Education in the post Covid 19 era: 

A few decades back we used to refer to the future scenarios in education with   phrase ‘21st Century education’.  I adopted the phrase “ Learning 321: education in the 3rd decade of the 21st century” to prepare for the changes that we could foresee in the coming decade.

But the Covid 19 has become a pivotal point in human history. The immediate future is being determined by two concurrent revolutions; ‘The 4th Industrial revolution’ to which attention was drawn by Prof Klaus Schwab at the World Economic Forum meeting in Davos in January 2016 and ‘The Fourth Education Revolution’ which is the title of a book ( in 2019) by Prof Anthony Seldon wherein he explores the most important issue facing education (and humanity at large): the fast approaching revolution in Artificial Intelligence or AI, to suggest that “ Nothing matters more than education if we are to see AI liberate, not infantilise humanity”. 

One of the very important experiences regarding education in the Covid era was the appreciation of the core activity of learning, being more important  than the form of organization structures and rituals ( like compulsory attendance). The widespread adoption of e-learning and multi-modal learning is the triumph of function over form.

Just a few years back, policy makers had started appreciating that 

the standard summary metric of education-based human capital used in macro analyses—the average number of years of schooling in a population—is based only on quantity. And ignoring schooling quality turns out to be a major omission. As recent research shows, students in different countries who have completed the same number of years of schooling often have vastly different learning outcomes. A new summary measure, Learning-Adjusted Years of Schooling (LAYS), that combines quantity and quality of schooling into a single easy to understand metric of progress seems to be a better choice. The cross-country comparisons produced by this measure are robust to different ways of adjusting for learning (for example, by using different international assessments or different summary learning indicators), and the assumptions and implications of LAYS are consistent with other evidence, including other approaches to quality adjustment. For the year 2018 data, India had a score of 5.8 compared to Japan with 12.3. 

While we may have differing views on what are the best metrics to measure the success of large educational systems, it is now agreed that in preparation for the knowledge economy, augmenting the cognitive capital of every individual is the path to be pursued. A few ‘islands of excellence’ are not adequate. This was articulated in the 1970s by Alvin Toffler in his profound statement that “ The illiterates of the 21st century will not be those who cannot read or write, but those who cannot learn, unlearn and relearn”. He was perhaps referring to the obsolescence of the ideas of Francis Bacon who had said “ Reading maketh a full man, writing an exact man and conversation a ready man”. For the economically inclined, the Stanford Professors Eric Hanushek and Ludger Woessmann in a 2015 book “ The Knowledge capital of nations “ carried out a  rigorous, pathbreaking analysis demonstrating that a country’s economic prosperity is directly related in the long run to the skills of its population. Of course, every country acknowledges the importance of developing human capital, but Hanushek and Woessmann argue that message has become distorted, with focus not on valued skills but on proxies for them. The problem is that the opposite of knowledge is not ignorance, but “the illusion of knowledge”.

A quantum jump in economic development can be only achieved by an all round proactive pole vaulting rather than a reactive leapfrogging ( to borrow the metaphor from RA Mashelkar) in the learning capabilities of all, not so much in the creation of a few isolated centres of excellence. This looks like a practical way to implement the Philosopher Voltaire’s exhortation that “ no problem can withstand the assault of sustained thinking”.

When the IITs were created in the country, in the 1960s there was a long and significant tradition of Engineering education from Guindy College of Engineering, Thomason College of engineering to Banaras Engineering College, but the IITs transformed engineering education fundamentally. There were many innovative features in the IIT system, and they benefitted from International partnerships with various countries, and several Institutions within those countries. 

During the first ten years of its existence, IIT Kanpur benefited from the Kanpur Indo-American Programme (KIAP), where a consortium of nine US universities ( Massachusetts Institute of Technology, California Institute of Technology, University of California, Berkeley, Princeton University, Carnegie Institute of Technology, University of Michigan, Ohio State University, Case Institute of Technology and Purdue University) helped set up the research laboratories and academic programmes. The first Director of the Institute was Prof. PK Kelkar who was a teacher–philosopher, able to see years ahead. He said that if engineering were the muscle for development, science was the brain. And that only with humanities could engineering education have a heart.

The IITs were created in the 1960s and their graduates were future ready having been transformed from slide-rule wielding engineers to engineers who were computer ready and suited to flourishing in the coming 3rd Industrial Age, which in Prof Klaus Schwab’s timeline began in the 1970s. 

Now that we are in the 4th Industrial Age driven by Artificial Intelligence and other emerging technologies, its fruits need not be only for a few, but we have truly an ocean of opportunities for anyone with the grit, tenacity and perseverance to be a self-reliant learner. With data and tools accessible through the cloud almost anywhere through the Internet, learning is more democratised. World class education is truly in one’s hands today. 

Not only that, by actively participating in learning communities such as GitHub or Google AI Hub, students who make better progress can get the support and encouragement of other members of the communities, to get ahead with their start-ups. 

While a lot of learning and experience can be through self-learning, it may be a good idea to create “co-learning spaces and emerging technologies experience centres” where heterogeneous groups of learners broadly pursuing similar goals but at different stages of their learning journey can learn from each other. 

Future teaching-learning models may draw upon the principles of machine learning: supervised, unsupervised and re-enforcement learning, to support the implementation of heutagogy, as the progression from pedagogy and andragogy. 

In many fields we have seen disruptive innovation transforming the status quo. 

A well known example is that of Kodak which ruled the world of photography, with many inventions, but became bankrupt in 2012. Here is a link to the Kodak story : https://medium.com/@brand_minds/why-did-kodak-fail-and-what-can-you-learn-from-its-failure-70b92793493c

The three reasons that analysts assign to Kodak’s downfall are : failure to reinvent itself, complacency and absence of organisational agility. 

Clayton  Christensen, the famous Harvard Professor in the field of Disruptive Innovation when confronted with what would be the disruption in education, articulated very clearly that it was not merely the use of technology in education that would cause the disruption, but the resulting personalisation of the learning experience is what would cause the real disruption in education. 

And this will happen when Artificial Intelligence is deployed to transform education from a homogenisation process to an individualised learning experience to help every individual achieve his or her potential. 

There are many examples of learning the required skills for a given task, which then allows you to practice those skills in various other new contexts which you may not have done earlier, with or without external  guidance. So if one has the skill of knitting, then one may make products with designs available from others ( including from magazines) or one could go ahead and make one’s own. Similarly after one has adequately learnt the various operations involved in cooking, one can either follow others recipes or experiment and create one’s own. Those with a more mechanical disposition routinely make things from DIY kits, and that has led to the recent emergence of the maker culture with 3D printers. Google has a suite of products that can help you create applications in the field of computer speech or computer vision, available under the AIY brand for AI

It would look as a natural progression from DIY, AIY to LIY: learn it yourself. After having achieved a certain degree of fluency in the skills of learning, one can continue to pursue lifelong learning with the help of learning recipes. If this appears far fetched, visit this link :Machine Learning Recipes with Josh Gordon: https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal

It therefore seems that the approach to education in the post Covid era must consider  the possibilities for how we can adapt our teaching and learning methods to the  new emerging technologies. 

We have seen a gradual progression of how IBM Blue in 1997 defeated the human champion Gary Kasparov in Chess, followed with IBM Watson in 2011 defeating the human champions Ken Jennings and Brad Rutter in Jeopardy. Then Google Alpha in 2016 defeated the human champion Lee Sedol in Go. And AlphaGo zero did it without being explicitly taught the rules of playing Go. 

And now we have self-driving cars. The Society of Automotive Engineers defines 6 levels of driving automation ranging from 0 (fully manual) to 5 (fully autonomous). These levels have been adopted by the U.S. Department of Transportation. 

Level 0 (No Driving Automation)

Level 1 (Driver Assistance ): at Level 1 the human driver monitors the other aspects of driving such as steering and braking. 

Level 2 (Partial Driving Automation) : This means advanced driver assistance systems or ADAS. The vehicle can control both steering and accelerating/decelerating. Here the automation falls short of self-driving because a human sits in the driver’s seat and can take control of the car at any time.

Level 3 (Conditional Driving Automation): The driver must remain alert and ready to take control if the system is unable to execute the task.

Level 4 (High Driving Automation):Level 4 vehicles can operate in self-driving mode. But with ‘geo-fencing’.

Level 5 (Full Driving Automation): Level 5 vehicles do not require human attention―the “dynamic driving task” is eliminated. Level 5 cars won’t even have steering wheels or acceleration/braking pedals. 

There have been  well known Auto-didacts who were also often polymaths. These names ranging from Leonardo da Vinci to Benjamin Franklin are well known.

Many educational researchers have studied learner autonomy in different contexts and David Nunan (1997) set out a scheme proposing five levels for encouraging learner autonomy in relationship to use of learning materials . He labelled them awareness, involvement,intervention, creation and transcendence. Somewhat reminiscent of Bloom’s taxonomy of learning objectives. 

There has also been a fair amount of research in deconstructing the “ skills of learning” into 5 or 6 categories further subdivided into about 10 each. Together these would build up the framework of Howard Gardiner’s Multiple Intelligence theory. 

I have added to the list of these skills, the abilities to deploy artificial Intelligence tools and Apps  to engage in self-directed learning. I am proposing the following levels of an autonomous learner. The pool of skills that describe a learner at each of these levels is detailed elsewhere :

Level 0: No self-learning disposition or ability

Level 1: Some inclination towards self-learning and core abilities of self-learning

Level 2: An autonomous learning system that supports the learner but can be superseded by the human educator ( an AI fluent SmartEducator) 

Level 3: The Autonomous learning enabling system requests to be superseded by the human educator ( an AI fluent SmartEducator)

Level 4: A fully autonomous learner that can function well in limited domains ( cognitive geo-fencing) 

Level 5: Fully autonomous learner with the right dispositions and skills

The AI fluent SmartEducator:

The most important quality of a modern educator is one of being a lifelong learner. The other important attribute is the appreciation that imparting  the knowledge of subjects is less important than fostering a desire to learn and the ability of learning to learn, and building of learning power. 

The best pro-active response to this challenge is to seize the opportunity and become an AI-fluent SmartEducator. Such a person has a Deep Learning mindset ( well versed in deep learning techniques of Machine Learning as well as pedagogy of mastery learning and learning in depth). 

• Is familiar and well acquainted with commercially available solutions ( both in the cloud and at the edge) for deploying AI in education and their best use situations.
• Can be actively involved in creating be-spoke solutions for a specific context. 
• Is well versed with the triple helix of AI-readiness: The Concepts ( theory); The tools; The applications ( educational).

Our program has been designed as a year long program structured as 4 terms of 3 months each.  At the end of each term, the educator-learner will earn a completion certificate of having become a level 1 to level 4 of an AI-ready educator. 

Level 1: AI-familiar educator

Level 2: AI-competent educator

Level 3: AI-proficient educator

Level 4: AI-fluent SmartEducator

The idea of levels or grades to describe a transition in capabilities was used in the Capability Maturity Model ( CMM) in the field of Software Engineering. It was also extended to Learning Maturity Model of 5 levels for e-learning organisations. The eMM supplements the CMM concept of maturity levels, which describe the evolution of the organisation as a whole, with dimensions. The eMM provides a set of thirty-five processes, divided into five process areas, that define a key aspect of the overall ability of institutions to perform well in the delivery of e-learning . Each process is selected on the basis of its necessity in the development and maintenance of capability in e-learning. All of the processes have been created after a programme of research and testing, conducted internationally. The five dimensions of the eMM are:

1. Delivery
2. Planning
3. Definition
4. Management
5. Optimisation

In this proposed approach of emphasising learning as the core activity in the educational context, we are really suggesting to use the learning from Science and the amazing recent progress in the field of Artificial Intelligence to empower both the educator and the learner in their common pursuit of learning. After all an educator is a learner who helps in the development of other learners. 

The ability to learn is present in all of us, and every child in its early years picks up cognitive, affective and psychomotor skills over a very short period of time. Learning becomes a challenge only after formal schooling begins. But as the anthropologist Edward T Hall puts it “ The drive to learn is as strong as the sexual drive, it begins earlier and lasts longer”. Using Artificial Intelligence to augment and enhance this natural drive, would play large dividends. 

The post Covid19 era may herald the transition from the art of teaching to the Science of Learning. So far, it is medicine that carries the label of being the youngest science. It is perhaps time for learning to be the next frontier in science driven by knowledge and rapid advancements in cognitive sciences and neurobiology.