Artificial intelligence (AI) has both benefits and challenges in education. AI can customize learning experiences, enhance teaching methods, and foster equal opportunities in education. However, ethical implications and potential biases linked to AI-generated data are concerns. AI-driven tools like adaptive learning systems and intelligent tutoring systems have the potential to revolutionize education. The chapter explores a range of AI-driven tools and systems, including natural language processing. The ethical implications and potential biases linked to AI-generated data are thoroughly analyzed. Suggestions on how to responsibly incorporate AI into the field of education are offered. The utilization of AI has been discovered to enhance learning experiences and address the disparity in educational opportunities, ultimately resulting in a more inclusive and equitable education system. The development of AI curricula for various educational levels is also a key area of focus to ensure that the potential of AI in education is realized in an inclusive and responsible manner.
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The emergence of Artificial Intelligence (AI) has brought about a new era of progress and change in various industries, particularly in the field of education. With the advent of AI technologies, there are now novel tools and applications that can revolutionize the conventional methods of teaching and learning. The potential uses of AI in education (AIEd) are vast and varied, ranging from enhancing productivity and learning outcomes to providing personalized instruction, immediate feedback, and increased student engagement (Adiguzel, Kaya, & Cansu, 2023). Technology such as artificial intelligence (AI) has been gaining popularity in education. For almost thirty years since its introduction in education, AI has been recognized as a valuable resource for creating innovative approaches to instructional design, technological advancement, and educational research, which would not have been possible in the conventional education model (Ouyang & Jiao, 2021; K. Zhang & Aslan, 2021). AI innovation in the field of education has progressed from theoretical laboratory settings to practical learning environments that are more intricate. Within the educational technology (EdTech) sector, companies have created various systems to enhance the learning experience. These include the Individual Adaptive Learning System, which enables personalized learning, the Aided Teaching System, which assists in managing classroom dynamics, grading, evaluation, and addressing second-language difficulties, and the Institute Administration System, which aids in student enrollment and handling inquiries, among other functions (Guan, Mou, & Jiang, 2020). Over the years, it has been proven through numerous studies that utilizing artificial intelligence (AI) techniques in a manner that aligns with human learning methods results in better performance compared to conventional computer-assisted instructional systems (Lane & D’Mello, 2019). An AI-assisted learning environment for precision education is anticipated to offer teachers and students more responsive and consistent feedback on their learning progress, thereby enhancing the quality of problem-based learning (PBL) classrooms. One such application of AI is the automatic recording of student behavior and identification of individual learners (Hu, 2022).
The implementation of AI in schools heavily relies on the teachers who act as the intermediaries between the school's AI policies and the student's requirements. Therefore, teachers play a crucial role in the successful deployment of AI in educational institutions. Despite being aware of the potential benefits of AI in education, many teachers may not be fully prepared for AI-enhanced teaching. This lack of readiness may be one of the reasons for the slow and unsatisfactory adoption of AI technologies in education, which lags behind the rapid advancements in AI (X. Wang, Li, Tan, Yang, & Lei, 2023). The authors (George & Wooden, 2023) stated that AI-driven data management systems can collect and analyze student performance data, enabling the identification of areas where students might encounter difficulties. By examining grades and test scores, these systems can precisely determine the courses in which students require additional support. Consequently, the university can offer personalized assistance to these students, addressing their specific needs and enhancing their chances of success.
Bridging the knowledge gap in utilizing AI technologies for students with special needs is crucial in advancing inclusive education. By addressing the digital divide and guaranteeing equal access to AI resources, educators can maximize the benefits of cutting-edge technologies to improve the educational journey of special needs learners. To accomplish this, it is crucial to prioritize comprehensive training for educators, enhance the accessibility and affordability of AI resources, and adapt these technologies to meet the unique requirements of students in special education. By closing the gap in knowledge and access to technology, we can empower special needs learners with the necessary assistance and opportunities to flourish in the era driven by artificial intelligence (Mpu, 2023). To further the discussion on the most effective method of incorporating AI education, this chapter delves into exploring its potential across various domains as shown in Figure 1.