Essential Features and Critical Issues With Educational Chatbots

2021 ◽  
pp. 246-262
Author(s):  
Jeremy Riel
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Formal Learning ◽  
Automated Systems ◽  
Conversational Agents ◽  
Teacher Instruction ◽  
Practical Applications ◽  
Significant Potential ◽  
Critical Issues ◽  
Future Work

Conversational agents, also known as chatbots, are automated systems for engaging in two-way dialogue with human users. These systems have existed in one form or another for at least 60 years but have recently demonstrated significant potential with advances in machine learning and artificial intelligence technologies. The use of conversational agents or chatbots for education can potentially reduce costs and supplement teacher instruction in transformative ways for formal learning. This chapter examines the design and status of chatbots and conversational agents for educational purposes. Common design functions and goals of educational chatbots are described, along with current practical applications of chatbots for educational purposes. Finally, this chapter considers issues about pedagogical commitments, ethics, and equity to suggest future work in the field.

scholarly journals IMPROVING INTERNET OF THINGS PARKING SYSTEMS

2021 ◽  
Vol 19 (3) ◽  
pp. 163
Author(s):  
Dušan Bogićević
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Information Needs ◽  
Vehicle Classification ◽  
The Internet ◽  
Parking Space ◽  
Automated Systems ◽  
The Internet Of Things

Edge data processing represents the new evolution of the Internet and Cloud computing. Its application to the Internet of Things (IoT) is a step towards faster processing of information from sensors for better performance. In automated systems, we have a large number of sensors, whose information needs to be processed in the shortest possible time and acted upon. The paper describes the possibility of applying Artificial Intelligence on Edge devices using the example of finding a parking space for a vehicle, and directing it based on the segment the vehicle belongs to. Algorithm of Machine Learning is used for vehicle classification, which is based on vehicle dimensions.

scholarly journals Application of Machine Learning in Auditing Teaching: A Case Study of Predicting the Audit Report Type of China ST Listed Companies

2020 ◽  
Vol 2 (1) ◽  
pp. 023-040
Author(s):  
Shi-Ming Huang Shi-Ming Huang ◽  
Chang-ping Chen Shi-Ming Huang ◽  
Tzu-ching Wong Chang-ping Chen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Listed Company ◽  
Accounting Students ◽  
Computer Assisted ◽  
Research Database ◽  
Audit Report ◽  
Professional Fields ◽  
Future Work

<p>Artificial intelligence is an important emerging technology in the accounting industry. Fear and hype associated with artificial intelligence and its impact on accounting and auditing jobs have pervaded the professional fields of accounting and auditing. It is important to develop AI competency in accountants and auditors. This paper presents a teaching case for a professor or lecturer to use for teaching machine learning to accounting students. The case is based on openly available data from the China Stock Market & Accounting Research database and aims to teach students how to predict the future audit report type of a China ST listed company. Through case teaching, students can learn skills related to computer-assisted auditing tools and machine learning (such as ACL) develop the confidence to apply artificial intelligence in their education and future work.</p> <p>&nbsp;</p>

scholarly journals Supervised Learning Methods and Applications in Medical Research

2022 ◽  
pp. 27-34
Author(s):  
Yung Ming ◽  
Lily Yuan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Feature Extraction ◽  
Job Performance ◽  
Computational Linguistics ◽  
New Technologies ◽  
Voice Recognition ◽  
Autonomous Driving ◽  
Automated Systems

Machine Learning (ML) and Artificial Intelligence (AI) methods are transforming many commercial and academic areas, including feature extraction, autonomous driving, computational linguistics, and voice recognition. These new technologies are now having a significant effect in radiography, forensics, and many other areas where the accessibility of automated systems may improve the precision and repeatability of essential job performance. In this systematic review, we begin by providing a short overview of the different methods that are currently being developed, with a particular emphasis on those utilized in biomedical studies.

scholarly journals Zhorai: Designing a Conversational Agent for Children to Explore Machine Learning Concepts

2020 ◽  
Vol 34 (09) ◽  
pp. 13381-13388
Author(s):  
Phoebe Lin ◽  
Jessica Van Brummelen ◽  
Galit Lukin ◽  
Randi Williams ◽  
Cynthia Breazeal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Young Children ◽  
Mental Models ◽  
Small Groups ◽  
Smart Devices ◽  
The Novel ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Limited Knowledge

Understanding how machines learn is critical for children to develop useful mental models for exploring artificial intelligence (AI) and smart devices that they now frequently interact with. Although children are very familiar with having conversations with conversational agents like Siri and Alexa, children often have limited knowledge about AI and machine learning. We leverage their existing familiarity and present Zhorai, a conversational platform and curriculum designed to help young children understand how machines learn. Children ages eight to eleven train an agent through conversation and understand how the knowledge is represented using visualizations. This paper describes how we designed the curriculum and evaluated its effectiveness with 14 children in small groups. We found that the conversational aspect of the platform increased engagement during learning and the novel visualizations helped make machine knowledge understandable. As a result, we make recommendations for future iterations of Zhorai and approaches for teaching AI to children.

scholarly journals Trust in Artificial Intelligence: Comparing trust processes between human and automated trustees in light of unfair bias

2021 ◽  
Author(s):  
Markus Langer ◽  
Cornelius J. König ◽  
Caroline Back ◽  
Victoria Hemsing
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Personnel Selection ◽  
Automated System ◽  
Ethical Standards ◽  
Automated Systems ◽  
Trust Repair ◽  
Selection Tasks ◽  
Decision Tasks

Introducing automated systems based on artificial intelligence and machine learning for ethically sensitive decision tasks requires investigating of trust processes in relation to such tasks. In an example of such a task (personnel selection), this study investigates trustworthiness, trust, and reliance in light of a trust violation relating to ethical standards and a trust repair intervention. Specifically, participants evaluated applicant preselection outcomes by either a human or an automated system across twelve personnel selection tasks. We additionally varied information regarding imperfection of the human and automated system. In task rounds five through eight, the preselected applicants were predominantly male, thus constituting a trust violation due to a violation of ethical standards. Before task round nine, participants received an excuse for the biased preselection (i.e., a trust repair intervention). Results showed that participants initially perceived automated systems to be less trustworthy, and had less intention to trust automated systems. Specifically, participants perceived systems to be less able, and flexible, but also less biased – a result that was sustained even in light of unfair bias. Furthermore, in regard to the automated system the trust violation and the trust repair intervention had weaker effects. Those effects were partly stronger when highlighting imperfection for the automated system. We conclude that it is crucial to investigate trust processes in relation to automated systems in ethically sensitive domains such as personnel selection as insights from classical areas of automation might not translate to application contexts where ethical standards are central to trust processes.

scholarly journals Chatbot and its Practical Applications in the Materialistic World

2019 ◽  
Vol 8 (4) ◽  
pp. 4459-4463
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industrial Revolution ◽  
Gradual Increase ◽  
The Other ◽  
Way Of Life ◽  
Practical Applications ◽  
Other Hand ◽  
The World ◽  
Different Types

These days Chat has become the new way of conversation and changed the way of life and the view that the world used to see before and due to Industrial revolution 4.0 , the gradual increase in machine learning and artificial intelligence fields has gone to higher and many companies are reaching customers to get their products with more ease . This is where chatbots are used. It all started with one question! can machines think? The concept of chatbots came into existence to check whether the machines could fool users and make them think that they are actually talking to humans and not robots. On the Other hand, with the Successes Rate of Chat bots, Different companies Started using machines for having conversations with their customers about everything which made their work simpler and reduced the need of man power. There are many different types of building a chatbot but this paper will mainly concentrate on building a Chatbot using TensorFlow API in python

scholarly journals Performance Analysis and Study of Chatbots

2021 ◽  
pp. 345-348
Author(s):  
Prof. Ahlam Ansari ◽  
Fakhruddin Bootwala ◽  
Owais Madhia ◽  
Anas Lakdawala
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Performance Analysis ◽  
User Experience ◽  
Language Processing ◽  
Conversational Agents ◽  
Learning Machines ◽  
The Past

Artificial intelligence, machine learning and deep learning machines are being used as conversational agents. They are used to impersonate a human and provide the user a human-like experience. Conversational software agents that use natural language processing is called a chatbot and it is widely used for interacting with users. It provides appropriate and satisfactory answers to the user. In this paper we have analyzed and compared various chatbots and provided a score to each of them on different parameters. We have asked each chatbot the same questions, and we have evaluated each answer, whether it’s satisfactory or not. This analysis is based on user experience rather than analyzing the software of each chatbot. This paper proves that even though chatbot performance has highly increased compared to the past, there is still quite a lot of room for improvement.

scholarly journals Artificial intelligence is an oxymoron

AI & Society ◽  
2021 ◽  
Author(s):  
Jakob Svensson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Science ◽  
Evolutionary Biology ◽  
Unintended Consequences ◽  
Present Moment ◽  
Automated Systems ◽  
Cognitive Sciences ◽  
Nicholas Of Cusa ◽  
Organic Body

AbstractDeparting from popular imaginations around artificial intelligence (AI), this article engages in the I in the AI acronym but from perspectives outside of mathematics, computer science and machine learning. When intelligence is attended to here, it most often refers to narrow calculating tasks. This connotation to calculation provides AI an image of scientificity and objectivity, particularly attractive in societies with a pervasive desire for numbers. However, as is increasingly apparent today, when employed in more general areas of our messy socio-cultural realities, AI- powered automated systems often fail or have unintended consequences. This article will contribute to this critique of AI by attending to Nicholas of Cusa and his treatment of intelligence. According to him, intelligence is equally dependent on an ability to handle the unknown as it unfolds in the present moment. This suggests that intelligence is organic which ties Cusa to more contemporary discussions in tech philosophy, neurology, evolutionary biology, and cognitive sciences in which it is argued that intelligence is dependent on having—and acting through—an organic body. Understanding intelligence as organic thus suggests an oxymoronic relationship to artificial.

scholarly journals Machine Learning and Its Application in Skin Cancer

2021 ◽  
Vol 18 (24) ◽  
pp. 13409
Author(s):  
Kinnor Das ◽  
Clay J. Cockerell ◽  
Anant Patil ◽  
Paweł Pietkiewicz ◽  
Mario Giulini ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Skin Cancer ◽  
Early Detection ◽  
Statistical Models ◽  
Automated Systems ◽  
Deep Convolutional Neural Networks ◽  
Cancer Early Detection ◽  
Cancer Specialists ◽  
Financial Burdens

Artificial intelligence (AI) has wide applications in healthcare, including dermatology. Machine learning (ML) is a subfield of AI involving statistical models and algorithms that can progressively learn from data to predict the characteristics of new samples and perform a desired task. Although it has a significant role in the detection of skin cancer, dermatology skill lags behind radiology in terms of AI acceptance. With continuous spread, use, and emerging technologies, AI is becoming more widely available even to the general population. AI can be of use for the early detection of skin cancer. For example, the use of deep convolutional neural networks can help to develop a system to evaluate images of the skin to diagnose skin cancer. Early detection is key for the effective treatment and better outcomes of skin cancer. Specialists can accurately diagnose the cancer, however, considering their limited numbers, there is a need to develop automated systems that can diagnose the disease efficiently to save lives and reduce health and financial burdens on the patients. ML can be of significant use in this regard. In this article, we discuss the fundamentals of ML and its potential in assisting the diagnosis of skin cancer.

scholarly journals Classification of Explainable Artificial Intelligence Methods through Their Output Formats

2021 ◽  
Vol 3 (3) ◽  
pp. 615-661
Author(s):  
Giulia Vilone ◽  
Luca Longo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Classification System ◽  
Hierarchical Classification ◽  
Future Research ◽  
Explainable Artificial Intelligence ◽  
Scientific Papers ◽  
The Many ◽  
Application Fields ◽  
Future Work

Machine and deep learning have proven their utility to generate data-driven models with high accuracy and precision. However, their non-linear, complex structures are often difficult to interpret. Consequently, many scholars have developed a plethora of methods to explain their functioning and the logic of their inferences. This systematic review aimed to organise these methods into a hierarchical classification system that builds upon and extends existing taxonomies by adding a significant dimension—the output formats. The reviewed scientific papers were retrieved by conducting an initial search on Google Scholar with the keywords “explainable artificial intelligence”; “explainable machine learning”; and “interpretable machine learning”. A subsequent iterative search was carried out by checking the bibliography of these articles. The addition of the dimension of the explanation format makes the proposed classification system a practical tool for scholars, supporting them to select the most suitable type of explanation format for the problem at hand. Given the wide variety of challenges faced by researchers, the existing XAI methods provide several solutions to meet the requirements that differ considerably between the users, problems and application fields of artificial intelligence (AI). The task of identifying the most appropriate explanation can be daunting, thus the need for a classification system that helps with the selection of methods. This work concludes by critically identifying the limitations of the formats of explanations and by providing recommendations and possible future research directions on how to build a more generally applicable XAI method. Future work should be flexible enough to meet the many requirements posed by the widespread use of AI in several fields, and the new regulations.

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