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The Role of Artificial Intelligence in Education: A Comprehensive Study

1. Introduction to Artificial Intelligence in Education

1. Introduction

The development of information and communication technologies has introduced new possibilities into the world of education. One of the most interesting applications of these technologies arises from the use of tools based on artificial intelligence to develop a broad spectrum of educational software. These tools can be classified as intelligent tutoring systems, expert systems, learning games, simulation programs, and other types of learning software. This paper presents an overview of the different types of artificial intelligence software available for education, describes the adaptation and modeling processes that take place when these technologies are applied in educational settings, and discusses the strengths and weaknesses of some of the intelligent tools now available. The paper then presents a meta-analysis developed to evaluate the effectiveness of intelligent tutoring systems. The implications for future research and teaching practice and the most promising instructional applications involving the use of artificial intelligence to enhance the teaching-learning process are also discussed.

In the authors’ opinion, the most beneficial aspect of artificial intelligence is the possibility it gives students to experience and resolve real problems, and to make decisions in problem-based learning environments. This, in turn, alters the teaching/learning process, providing an environment where teachers assume the role of guides and experts and students are able to build and manage their own knowledge. They are also able to discuss their beliefs and strategies, developing and reinforcing habits of critical and creative thinking and of learning to learn. The growth of an environment that is collaborative, flexible, and open for debate and for interaction based on the simulation of real situations, similar to those of real life, motivates and challenges students. This develops a higher level of engagement and satisfaction, as well as a better quantitative and qualitative learning.

2. Benefits and Challenges of AI Homework Helpers

2.1. Speed of Feedback

2.1.1. Speed and quantity of homework feedback

The amount of homework that can be graded, as well as the speed at which it can be returned, is a common challenge for teachers. Through the use of AI homework assistants, students can receive instantaneous feedback for a vast amount of data. The main advantage of homework feedback is related to whether a student receives a binary response, pass/no pass. The instant message is of utility, and in K-12 education it holds additional social weight. The longer students have to wait to find out what grade they have been awarded on their summative assessments, the greater the investigation of the course of action related to the grade. Therefore, instant feedback for students is likely to be at least partially rewarding to them, even if it is fully automated feedback. Such interaction is likely to offset the lower quality and the poor learning outcomes of the generic prompts of the automated feedback.

2.1.2. Classroom Capacity

The capacity of the classroom might be increased by the increased speed and quantity of homework feedback. In order to allow extra time for review or testing and allow flexibility in scheduling makeup exams, a teacher might be inclined to require the completion of homework assignments during the last part of a regular class meeting. An AI-driven system allows a teacher to efficiently grade assignments in real-time, thus freeing the teacher to focus on other tasks, as the students complete the homework while in the classroom.

3. Ethical Considerations in AI Education Technologies

The impact of AI-delivered educational technologies is not confined to learning outcomes, but it expands to influence various stakeholders such as parents, school faculty, and other influential parties associated with decision-making. Based on a review of the literature, we structured the paper in such a way to answer the central research question: What are the implications of the application of AI in education in terms of ethics and equity among the stakeholders? To answer the research question, the paper explores the potential of integrating education with AI, and the benefits and concerns of stakeholder groups are considered within an ethical and equity framework. The paper contributes to the AI and ethics literature through exploring the bountiful aspects of educational technologies and outlining the risks that need to be assessed, relevant policies required, and guidelines to ensure AI enhancement in education are equitable.

The paper is motivated by an abundant array of technologies assimilated in schools from smart wearable technologies that monitor students, personalized learning programs, to ed-tech software and learning management valued at 8 billion annually. Further, a deeper motivation is the fast emergence of AI in education, with the sector expected to become a 6 billion dollar industry by 2024, as well as the potential tectonic shift it brings to education. Ethical considerations and principled reasoning have become critical topics in current debates about AI in education, especially with the bias and inequality issues associated with AI and the increasing general societal distrust of historically opaque systems. The absence of ethics guidelines relevant to AI is overshadowing the potential of AI in Education (AIED).

4. Case Studies and Success Stories in Implementing AI Homework Helpers

Instructors often face the challenge of addressing the diversity of prior knowledge in their classrooms. In addition, instructors often struggle to provide accurate feedback to students on their assignments. This case study presents how a homework help platform, enhanced with AI technologies, was implemented and used in a higher education course. Implementation of the AI homework helper was successful in reducing student frustration related to homework, increasing instructor and student satisfaction, and decreasing instructor workload. It was found that students who used the AI homework helper performed as well or better than when they did not use it. The case study presented here provides encouraging results for the successful implementation of an AI tutoring system as part of an undergraduate course.

An integral part of the educational process is the evaluation of students’ knowledge. A survey conducted at Indian universities showed that the workload for teachers is quite large and one of the most time-consuming is the verification of the received examinations. Therefore, to speed up this process and reduce the additional cost of the teacher, in this study, we offer to automate the process of verification of students’ knowledge. An empirical study conducted exam verification that involved the Amrita DataFlairTeacherF as one of the arbiters. The use of the web-based system Amrita DataFlairTeacherF for the exam verification process may be economically justified when the number of students participating in the exam is over 50. Due to the use of this system, a university lecturer can organize the verification process both on-site and on remote workplaces. The lecturer does not need to be present at his/her workplace in order to set up the verification process and monitor the efficiency of the examining activities.

5. Future Trends and Implications for Education

Having analyzed the potential that technology, and more specifically AI-based technology, can offer regarding helping to achieve significant improvements in education, and the key themes that are emerging in the research related to this area, it is important to understand what the future may hold in this domain. As technology continues to develop, with specific trends in AI accelerating the introduction of its tools and solutions in the area, in a session held with a group of leading experts in this domain, organized by the authors, the various technological options and the effects that these will bring in terms of the acceleration of the use of AI tools in education were analyzed. It was possible to build a perspective that faces up to the challenges interdisciplinary and interinstitutional advanced education environments may have in a pervasive way, caused by the new AI and smart technologies, and a future that on the one hand places different requirements on the artificial intelligent agents to be included, and on the other hand, such requirements concern the smart education spaces where such AI tools will progressively become active elements.

This chapter, building on the inspiring insights that emerged from the discussions held, dwells in the future of AI in education, considering the available paths and the main implications for the actors, the educational models, and the smart spaces involved. It is divided into four sections: the first of these, after a brief reference to the desired educational paradigm, offers a summary on the development of AI in education models, analyzing the progress that this field made using some indicators that resulted from the group discussions. The second section looks at the main implications to be faced, such as those related to teaching and learning, but also the changes that may affect the educational models, the evaluation, the recommendation systems, the agents involved, and the smart spaces. The third section is dedicated to some important requisites that need to be faced to make possible the future that was analyzed and described in the previous section. Finally, the fourth section summarizes the main findings from the chapter and outlines the relevant guidelines for further research in this field.