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The Role of Artificial Intelligence in Education: A Comprehensive Study

1. Introduction to Artificial Intelligence in Education

The term artificial intelligence (AI) was first coined in 1956 by a group of researchers at Dartmouth College, U.S. And over the years, it has evolved to a very high-performing technology that attempts to mimic human intelligence. The different concepts that can be associated with AI vary widely, and the use of AI in the field of educational technology is also expanding – that is, the use of intelligent tutoring systems and adaptive learning environments – all in a position to be the basis used in intelligent education. There is little evidence of cohesive deployment in practices, with the technology generally being exploited on an individual or small scale. According to Ahson and Ilyas, AI refers to the ability of a machine to carry out the operations usually requiring human intelligence, such as understanding of natural language, speech, and decision making. It is often regarded as a subset of information technology (IT), but AI is capable of improving the efficiency and quality of IT efforts to an unimaginable degree.

2. Benefits of AI in Homework Assistance

Homework assistance is one of the most pursued applications for AI in an educational setting, and it is probably the easiest and most straightforward application with early likely returns. Its potential benefits are wide-ranging and extremely impactful. Homework is the bridge which fills the hole of learning before it falls apart. If students fail at homework, the learning gap can turn into a full-blown canyon over time. Many students experience their first sense of being overwhelmed at this stage, leading to academic stress and its adverse consequences. This group of students may start to resist the classroom experience, and in some jurisdictions and situations, become involved in behavioral issues and potential dropout syndrome. AI as a homework assistant can intervene by providing instruction in understandable, different, and maybe even more enjoyable threads of delivery. Consequently, it is predicted that naturally curious students will be more attentive and will be ready to master different types of content. Gradually, these students will improve their skills and become proficient enough to overcome obstacles technically. This AI-based empowerment should increase overall achievement in a classroom and school, turning homework stress to success.

Second, students at a lower stage of their learning capability will benefit from homework assistance. AI can process their queries in a more understandable way, through personalized guidance protocols. Answer research questions they are not necessarily brave enough to ask in class by extracting explanations from model predictions, or other technical tools. Unlike humans, AI is not judgmental, which should increase their learning confidence. In addition, AI is always available and continually working, providing students with instant help, any time of the night or day. AI functioning will allow students to complete their homework with less academic stress, enabling them to engage in other pursuits, and preserving family harmony. Parents will no longer have to fight with their children to settle down and complete homework, nor will they have to struggle with the subject content.

3. Challenges and Ethical Considerations

The implementation of AI in education will unavoidably lead us to face some challenges in different dimensions: technological, pedagogical, administrative, social, and ethical. AI can help to alleviate the load on educational administrators. It can quickly complete many routine tasks, such as answering student questions, providing school information, and solving budget problems, which would otherwise require significant amounts of administrative time. Routine administrative operations, such as tasks of accrediting and providing services to a countless number of students, the collection, organizing, managing, and retrieving data from students, can take up an enormous amount of administrative time. It is also common at this level of responsibility in educational organizations that executives have to provide support for specific school projects, manage the institutional network, becoming the pillars on which all of the teaching and learning structures rely on.

4. Case Studies and Success Stories

There are a few AI systems that have been successful in achieving their goals. Most of these systems included adaptive instructional systems designed for tutoring students with an intelligent system. The large part of research in this domain includes tutoring model, curriculum sequencing model, and natural science domain model which is led by Sleeman and Brown who built SiF and SiWA (SIs and WheAtstones) in 1982 and 1983, respectively. Several other systems have been developed based on the early works of Sleeman and Brown (1982). Most tutoring systems developed in the 1980s implemented models of intelligent tutoring systems originally proposed by Gerald in the 1970s. From the available historical reports, only examiners and SES/TWF existed in the later into the early 1990s.

The AI projects developed in the 1990s implemented adaptive learning environments which are based in part on Knight’s CIRCSIM-TUTOR and Brown’s SOPHIE and Steve’s LDP, but there are no projects documented in the AIED research that implemented expert-assisted curriculum sequencing. Nearly all projects in both the 1980s and 1990s relied heavily on human programmers for design and function development and were based on MIT list processing or LISP and PROLOG. Educational programs supported only intellectually gifted learners, students who were already at the advanced undergraduate level in college. For example, the concept used in MILO in the 1980s was designed to support G&L learners in MIT. These kinds of projects utilized computer graphics to simulate an intelligent computer-based system for these learners.

5. Future Directions and Implications

Given the relatively early stage of the leveraging of AI in teaching and learning processes, teachers’ willingness to understand, learn about, and be open to the inclusion of new tools is crucial. Initial training and ongoing professional development can serve to break down resistance towards the inclusion of AI, achieving better results through new pedagogical methodologies and the optimization of teaching and learning processes. The importance of training in AI for teachers must be underlined, since this skill will allow them to pass on both knowledge and attitudes towards the technology, completing a market that is constantly looking for qualified staff. Moreover, the ideal would also be to train the final students, promoting integrated training in order to prepare them for a future labor market based on digital skills.

In this context, schools, universities, and other institutions are advised to set up ad hoc training programs. In the same way, teachers’ associations and unions, self-use societies, research centers, and universities can play an important role in promoting training activities for teachers through seminars, workshops, and online courses – widening the range of opportunities available. Likewise, employers’ associations, companies, and public authorities can be key players in the process, contributing to setting up partnerships with the education system and public bodies responsible for education.