‘Artificial Intelligence can adapt to the learning patterns of each student’
Artificial Intelligence (AI) as an idea seems to have caught the imagination of both industry and academia alike. Although AI related academic research has been in place since late nineties but it is recently that products and services inspired by AI have emerged out of labs into our daily routine activities. Whether it is the buzz around autonomic vehicles, drones, speech recognition, various voice response systems like Alexa and Google assistant, every single one of these products have some form of AI at its core.
Undoubtedly, ever increasing processing speeds and storage capacities along with possibilities of machine to machine (M2M) communication have set the cat out of the bag. Today we produce more data in a single day then possibly we did in the entire year in eighties. It did not take long for the industry stalwarts to realize that they were not storing and analyzing a lot of data that could help in offering completely new and customized services, thereby bringing completely new revenue streams in existence. Now with every vertical across industry engaged in understanding exactly how AI and analytics could help them transform, education as an industry too followed suite. This article aims to differentiate the achievable from the hype in this arena.
Various scholarly and popular articles over years have identified domains within higher education where AI and analytics together could reshape the future. Let us break this down into in-classroom and learning oriented areas as well as beyond classroom processes. For learning oriented and in-classroom processes, traditional classrooms have often been blamed to be unresponsive and mass delivery focused. In other words it does not enable personalization of educational experience (this needs to be interpreted as way beyond anytime access to material which is easily available today). Every participant irrespective of backgrounds might have different learning needs and more importantly learning may happen at a varied pace.
AI based algorithms could help assess before hand the kind of learning set-up and speed that would be suitable for a specific participant. Subsequently, a blended, adaptive learning environment could be implemented wherein the coursework could move with appropriate video lectures, forums and specific teaching assistance as is required by the participant. The most obvious impact of AI and analytics could be felt in evaluation methods.
AI could help the participant in identifying the level of understanding reached after relevant sections by exposing students to adaptive evaluation, at times leading to participant going back to the sections where learning is identified as inadequate. AI could also help in assessing the parts of coursework where greater human intervention may be required for higher learning impact.
Based on the learning speed and interaction of student with the course material, final customized evaluation could be conducted, thereby establishing higher engagement among students as well as ensuring better learning outcomes. In this regard the role of IBM’s cognitive computing platform Watson based teaching assistant in a master’s level course at Georgia Tech is especially noteworthy. Students were so impressed by the teacherbot that they nominated it as the best teaching assistant.
AI could also help students in engaging in specific social groups by identifying groups among the learning community that aid in learning of each other and further help the instructors in identifying most effective learning content amongst the myriad of choices. Lastly, the instructors could utilize AI to assess even the most complex answer sheets in a much more objective way as compared to today.
Among the beyond classroom activities AI could help the higher learning institutions setup effective career counseling sections. AI and analytics could help the students identify various domains wherein they could do better or hep them map learning trajectories to be followed for specific career choices. Among more efficiency based aspects AI and data analytics could be used to answer various admission related queries thereby bringing down need more manpower, identifying parking spaces, lighting and facility maintenance using data from Internet of Things (IoT).
Deakin University in Australia is a case in point as they have developed a 24/7 query response system for students that brings together over 90 servers and 200 million pages of data with the aim of cost saving and better service. Clearly, higher education world of tomorrow would be a lot more automated, more personalized and more impactful in its ability to enhance the learning of the participants.
However, a word of caution is to be put in place. We have still not reached a point where a teacher or the classroom learning could be completely avoided or made obsolete. A teacher in a classroom is here to stay for a very long time given the complexities and subtleties of human to human interaction based on real time behaviour of the teacher and student alike. Nevertheless, there is a greater need to study and understand the newer and emergent paradigms of human machine interface that may impact the pedagogical methods for higher learning in a futuristic world.
(The author is Assistant Professor, Information Management Area, MDI Gurgaon)