The introduction of Artificial Intelligence technology enables the integration of Chatbot systems into various aspects of education. This technology is increasingly being used for educational purposes. Chatbot technology has the potential to provide quick and personalised services to everyone in the sector, including institutional employees and students. This paper presents a systematic review of previous studies on the use of Chatbots in education. A systematic review approach was used to analyse 53 articles from recognised digital databases. The review results provide a comprehensive understanding of prior research related to the use of Chatbots in education, including information on existing studies, benefits, and challenges, as well as future research areas on the implementation of Chatbot technology in the field of education. The implications of the findings were discussed, and suggestions were made.

Chatbots applications in education: A systematic review

The use of natural language interfaces in the field of human-computer interaction (HCI) is undergoing intense study through dedicated scientific and industrial research. The latest contributions in the field, including deep learning approaches like recurrent neural networks (RNNs), the potential of context-aware strategies and user-centred design approaches, have brought back the attention of the community to software-based dialogue systems, generally known as conversational agents or chatbots. Nonetheless, and given the novelty of the field, a generic, context-independent overview of the current state of research on conversational agents covering all research perspectives involved is missing. Motivated by this context, this article reports a survey of the current state of research of conversational agents through a systematic literature review of secondary studies. The conducted research is designed to develop an exhaustive perspective through a clear presentation of the aggregated knowledge published by recent literature within a variety of domains, research focuses and contexts. As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents’ field, which is expected to help researchers and to lay the groundwork for future research in the field of natural language interfaces.

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Software-Based Dialogue Systems: Survey, Taxonomy, and Challenges

Background Coronavirus continues to spread worldwide, causing various health and economic disruptions. One of the most important approaches to controlling the spread of this disease is to use an artificial intelligence (AI)–based technological intervention, such as a chatbot system. Chatbots can aid in the fight against the spread of COVID-19. Objective This paper introduces COVID-Bot, an intelligent interactive system that can help screen students and confirm their COVID-19 vaccination status. Methods The design and development of COVID-Bot followed the principles of the design science research (DSR) process, which is a research method for creating a new scientific artifact. COVID-Bot was developed and implemented using the SnatchBot chatbot application programming interface (API) and its predefined tools, which are driven by various natural language processing algorithms. Results An evaluation was carried out through a survey that involved 106 university students in determining the functionality, compatibility, reliability, and usability of COVID-Bot. The findings indicated that 92 (86.8%) of the participants agreed that the chatbot functions well, 85 (80.2%) agreed that it fits well with their mobile devices and their lifestyle, 86 (81.1%) agreed that it has the potential to produce accurate and consistent responses, and 85 (80.2%) agreed that it is easy to use. The average obtained α was .87, indicating satisfactory reliability. Conclusions This study demonstrates that incorporating chatbot technology into the educational system can combat the spread of COVID-19 among university students. The intelligent system does this by interacting with students to determine their vaccination status.

COVID-Bot, an Intelligent System for COVID-19 Vaccination Screening: Design and Development

Proactive Chatbot Framework Based on the PS2CLH Model: An AI-Deep Learning Chatbot Assistant for Students

Increased screen time may cause significant health impacts, including harmful effects on mental health. Studies on the association between technological obsessions and their influence on health have been conducted using Deep Learning (DL) and Machine Learning (ML) techniques. The deployment of chatbots in different industries has been proven as a game-changer. We study conversational Artificial Intelligence (AI) systems enabling operators to conduct conversations with machines that resemble those with humans. We design and develop two retrieval-based and generative-based chatbots, each with six designs. Among the retrieval-based chatbots, Vanilla Recurrent Neural Network (RNN) has an accuracy of 83.22%, Long Short Term Memory (LSTM) is 89.87% accurate, Bidirectional LSTM (Bi-LSTM) is 91.57% accurate, Gated Recurrent Unit (GRU) is 65.57% accurate, and Convolution Neural Network (CNN) is 82.33% accurate. In comparison, generative-based chatbots have encoder–decoder designs that are 94.45% accurate. The most significant distinction is that while generative-based chatbots can generate new text, retrieval-based chatbots are restricted to responding to inputs that match the best of the outputs they already know. 

A comparative study of retrieval-based and generative-based chatbots using Deep Learning and Machine Learning

Conversation Agents also known as Chatbots are considered the next big thing in the modern technological era. Chatbots aim to emulate humans, by mimicking human conversation in the most realistic way possible. There are several methods to generate such intelligent agents. Thus to evaluate the effectiveness of the various approaches, a systematic review of the approaches is required. This paper traces the evolution of chatbots and also elaborates on the impact chatbots have in different industries. Additionally, it also presents a survey of the different modern chatbot methodologies that different researchers have proposed. When analyzing the different approached, this paper has taken into consideration the dataset, input-output, design, development, performance metric, and limitations into consideration. In the end, we've elaborated on the key insights that were obtained pertaining to the crucial factors that influence chatbot design, generation, and development.

Ragini Patil

Papers

Chat with Bots Intelligently: A Critical Review & Analysis