Artificial Intelligence (AI) has been transforming various industries, and Education is no exception. AI is revolutionising the traditional approach to learning and teaching, and its potential impact on the future of Education is immense. In this article, we will explore how AI is changing the education landscape and this transformation's potential benefits and challenges. Personalized Learning: Artificial intelligence can enable personalised learning by creating customised plans based on individual needs and preferences. With the help of AI, educators can track a student's progress, strengths, and weaknesses and create a personalised curriculum that addresses their learning needs.

September 8 has been celebrated as the'International Literacy Day' across the world since 1967. The significance of this day arises from the fact that despite the steady rise in literacy rates over the past 50 years, there are still 773 million illiterate adults around the world. In India, though the literacy rate has seen phenomenal growth--from 18.3% to 74.4% between 1951 and 2018--there are 313 million illiterate people, according to the study, "Literacy in India: The gender and age dimension." Illiteracy and dropout rates are acutely linked. Dropping out of school is a rampant trend in India.

How is AI Being Used to Change Higher Education? Medical, financial, energy, and commerce industries are being revolutionized rapidly by artificial intelligence (AI). The use of AI technologies in Higher Education is particularly promising. In the coming years, artificial intelligence could have a huge impact on higher education. A new generation of innovations, such as virtual reality and other innovations, may be able to improve learning as well as lower costs for Generation Z and beyond. We will discuss in depth in this article how artificial intelligence can be used to make higher education a better experience for students and teachers alike. Also Read: How Technology Has Changed Teaching and Learning. It is clear why American universities are reliant on algorithms for selection models to manage enrollment by understanding the status of higher education as a whole.

Many higher-education institutions are now using data and analytics as an integral part of their processes. Whether the goal is to identify and better support pain points in the student journey, more efficiently allocate resources, or improve student and faculty experience, institutions are seeing the benefits of data-backed solutions. This article is a collaborative effort by Claudio Brasca, Nikhil Kaithwal, Charag Krishnan, Monatrice Lam, Jonathan Law, and Varun Marya, representing views from McKinsey's Public & Social Sector Practice. Those at the forefront of this trend are focusing on harnessing analytics to increase program personalization and flexibility, as well as to improve retention by identifying students at risk of dropping out and reaching out proactively with tailored interventions. Indeed, data science and machine learning may unlock significant value for universities by ensuring resources are targeted toward the highest-impact opportunities to improve access for more students, as well as student engagement and satisfaction.

Artificial intelligence offers new opportunities to improve university education. This is demonstrated by the Learning Intelligent System (LIS) project, which has been developed by researchers at the Universitat Oberta de Catalunya (UOC) with backing from the eLearning Innovation Center. The system was created by a transdisciplinary research team at the UOC and has already produced excellent results over the past year. It shows how an automatic system can be used to help students who are at risk of failing or dropping out to improve their academic performance. In 2021, a team from the UOC's Faculty of Computer Science, Multimedia and Telecommunications published a study in the International Journal of Educational Technology in Higher Education (ETHE) on the ability of LIS to successfully identify students at risk of failing a course.

Sinem Aslan, a research scientist at Intel who helped develop the technology, says the main objective is to improve one-on-one teaching sessions by allowing the teacher to react in real-time to each student's state of mind (nudging them in whatever direct An artificial intelligence (AI) software solution developed by Intel and Classroom Technologies to identify students' emotional states is generating controversy in the context of ethics and privacy. The technology, incorporated into Classroom Technologies' Class software product, can classify students' body language and facial expressions whenever digital classes are conducted through Zoom. The software inputs students' video streams into the AI engine alongside contextual, real-time data that enables it to identify students' level of comprehension of subject matter. Intel's Sinem Aslan said the main goal is to improve one-on-one teaching by allowing educators to respond in real time to each student's emotional state. Among the software's caveats is that the act of labeling emotional states into easy-to-grasp categories invites error.

When students in suburban Atlanta returned to school for in-person classes amid the pandemic, they were required to mask up, like in many places across the US. Yet in this 95,000-student district, officials took mask compliance a step further than most. Through a network of security cameras, officials harnessed artificial intelligence to identify students whose masks drooped below their noses. "If they say a picture is worth a thousand words, if I send you a piece of video – it's probably worth a million," said Paul Hildreth, the district's emergency operations coordinator. "You really can't deny, 'Oh yeah, that's me, I took my mask off.'"

A pilot study of the use of artificial intelligence to detect the needs of students in remote learning has concluded that the data obtained can be used by teachers to offer help to the students who most need it. The research, carried out in conjunction with the Universitat Oberta de Catalunya (UOC), the Eurecat technological center and the Universidad Autónoma de Madrid, will help solve one of the biggest problems faced by off-site education, which has become more widespread during the pandemic: how to obtain information concerning the progress of students in order to provide them with the necessary support before it is too late. Laia Subirats, a course instructor at the UOC's Faculty of Computer Science, Multimedia and Telecommunications and a researcher at Eurecat, said: "We were able to carry out a continuous assessment in pre-pandemic years, then during lockdown and later in the second wave of the pandemic." She added: "Our objective is to develop a method to improve remote learning which will allow teachers to identify students who are at risk of failing, so that they, as well as the students themselves, can reinforce their learning process." The study, published in the open-access scientific journal Applied Sciences, drew on information gathered from 396 university students between the 2016/2017 and 2020/2021 academic years. Before the final exam, students were given the chance to take tests featuring various questions adapted to their individual level.

Multi-task learning is an approach in which one model is asked to perform multiple tasks. "In our case, we wanted the model to be able to predict whether a student would answer each question on a test correctly, based on the student's behavior while playing an educational game called Crystal Island," says Jonathan Rowe, co-author of a paper on the work and a research scientist in North Carolina State University's Center for Educational Informatics (CEI). "The standard approach for solving this problem looks only at overall test score, viewing the test as one task," Rowe says. "In the context of our multi-task learning framework, the model has 17 tasks -- because the test has 17 questions." The researchers had gameplay and testing data from 181 students.