers integrate certain agile and design thinking practices in their development. Among these practices include identifying personas, creating empathy and journey maps and writing user stories. This research adopts the Design Science Research Methodology, focusing on integrating agile and design thinking practices in generating quality user stories in the design of augmented reality (AR) applications. An integrated development process is modelled and applied in an example application, an AR application for student welfare services events. The study goes through the process of eliciting user requirements by identifying user personas, creating empathy maps and customer journeys and writing user stories as part of the discovery stage of the AR application development. The user stories are evaluated by fifteen agile practitioners based on fourteen criteria adapted from Quality User Story Framework. The user stories are scored and assessed to determine the level of quality. Request access from your librarian to read this article's full text.

A bibliometric analysis study investigating chatbots' current state and developments in education research has not been adequately addressed in literature. Thus, this study highlights the current state, emerging research trends and directions of chatbots in education using bibliometric analysis. The significance of the study is to provide insights into the most recent developments of chatbots application in education and future research directions for academics and practitioners. A bibliometric analysis of publications on chatbots in education published between 2012 and 2022 was conducted. A total of 759 publications were collected from the Scopus database for the bibliometric analysis. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was adopted to identify and screen the dataset. VOSviewer (version 1.6.18) was used to perform the following network analysis: co-authorship and cooccurrence. Also, Bibliometrix was used for the descriptive statistics of the bibliometric data and the publication trend. The study's findings showed that fewer studies have been conducted from the African region on chatbot education research. Researchers from the United Kingdom, United States, Australia, China, India, Greece, Japan, Vietnam, Malaysia, and United Arab Emirates have collaborated significantly in chatbot education research. Also, the main keyword occurrences in research on chatbots in education are chatbots, students, artificial intelligence, natural language processing, education, and learning. The trends indicate a steady increase in research on chatbots over the past decade.