Abstract Chatbots hold the promise of revolutionizing education by engaging learners, personalizing learning activities, supporting educators, and developing deep insight into learners’ behavior. However, there is a lack of studies that analyze the recent evidence-based chatbot-learner interaction design techniques applied in education. This study presents a systematic review of 36 papers to understand, compare, and reflect on recent attempts to utilize chatbots in education using seven dimensions: educational field, platform, design principles, the role of chatbots, interaction styles, evidence, and limitations. The results show that the chatbots were mainly designed on a web platform to teach computer science, language, general education, and a few other fields such as engineering and mathematics. Further, more than half of the chatbots were used as teaching agents, while more than a third were peer agents. Most of the chatbots used a predetermined conversational path, and more than a quarter utilized a personalized learning approach that catered to students’ learning needs, while other chatbots used experiential and collaborative learning besides other design principles. Moreover, more than a third of the chatbots were evaluated with experiments, and the results primarily point to improved learning and subjective satisfaction. Challenges and limitations include inadequate or insufficient dataset training and a lack of reliance on usability heuristics. Future studies should explore the effect of chatbot personality and localization on subjective satisfaction and learning effectiveness. 

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Interacting with educational chatbots: A systematic review

Conversational Agents (CAs) are widely spread in a variety of domains, such as health and customer service. There is a recent trend of increasing publications and implementations of CAs in education. We conduct a systematic literature review to identify common methodologies, pedagogical CA roles, addressed target groups, the technologies and theories behind, as well as human-like design aspects. The initially found 3329 records were systematically reduced to 252 fully coded articles. Based on the analysis of the codings, we derive further research streams. Our results reveal a research gap for longterm studies on the use of CAs in education, and there is insufficient holistic design knowledge for pedagogical CAs. Moreover, target groups other than academic students are rarely considered. We condense our findings in a morphological box and conclude that pedagogical CAs have not yet reached their full potential of long-term practical application in education.

Conversational Agents in Education - A Systematic Literature Review

Background In knowledge transfer for educational purposes, most cancer hospital or center websites have existing information on cancer health. However, such information is usually a list of topics that are neither interactive nor customized to offer any personal touches to people facing dire health crisis and to attempt to understand the concerns of the users. Patients with cancer, their families, and the general public accessing the information are often in challenging, stressful situations, wanting to access accurate information as efficiently as possible. In addition, there is seldom any comprehensive information specifically on radiotherapy, despite the large number of older patients with cancer, to go through the treatment process. Therefore, having someone with professional knowledge who can listen to them and provide the medical information with good will and encouragement would help patients and families struggling with critical illness, particularly during the lingering pandemic. Objective This study created a novel virtual assistant, a chatbot that can explain the radiation treatment process to stakeholders comprehensively and accurately, in the absence of any similar software. This chatbot was created using the IBM Watson Assistant with artificial intelligence and machine learning features. The chatbot or bot was incorporated into a resource that can be easily accessed by the general public. Methods The radiation treatment process in a cancer hospital or center was described by the radiotherapy process: patient diagnosis, consultation, and prescription; patient positioning, immobilization, and simulation; 3D-imaging for treatment planning; target and organ contouring; radiation treatment planning; patient setup and plan verification; and treatment delivery. The bot was created using IBM Watson (IBM Corp) assistant. The natural language processing feature in the Watson platform allowed the bot to flow through a given conversation structure and recognize how the user responds based on recognition of similar given examples, referred to as intents during development. Therefore, the bot can be trained using the responses received, by recognizing similar responses from the user and analyzing using Watson natural language processing. Results The bot is hosted on a website by the Watson application programming interface. It is capable of guiding the user through the conversation structure and can respond to simple questions and provide resources for requests for information that was not directly programmed into the bot. The bot was tested by potential users, and the overall averages of the identified metrics are excellent. The bot can also acquire users’ feedback for further improvements in the routine update. Conclusions An artificial intelligence–assisted chatbot was created for knowledge transfer regarding radiation treatment process to the patients with cancer, their families, and the general public. The bot that is supported by machine learning was tested, and it was found that the bot can provide information about radiotherapy effectively.

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Learning the Treatment Process in Radiotherapy Using an Artificial Intelligence–Assisted Chatbot: Development Study

BACKGROUND
In knowledge transfer for educational purposes, most cancer hospital/centre websites have existing information on cancer health. However, such information is usually a list of topics which are neither interactive nor offering any personal touches to people facing dire health crisis, not to say attempting to understand the concerns of the users. Cancer patients, their families and the general public accessing the information are often in challenging, stressful, situations, wanting to access accurate information as efficiently as possible. In addition, there is seldom any comprehensive information specifically on radiotherapy, despite the large number of cancer patients in senior ages, to go through the treatment process. Therefore, having "someone" with professional knowledge who can "listen" to them, provide the medical information with good will and encouragement would help patients and families struggling with critical illness, in particular during the lingering pandemic.


OBJECTIVE
The goal of this work is to create a novel virtual assistant, a chatbot that can explain the radiation treatment process to stakeholders comprehensively and accurately, in the absence of any similar software. This virtual assistant was created using the IBM WATSON assistant with artificial intelligence (AI) and machine learning (ML) features. The chatbot or Bot was incorporated into a resource that can be easily accessed by the general public.


METHODS
The radiation treatment process in a cancer hospital/centre was described by the radiotherapy process: (1) patient diagnosis, consultation and prescription, (2) patient positioning, immobilization and simulation, (3) 3D imaging for treatment planning, (4) target and organ contouring, (5) radiation treatment planning, (6) patient setup and plan verification, and (7) treatment delivery. The Bot was created using IBM WATSON assistant. The natural language processing (NLP) feature in the WATSON platform allowed the Bot to flow through a given conversation structure and recognize how the user responds based on recognition of similar given examples, referred to as "intents" during development. The Bot therefore can be trained from the responses received, by recognizing similar responses from the user and analysed by the WATSON NLP.


RESULTS
The Bot is hosted on a website by the WATSON application programming interface (API). It is capable of guiding the user through the conversation structure and can respond to simple questions and provide resources for requests for information that was not directly programmed into the Bot. The Bot was tested by potential users and the overall averages of the identified metrics are excellent. The Bot can also acquire users' feedbacks for further improvements in the routine update.


CONCLUSIONS
An AI-assisted chatbot was created for the knowledge transfer of radiation treatment process to the cancer patients, their families and general public. The Bot with character supported by ML was tested, and it was found that the bot can provide information in radiotherapy effectively.


CLINICALTRIAL

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Learning the Treatment Process in Radiotherapy: An Al-assisted Chatbot.

This paper describes the development of a chatbot as a cognitive user interface for portfolio optimization. The financial portfolio optimization chatbot is proposed to provide an easy-to-use interface for portfolio optimization, including a wide range of investment objectives and flexibility to include a variety of constraints representing investment preferences when compared to existing online automated portfolio advisory services. Additionally, the use of a chatbot interface allows investors lacking a background in quantitative finance and optimization to utilize optimization services. The chatbot is capable of extracting investment preferences from natural text inputs, handling these inputs with a backend financial optimization solver, analyzing the results, and communicating the characteristics of the optimized portfolio back to the user. The architecture and design of the chatbot are presented, along with an implementation using the IBM Cloud, SS&C Algorithmics Portfolio Optimizer, and Slack as an example of this approach. The design and implementation using cloud applications provides scalability, potential performance improvements, and could inspire future applications for financial optimization services.

https://tspace.library.utoronto.ca/bitstream/1807/105424/1/jrfm-14-00180.pdf

Cognitive User Interface for Portfolio Optimization

This complete research work presents the use of chatbot in the educational context for automation of care to the student of higher education. Due to the evolution of technologies in educational and professional contexts, new teaching/learning methods have been an emerging demand for today’s society. The expansion of learning modalities such as face-to-face, combined and distance learning as well as demand anywhere, at any time, has been a premise in these education systems. With the advancement of virtual learning environments (VLEs), successful interaction depends on solutions that enable efficient communication between the student and the educational context. One way to achieve this goal has been to use chatbots to respond to students’ questions in the educational field. This has become possible through artificial intelligence and natural language processing embodied in simple service platforms. The purpose of this article is to develop a chatbot that interacts with students through text messages, on subjects in a closed context, to students’ doubts about the Higher Education Institution Course. To validate chatbot performance, it will be submitted to a set of questions of the same context that were not correctly answered in the previous interactions. The results of these interactions will be analyzed in order to verify the efficiency and contribution of a chatbot as a support tool for the student. It is hoped that this study contributes to solutions for assistance automation in the learning context.

IBM Watson Application as FAQ Assistant about Moodle

This survey features an intelligent IBM Watson chatbot to improve student participation in Moodle. Recurrent Neural Networks were used to generate a model and classify the intentions of 48 students who interacted with the assistant. A neural network was trained and submitted to the evaluation criteria and obtained 99.80% precision tests. The virtual assistant also demonstrated the ability to collaborate in student service activities. Infrequently asked questions about the use of Moodle. This study showed evidence that it is feasible to implement the chatbot to help with doubts in these contexts.

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Jeferson da Silva Oliveira

Papers

IBM Watson Application as FAQ Assistant about Moodle

Intent Classifier Model using Recurrent Neural Networks: a case study to help students with doubts about the functionalities of AVA MOODLE