Artificial Intelligence has become omnipresent and made inroads in all walks of life including education where AI is gathering a momentum to ease and aid the teachers and educational institutions as it shows immense potential, such as in administration, adaptive & personalised learning, reasoning, problemsolving, tutoring, grading, using language assessments and predicting the requirements of every student differently.
This article specifically analyses how Artificial Intelligence can be used to improve learning outcomes in India, presenting cases of different countries on how AI technology helped the education system, using data to improve educational equity and quality. In short, all the possibilities of AI in New Education Policy (NEP) 2020, Starting from early childhood education to professional level education.
Though AI may not replace the job of teachers but its combine use with other educational technologies as well as the teachers would give optimum support to the education system. So, the quality of education which NEP is talking about can be made available for all students.
NEW EDUCATION POLICY 2020
The New Education Policy 2020 is said to be the first education policy of the 21st century and it aims to address the many growing educational imperatives of India. The main focus of NEP 2020 is to improve the quality of education, curriculum and roping in new technologies while keeping India’s traditions and value systems intact. It ensures comprehensive and equitable quality education and to promote lifelong learning opportunities for all. And keeping all the abovementioned objectives, AI could become a promising technology to transform the education system of India.
Equitable and Inclusive Education
Every citizen must have the opportunity to dream, thrive, and contribute to the nation.
Marginalised people and communities, speciallyabled people, refugees, dropouts, and those living in isolated communities can access proper learning opportunities with the use of AI technologies. For example, telepresence robotics allow students with special needs to attend schools at home or maintain continuity of learning in emergencies or crises. In this way, it can support inclusion and ubiquitous access.
Personalise Learning & Predictive quality
The ultimate purpose of developing AI in Education is not to replace teachers but make teachers smarter.
Currently, teachers and an educational institution cannot be able to provide individual attention to every student. However, when content is created and graded by AI, it would ensure personalized paths of learning for the children by identifying weak points for the students and providing recommendations accordingly. Essentially, an educational set-up driven by AI would give each student a personalized tutor. Personalized tutors continue to collect data points at each juncture in the child’s education journey, classification Machine Learning models could be used to predict the children at risk of dropping out and proper redressal mechanisms can be put in place. A culmination of these activities would help a higher education enrolment ratio and make sure a substantial proportion of adults in India achieve literacy, mandates in line with targets under the NEP.
Also, Data gathered throughout the learning process may be used to power AI engines to discover better content, identify patterns of what is considered relevant by the user and transfer appropriate content to the correct individual at the appropriate time, to enable highly efficient and personalized learning plans.
Another way in which AI can help in personalised learning is that AI can take over the teacher’s routine and administrative tasks such as making assignments and answering frequently asked questions over and over again in school in which teachers spend their plenty of time. Taking over these tasks by AI will enable teachers to bring in more human capabilities such as mentorship, emotional support, interpersonal skills, to work more on students with difficulties and one-on-one communication with student etc. This is how personalisation and better learning outcomes can be promoted by using AI technologies.
CASE STUDIES
Hujiang, a private company working on digital education, developed an image and voice recognition software which is capable of understanding student facial expressions to give AI feedback online.
Liulishuo, also known as LAIX is an education company that teaches English to 600,000 students at the cost of a single teacher. It uses AI to create virtual educators.
Master Learner developed a “Superteacher” capable of answering 500 million real-time questions asked by students preparing for the Gaokao university entrance examination.
In 2016, China’s Ministry of Education established that every educational branch of local governments must allocate at least 8% of its budget to the digitisation of education. With 95% of schools connected to the internet, China is ready for the largest digital education experiment in the world. The country started to work more than 50,000 schools for automatic essay correction with a level of accuracy matching humans in 92% of the cases.
Mapping of School
Data mentioned under NEP shows that certain geographical locations require special interventions for promoting their educational development, Hence, it is advisable that the school mapping initiative to be introduced by the government so that all the schemes and policies of the government can be easily implemented to the maximum through additional concerted efforts, to truly change their educational landscape.
Also, the isolated location of small schools’ harms education and the teachinglearning process as teachers function best in communities and teams, and so do students. Small schools also cause systemic challenge for administration and management by the government. The geographical dispersion, challenging access conditions, poor academic results and dropout predictions Thus, AI can help government to recognise schools in satellite imagery, thus rendering unmapped schools visible.
More examples
AI has begun opening up its potential in research for sustainable development. The Inter-American Development Bank organised a contest “New debates, Data for development” and financed the study “Big Data for public policy in education: the Chilean case”. This study talked about how Chilean researchers used open data published by the government regarding social, geographical and educational contexts. The study was able to predict student dropout by localising the geographical distances from houses to schools. By using 127 characteristics of students and their locations, an algorithm was created by researchers which develop a “geography of educational opportunities”, with a detailed map of schools, access, dropout predictions and academic results.
Coding
Coding in educational curricula meant “embedding thinking skills – not IT skills.
Introduction of Coding in the middle stage of learning by NEP. This underlines that it´s not enough for teachers to have certain skills to manage digital technologies and to teach them to their students, but also that teachers must help their students to be capable of collaborating, doing mathematical and computational thinking and being creative in the use of digital technologies. In a growing technological world, these skills are very important for India’s future and leadership role in the numerous upcoming fields and professions that will involve artificial intelligence, machine learning, and data science, etc.
In 2018, Twenty-two schools in Malaysia have been selected as #mydigitalmaker Champion Schools, i.e. schools funded by MDEC to implement the #mydigitalmaker framework, including the establishment of a Digital Maker Hub, which is a key feature of the #mydigitalmaker Movement (My Digital Maker, 2018). A Digital Maker Hub functions as a workshop or laboratory with a structured learning programme, whereby students have access to various tools to create and collaborate on tech projects (Malaysia Digital Economy Corporation Ministry of Education, 2017). All Digital Maker Hubs contain a ‘creative lab’ where students can convert their ideas into code in any programming language, and a prototyping studio, where they can test and see their products at work.
Promoting Multilingualism
It is a well-known fact that young children can learn and grasp non-trivial concepts more quickly in their home language/mother tongue.
AI disguised as Real-time text to speech and text translation systems can be used to disseminate information seamlessly in the regional language, in line with the National Education Policy 2020 that has encouraged learning of mother-tongue languages.
ETHICAL AND ADMINISTRATIVE CHALLENGES & RECOMMENDATIONS
The education sector in India has opened and will continue to open multiple avenues of AI intervention. It could be a difficult road for the nation to achieve the targets under the NEP 2020 on Quality Education without harnessing this superpower, often believed to be the new revolution for the 21st century.
If we are headed towards the datafication of education, the quality of data should be the chief concern. It´s essential to develop a framework on state capabilities to improve collection and systematisation of data. Here, AI developments should be considered as an opportunity to increase the importance of data in educational system management and to develop quality and inclusive data systems.
Ethics and transparency in the collection, use and dissemination of data opens up many ethical concerns about access to the education system, one-on-one recommendation to students, the concentration of personal data, liability, data privacy and ownership of data feeding algorithms. AI regulation will thus require public awareness on ethics, accountability, transparency and security.
Recommendations
The state must create partnerships with the private sector to enlarge the AI ecosystem because the public sector will not be able to innovate at such a complex technological level alone.
Private companies like SkoolDesk (Uganda), Siyavula (South Africa and Nigeria), Virtual Learning Africa and TopDog (South Africa) that develop educational content for students of all levels in Africa.
People are also concerned with the ethical consequences of AI in education. Therefore, new regulations to be introduced to secure the use of AI by private companies in terms of data use, privacy and the transparency on how algorithms are designed. To address ethical issues, it´s essential to consult experts and form teams to create blueprints and roadmaps in the uncertainty of the near future. Data privacy and security almost immediately come up in discussions regarding data ethics. The main challenge lies in being able to use personal data while ensuring that personally identifiable information and individual privacy preferences are protected. Installing necessary safeguards to prevent data theft is also critical. In education, this becomes even more challenging in the context of young learners, who, in legal terms, cannot yet provide express consent regarding the collection and use of their data.
To conclude, Good networks, as well as continuous and reliable connectivity, need to be provided even in remote areas of the country for seamless online and digital education and to make NEP not good only on paper but in reality.