The field of AIED, as defined by the work conducted under the auspices of the International Society of Artificial Intelligence in Education, has been built on big and well-intentioned ambitions to understand, devise and scale-up best learning and teaching practices to as many students as possible. This ambition has been bolstered most notably by the Bloom (1984) studies, which are still routinely cited throughout the AIED literature as a key justification and motivation for the field. This ambition had bootstrapped much of the work within the field and it has spurred in-depth research examining how specific populations of students learn, what are the prerequisites (cognitive, affective, and pedagogic) for successful learning, and how AIED technologies might be designed to help develop and capitalise on such learning prerequisites. Personalisation through adaptivity of assessment and feedback (for the purpose of this article used in the broad sense of pedagogical support) remains at the heart of the work conducted by AIED researchers, regardless of their specific areas of specialisation, or their philosophical or epistemological perspectives. This is why, to date, the AIED community repeatedly voted to retain its long-debated connection with the wider field of AI - a domain like AIED insofar as its central paradigm of adaptive agent technologies, but unlike AIED as far as its aim to emulate human capacities only to the extent that it is useful to a given application's success in achieving its specific goals.

Technology or A.I. is going to disrupt the overall education market. There are many use cases of A.I. to make learning accessible, derive better efficiencies, or even get better outcomes for learners. Its well known that teachers spend more than 50% of their time on evaluating homework, filing paperwork, grading tests, making progress reports, organizing materials and resources for classes, managing teaching materials, etc.

Artificial intelligence and machine learning are fast becoming a part of the educational sector. In the global educational market, AI is estimated to reach US$3.68 billion by 2023 at a CAGR of 47%. Teachers, students, and the administration teams will benefit from using AI in their institutions. Artificial intelligence will soon become a deciding factor in EdTech. Educational institutions will need to prove themselves by adopting the latest AI technology and providing the staff, teachers, and students with the best possible environment to work and learn.

Artificial intelligence is trying to make computer systems mimic the human brain. On the other hand, chatbots are infused programs on your websites and apps. Chatbots use Natural Language Processing to respond and detect a human voice. Just like evolution in humans, chatbots get smarter with time. They learn by analyzing past data and information.