Integration of AI in Learning: A Paradigm Shift in Education

Introduction

All human actions are motivated by future expectations. We can't predict the future since it hasn't happened yet, but we can imagine and build it using what we know now. We will be better equipped to fathom future possibilities if we have a greater understanding of the present and the past that has shaped it. To properly comprehend the advantages and challenges of artificial intelligence (AI), we must first comprehend what AI is now and what the future may hold after AI is widely deployed in society. Academics, governments, and the general public have given AI a lot of attention in the twenty-first century. Learning, teaching, and education have the potential to enable new types of learning, teaching, and education, as well as reshape society in ways that will present educational institutions with new challenges. It has the potential to widen skill gaps and polarise employment, or it has the capacity to equalise learning opportunities. It may restructure or replace classrooms, improve instructional efficiency, or force students to conform to electronic demands, stripping humans of their agency and ability to take responsible action. Everything is conceivable. There are a lot of buzzes, and the subject isn't easy. It is, nevertheless, significant, fascinating, and well worth the effort.

Ethical, legal, and sociological concerns have surfaced in tandem with initiatives to integrate AI into business settings in order to maximise its utility. Much of the public, corporate, and academic discussion of AI has been on labour changes and displacement, military and cybersecurity concerns, ethical issues such as prejudice, and financial rewards. In the digital world we now live in, the rise of big data is transforming knowledge environments such as schools. By assisting students and teachers, artificial intelligence (AI) has the potential to change learning and teaching. Children's learning potential can be unlocked, and educational institutions' operational efficiency can be improved, by using advanced learning analytics and collecting data-driven insights on student behaviours, needs, and skills. Utopian reformers and dystopian cynics coexist as technology improves. This is especially true in the field of education, where the stakes are nothing less than civilization's survival. Either young people will be truly empowered to change the world, or they will be forced to work for cold, unfeeling authority (Feenberg and Hamilton 2012). Today's educational environments and theories attempt to integrate authentic activities in collaborative contexts by employing significant challenges. To remain relevant and influential, the field of artificial intelligence in education must adapt to these changes.

As technology advances, utopian reformers and dystopian cynics coexist. This is particularly true in the sphere of education, where the stakes are nothing less than the future of civilization. Young people will either be genuinely empowered to change the world or will be forced to work for cold, unfeeling authorities (Feenberg and Hamilton 2012). Through the use of enormous difficulties, today's educational environments and theories try to mix authentic activities in collaborative situations. The field of artificial intelligence in education must adapt to these shifts to maintain its relevance and influence.

Because current educational theories advocate for greater agency and personalization, these educational transformations are also an opportunity (Collins & Halverson, 2010). While no one can predict the future perfectly, this cycle suggests that looking at AI in Education through the lens of history is a good way to look at its potential future. One such antecedent for AI in Education is distance education or (digital/online) learning, which is not often AI-based. In the 1990s, along with the early Internet, distance education surged into popularity, promising to automate, streamline, and universalize education (Feenberg 2002; Li 2002).