WHY INDIA TRAILS BEHIND IN ADOPTING AI FOR EDUCATION?
In our quest for a digital transformation across every sector, education is one of the vital segment that needs a massive technological upgrade. Artificial Intelligence is slowly altering education tools and institutions and changing what the future might look like in education. AI along with its subset Machine Learning brings many benefits to this field. Therefore, it’s no wonder that educational institutions are racing to harvest available AI features to enhance the talent of young minds by helping them achieve their learning objectives.
With the help of AI, the education industry has undergone some significant improvements like providing online classes, the inclusion of online modules, proctored online assessments, virtual mentoring, and boosted teacher-student engagement with the option of communicating with lecturers after hours. All these changes occurred due to the training of the computer to carry out such complicated tasks. And for training any model, whether it is for AI or ML applications, we need data. The more accurate and abundant data we have, the better is the AI system. This is where the education sector has been proving helpful in providing access to such data. This occurs at school, high school, undergraduate, and postgraduate levels of education. So, in retrospect, big data software and tools are also improving education; without it, innovations in AI is not possible. 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.
In India, the Central Board of Secondary Education (CBSE), is trying to reshape their education system to make students well-versed with today’s fast-growing and highly demanding technologies, by including artificial intelligence in their syllabus for students. Unfortunately, our education system is already trailing behind due to inefficiencies that deter student learning. Moreover, only a mere 13 percent of the startups that use AI resources for their products are catering to the education sector. In terms of data retrieval, India faces two major loopholes.
The National Academic Depository (NAD) was launched in 2016 by the government of India. The main aim was to provide an online store that can hold all academic awards like certificates, diplomas, degrees, mark-sheets in a thoroughly digitalized format by academic institutions or boards or eligibility assessment bodies. Yet it still incomplete with working in progress due to no deadline set by NAD.
Another issue is the lack of internal data at the grass-root level. Many educational institutions still don’t have an ERP (Electronic Resource Planning) implemented, and hence, there is no central repository that collects data for further intelligence to be drawn from it. These issues can be huge roadblocks in learning analytics (LA) and educational data mining (EDM), which are critical to the development and implementation of AI and ML tools in education.
Additional obstacles in AI-based education in India are lack of good internet connectivity and limited infrastructure in the rural areas where the majority of the students hail from—thereby showcasing a vast digital divide in India in terms of education and learning.
Meanwhile, another question pops up in mind, i.e. if the teachers and instructors are equipped to utilize the available resources like simple tools and software like Microsoft Office, or learning analytics to find which student must be given focus based on the past and present academic records.
Therefore, in conclusion, numerous challenges need to be tackled before we approach for an AI and Machine Learning based education system in India. While countries like China have witnessed impressive outcomes of amalgamating technology with an innovative learning process that has truly revolutionized education systems across the nation, much needs to be done back at home. Fortunately, the education pioneers of the country have vowed to take the national education system at par with the global level under the 2018 National Strategy for AI: #AIforALL. Further, they must create policy measures to train teachers and incentivize universities to adopt credit-bearing MOOC ((Massive Open Online Courses) in their curriculum, which is related to AI. But before any of this, it is important the work to eliminate barriers in data collection for a detailed and accurate AI framework.