Abstract
Artificial intelligence (AI) can address some of the most significant issues facing education today, innovate teaching and learning methods, and eventually quicken the fulfillment of SDG 4. However, these quick technological advancements carry with them several challenges that have thus far surpassed regulatory structures and policy discussions. The Education 2030 Agenda can be achieved with the help of AI technologies. AI has already been used in education, especially in various tools and assessment platforms that aid skill development. The goal is that as AI educational solutions continue to develop, they will help close gaps in learning and teaching and free up schools and teachers to accomplish more than before. To provide teachers the time and freedom to teach understanding and adaptability—uniquely human talents where computers would struggle— while AI can promote efficiency, personalization, and streamline administrative procedures. This chapter presents challenges and opportunities related to AI use in education and ends with recommendations.
TopBackground
When John McCarthy organized a two-month workshop at Dartmouth College in the United States in the 1950s, artificial intelligence (AI) was born. McCarthy used the term “artificial intelligence” for the first time in the workshop proposal in 1956 (Russel & Norvig, 2010): The hypothesis that every facet of learning or any other characteristic of intelligence can, in theory, be so succinctly stated that a computer could be created to replicate. It is the foundation upon which the study [of artificial intelligence] is to proceed. The broad definition of artificial intelligence offered by Baker and Smith (2019) is: “Computers that do cognitive functions, often associated with human minds, particularly learning and problem-solving.” They clarify that “AI” does not refer to a specific technology. It serves as a general phrase to describe various tools and techniques, including algorithms, neural networks, data mining, and machine learning. AI and machine learning are frequently used interchangeably. Machine learning is an artificial intelligence (AI) technique for supervised and unsupervised categorization and profiling. Machine learning is a branch of artificial intelligence, according to Popenici and Kerr (2017), that entails “software capable of recognizing patterns, making predictions, and applying newly discovered patterns to circumstances that were not included or covered by their initial design”. AI is based on rational agents, which may be defined as anything that can perceive its environment through sensors and act on it through actuators (Russel & Norvig,2010).
Key Terms in this Chapter
Deep Learning: Artificial neural networks, a class of algorithms inspired by the structure and operation of the brain, are the focus of the machine learning discipline known as deep learning.
CBE: Computer-based education.
Machine Learning: With machine learning (ml), a form of artificial intelligence (ai), software programs can predict outcomes more accurately without having to be explicitly instructed to do so. To forecast new output values, machine learning algorithms use historical data as input.
Teacher Bots: The most recent innovation in education technology is ai teaching robots. These devices are used in many schools' programs to carry out all of the teachers' monotonous duties. These bots can keep tabs on pupils' academic achievement in the classroom.
Artificial Intelligence: The replication of human intelligence functions by machines, particularly computer systems, is known as artificial intelligence. Expert systems, natural language processing, speech recognition, and machine vision are some examples of specific ai applications.
ICT: Information communication technology.
Digital Humanism: The movement toward people-literate technology and away from computer-literate people is known as digital humanism.
4IR: Fourth Industrial Revolution.