Название: A Beginner's Guide to Introduce Artificial Intelligence in Teaching and Learning
Автор: Muralidhar Kurni, Mujeeb Shaik Mohammed, Srinivasa K.G.
Издательство: Springer
Год: 2023
Страниц: 236
Язык: английский
Формат: pdf (true), epub
Размер: 12.2 MB

This book reimagines education in today’s Artificial Intelligence (AI) world and the Fourth Industrial Revolution. Artificial intelligence will drastically affect every industry and sector, and education is no exception. This book aims at how AI may impact the teaching and learning process in education. This book is designed to demystify AI for teachers and learners. This book will help improve education and support institutions in the phenomena of the emergence of AI in teaching and learning. This book presents a comprehensive study of how AI improves teaching and learning, from AI-based learning platforms to AI-assisted proctored examinations. This book provides educators, learners, and administrators on how AI makes sense in their everyday practice. Describing the application of AI in ten key aspects, this comprehensive volume prepares educational leaders, designers, researchers, and policymakers to effectively rethink the teaching and learning process and environments that students need to thrive. The readers of this book never fall behind the fast pace and promising innovations of today’s most advanced learning technology.

What Is Artificial Intelligence? Artificial Intelligence, or AI, is the study of using data in large quantities to program intelligent machines. Incorporating previous knowledge and experience, these systems can mimic human performance. It improves how quickly, accurately, and successfully humans can complete tasks. In order to create autonomous devices, AI researchers and developers employ elaborate algorithms and techniques. The foundations of AI are the techniques of Machine Learning and Deep Learning.

Artificial Intelligence (AI) is significant because it can potentially improve enterprises’ processes and provide new insights that were previously unavailable. It is common for AI systems to finish projects fast and with relatively few errors, especially when it comes to repeated, detail-oriented activities like evaluating many legal documents to verify essential fields are filled out correctly.

Machine Learning (ML) is a method for making predictions without using predetermined rules by analyzing vast volumes of data to find patterns and construct a model. Recent developments in machine learning-based computational algorithms have enabled several recent AI advancements, such as natural language processing, facial recognition, and self-driving automobiles. In this context, we talk about “learning” algorithms instead of “hard-coded” ones.

Supervised learning, unsupervised learning, and reinforcement are the three most common ML methods. In the case of supervised learning, data is used that has already been tagged, such as thousands upon thousands of photos of people annotated by humans. By associating the data with the labels, supervised learning generates a model that can be applied to comparable data, such as automatically identifying persons in new images. Regarding unsupervised learning, the AI is given even more data but is not labeled or categorized this time. Unsupervised learning aims to find previously unseen clusters or patterns in the data that can be used to assign labels to fresh observations. It may, for instance, scan handwritten text for recognizable letters and numbers by analyzing thousands of examples for similarities.

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