The demand for automated customer support approaches in customer-centric environments has increased significantly in the past few years. Natural Language Processing (NLP) advancement has enabled conversational AI to comprehend human language and respond to enquiries from customers automatically independent of the intervention of humans. Customers can now access prompt responses from NLP chatbots without interacting with human agents. This application has been implemented in numerous business sectors, including banking, manufacturing, education, law, and healthcare, among others. This study reviewed earlier studies on automating customer queries using NLP approaches. Using a systematic review methodology, 73 articles were analysed from reputable digital resources. The evaluated result offers an in-depth review of prior studies investigating the use of NLP techniques for automated customer service responses, including details on existing studies, benefits, and potential future study topics on the use of NLP techniques for business applications. The implications of the results were discussed and, recommendations made.

In the current African society, Artificial Intelligence (AI) is becoming more popular and seeking to cover all facets of human activity. The adoption and use of these modern technologies in the African context are currently low due to some emerging challenges. Consequently, these difficulties may have a direct influence on African economic development. In this paper, we highlight the challenges facing the adoption of AI technologies in Africa which include skills acquisition, lack of structured data ecosystem, ethics, government policies, insufficient infrastructure and network connectivity, uncertainty, and user attitude. Finally, various solutions to enhance AI adoption in Africa were then proposed.

Background: Educational games have been proven to support the teaching of various concepts across disciplines. Plagiarism is a major problem among undergraduate and postgraduate students at universities. Objective: In this paper, we propose a game called Plagi-Warfare that attempts to teach students about plagiarism. Methods: To do this at a level that is beyond quizzes, we proposed a game storyline and mechanics that allow the player (or student) to play as a mafia member or a detective. This either demonstrated their knowledge by plagiarizing within the game as a mafia member or catching plagiarists within the game as a detective. The game plays out in a 3D environment representing the major libraries of the University of Johannesburg, South Africa. In total, 30 students were selected to evaluate the game. Results: Evaluation of the game mechanics and storyline showed that the student gamers enjoyed the game and learned about plagiarism. Conclusions: In this paper, we presented a new educational game that teaches students about plagiarism by using a new crime story and an immersive 3D gaming environment representing the libraries of the University of Johannesburg.

Instant messaging (IM) has gained huge popularity, not only in the social space, but in many sectors of work and everyday life. One such sector is finance, where IMs are used for bargaining, trading and many other financial information exchanges between parties. A popular IM software in finance is the Instant Bloomberg (IB). The conversations (chat histories) recorded via IB often contain crucial information about the financial positions of the market, quotes for different instruments, as well as unimportant texts. Automatic comprehension of this type of text is a natural language processing task. In this paper we employ front-end compiler analyses in the automatic comprehension and summarisation of financial chats retrieved from IB. Taking the result, we present a software tool called FINCHAN that takes lengthy chat histories and attempts to recognise the syntax up to a success rate of 98%, extracts semantic information to 61% success rate, generates concise summaries of these chats, and offers analyses of hidden patterns, text-to-speech synthesis of generated summaries, with functionalities for archiving/indexing IMs.