India’s education sector, with more than 265 million school-going kids and 38 million tertiary-level students, is being transformed by digitalization. Artificial Intelligence sub-streams ML and DL, transforming learning delivery, access, and personalization, are the catalysts of this change. Since the nation wants to transform into a knowledge economy on the world stage, the use of ML and DL is a highly worthful tool to solve systemic issues like unbalanced access, low retention rates, teacher shortages, and outdated pedagogical techniques.
One of the most revolutionary uses of ML and DL for Indian education would most certainly be personalized learning. Indian classroom pedagogy is normally one-size-fits-all, without regard to diversity of learning style or learning pace. These technologies have utilized ML algorithms to understand the learning behavior, strengths, and weaknesses of students with an aim to providing learner-centered learning pathways. BYJU’S, for instance, with over 150 million registered learners, utilizes neural networks and reinforcement learning to modify adaptively the levels of content difficulty based on feedback from students. KPMG conducted a survey in 2021, which again revealed that AI-driven personalized learning can raise education levels to up to 32% in math and science.
Along with this, ML/DL solutions are also used for intelligent tutoring systems and adaptive testing. Embibe and Cuemath, for instance, use deep learning algorithms that dynamically alter the tests based on the students’ responses and give accurate feedback. Such systems typically use Natural Language Processing (NLP) for evaluation of written responses, giving feedback on correctness as well as reasoning. ITS can also act like virtual tutors, a very essential function in rural and disadvantaged regions where the teacher-pupil ratio is greater than 1:50. Such AI tutors are round-the-clock and are found to effectively narrow down learning gaps generated due to regional disparities.
The other important application of ML/DL in Indian education is predictive analytics. IIT Bombay and IIIT Hyderabad, for instance, have employed AI models that forecast students’ performance based on attendance, assignment grades, and socio-economic status. Predictive systems alert teachers to act early into at-risk students. Predictive analytics have, as per a 2023 report of NITI Aayog, also lowered the rate of dropouts by 20% in pilot projects in the case of some state government schools in the states of Tamil Nadu and Maharashtra. Predictive analytics are also assisting with admissions in colleges via forecasts of academic performance and expediting screening of candidates via the use of AI-driven scoring mechanisms.
Deep learning is also a key enabler of autonomous content generation and translation in content creation. India is a multilingual nation with 22 official languages and several dialects. Multilingual translation-trained DL models and NLG are connecting language gaps by real-time translating education content. Government-sponsored National Digital Education Architecture (NDEAR) uses DL-powed engines to translate English-centric curricula into local languages and hence democratize access. Uttar Pradesh and Assam, local language textbooks and audio-visual content with the help of AI have seen that there is an increase in students’ level of comprehension by 12%, predominantly primary-grade students.
Machine learning is also giving administrative efficiency and teacher training a boost. The AI school administration software allows automating of timetabling, attendance, and grading. For instance, the DIKSHA learning platform funded by the government uses ML for detection of patterns in pedagogy and recommending personalized courses to educators. More than 3 million educators have used the AI components on DIKSHA since its deployment. Beyond learning tracking, the components also recommend performance parameter-based competency development. AI teacher training has been proven to boost pedagogical capacity by 28%, according to a 2022 UNESCO report.
One of the more recent and promising fields is the use of voice recognition and computer vision algorithms on special children. Thinkerbell Labs developed AI-powered software that converts text into Braille and offers voice-based learning experiences. AI technologies also interpret sign language, monitor eye movement to measure cognition in numbers, and record speech as text with close hundred percent accuracy—education made easier. The government’s Accessible India Campaign (Sugamya Bharat Abhiyan) has already begun deploying these AI technologies in model inclusive schools of the country.
ML and DL also have significant applications in examination invigilation and plagiarism checks. With online education and distant learning finding increasing popularity—particularly in the wake of COVID-19—safe test-taking measures are needed more than ever. AI-based proctoring tools employ facial detection, keyboard behavior, and voice recognition to authenticate tests. Indian educational institutions like Delhi University and JNU have used AI-powered examination solutions like Mettl and Talview, resulting in a 40% reduction of malpractice cases through online examinations.
Though promising, multiple barriers prevent AI and DL from going mainstream in Indian education. To begin with, the digital divide continues: 47% of rural Indian households have internet access, the 2022 National Sample Survey reports. Secondly, low digital competence among students and teachers can be an obstacle to proper utilization of AI-based software. Thirdly, AI-based applications have the potential to become skewed and increase socio-economic inequalities if properly developed and filtered.
There are also matters of ethics and privacy. AI programs will need to have access to gigantic amounts of student information, including biometric information, behavioral information, and personal information. Without strict data governance laws—India’s Digital Personal Data Protection Act still awaits consideration as a bill—there is the risk of misuse or data breach. AI adoption in education will need mechanisms of transparency, consent, and accountability.
For bridging such gaps, a public-private-academic partnership model is needed. EdTech startups, schools, and government ministries together can fuel scalable AI ecosystems. The Indian government’s National Education Policy (NEP) 2020 has laid out in categorical terms the incorporation of AI as a subject and its inclusion in school and higher education course syllabuses. Besides the PM eVidya program, AI-driven digital platforms are being developed to support country-level digital learning.
Additionally, AI literacy education and educator/administrator training courses must be mainstreamed. NASSCOM and AICTE have pilot programs for teacher upskilling in AI ethics, data interpretation, and pedagogics of teaching. If appropriately scaled, these can redirect the teacher cadre to technologically educated agents of AI-facilitated learning.
In brief, the revolution wrought by Machine Learning and Deep Learning in India’s education is long overdue. From customized learner paths and more institution-wide influence to leveraging inclusive learning and evidence-driven policy-making, ML and DL are remapping the edtech playbook. There are front-end issues around infrastructure, equity, and ethics, but considered and inclusive action can realize the full potential of the technologies. As India attempts to emerge as an international knowledge hub, the integration of smart technologies into the education system would be the key driver for innovation, equity, and lifelong learning
Prepared by
Jagadish Sripelli,
Assistant Professor, School of Computer Science and Artificial Intelligence,
SR UNIVERSITY, Warangal
jagadish.sripelli@gmail.com
