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Challenges in adopting Artificial Intelligence and Machine Learning - B-AIM PICK selects

Artificial Intelligence thrives on data. In recent times AI has been instrumental in creating new teaching and learning solutions that are now undergoing testing in different contexts. The beauty of any AI application is that it becomes more accurate when there is more accurate data available. Any AI application uses massive amount of data, and then the intelligence is built on it.

One very interesting aspect of the education sector is that the data that is available is accurate and also is built year on year. This happens both in the school ecosystem and also in the undergraduate and postgraduate education.Given that big data enables AI to reach its full potential, it would be fair to say that there is no data-driven AI without big data. This big data environment can be built at the country level if there is an infrastructure that is created by the government, which can give immense insights into the scenario of education in the country.

As on date, two areas have evolved in AI and ML with a special focus on education. They are learning analytics (LA) and educational data mining (EDM). Both of them overlap each other in terms of objectives and techniques. While EDM methods are drawn from diverse disciplines, including data mining, machine learning, psychometrics of statistics, information visualisation, and computational modeling, LA is more focused on learning content management systems and large-scale test results. LA combines institutional data, statistical analysis, and predictive modeling to identify which learners need help and how instructors can change academic behaviour.

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