AInEd Supremacy: 

Recently China announced that it had achieved Quantum Supremacy. Google had made the same claim in October2019. The phrase ‘ Quantum Supremacy’  was coined  in 2012 by the Caltech Professor John Preskill to describe the  situation when a Quantum Computer could solve a problem that no classical computer of the day could solve in a reasonable time. 

In a similar spirit I have coined  the phrase “ AInEd Supremacy” to describe the situation when the AI technology of the day in conjunction with a human educator will achieve learning outcomes that no traditional teacher with traditional technology could achieve. This may also be seen as the solution to the 2 sigma problem identified in 1984 by Benjamin Bloom.

About a decade earlier, while teaching at IIT Kanpur, I had explored Mastery Learning with Personalised Instruction, for which I had published a paper : M.M. Pant, “Experiment with self–paced instruction in undergraduate Physics”, J. of Phys. Education, 3, 1 (1975).

The AInEd supremacy will be achieved during this decade, perhaps much earlier with the use of Chatbots, Face and emotion recognition, recommender systems, text to speech, speech to text and machine translation. And of course technologies like GPT3 which were explained in this group earlier by Dr. RC Sharma. 

The 2018 book by Anthony Seldon “ The fourth Education revolution” explains how AI will fundamentally transform education. He says that so far education has been the Cinderella of the AI story. 

Terrence Sejnowski, Francis Crick Professor at the Salk Institute for Biological Studies and Director of the Computational Neurobiology Laboratory, says that “ Education is going to be the killer app for deep learning”. 

Sir Anthony Seldon illustrates how each of five traditional factors in teaching will be transformed by AI over the coming decades: 

1. Preparation of material will be done by ‘Curation specialists . . whose job it is to work with AI machines to author and identify the most appropriate material for particular student profiles.’ 
2. Organisation of the learning space: ‘Separate classrooms will disappear in time and replaced by pods and wide open, flexible spaces which can be configured for individual and flexible collective learning. Sensors will monitor individual students, measuring their physiological and psychological state, picking up on changes faster and more accurately than any teacher could.’ 
3. Presentation of material to optimise learning/deeper understanding: ‘The flexibility of visual representation with AI allows material to be presented to students which renders much teacher exposition redundant.’ 
4. Setting assignments and assessing/self-assessing progress: ‘Advances in real-time assessment enabled by AI will virtually eliminate this waiting period [the time lag between students being assessed and them receiving feedback on their performance} and ensure feedback comes when most useful for learning.’ 
5. Preparation for terminal examinations and writing summative reports: ‘All this will be swept away by AI. . . . In its place will be attention to continuous data reporting, and real time feedback that will help students discover how to learn autonomously and how to address any deficiencies on their own.’ 

Will we need teachers in the future? Seldon is clear ‘We do not believe that it is either possible or desirable for AI to eliminate teachers from education’ but he goes on to point out that ‘the application of AI places more responsibility for learning in the hands of the student, for how their time is spent and on what, even from a young age.’ AI will change however the job of the teacher forever. By supporting teaching in all their five traditional tasks, AI will usher in the biggest change the profession has ever seen.’ We will see the emergence of the AI fluent SmartEducator. 

Interestingly Seldon recognises that remote teaching is a distinct possibility: ‘Imminent advances in virtual technologies will mean too that teachers no longer have to be physically present to offer their services.’ 

The technology to deploy ‘holograms’ for professor’s presence anywhere ( watch this 4 minute video) already exists. With time it will become more readilyavailsble: https://youtu.be/auJJrHgG9Mc

Technology powered by AI could even hel on resurrecting dead professors : https://theconversation.com/chatbots-that-resurrect-the-dead-legal-experts-weigh-in-on-disturbing-technology-155436

In addition to the help that AI will provide to facilitate the tasks of a teacher, there are a few more results that learning analytics will provide that enhance the teaching quality and the learning effectiveness. For example when the learner is transacting the content, data will automatically be recorded for the time spent at various sections. With permission granted to record facial expressions, there will be very useful data that indicates the level and nature of engagement with the content. The qualities of grit, perseverance etc. referred to by Paul Tough in his seminal work “ How children succeed” can now be observed and analysed quantitatively. Drawing on groundbreaking research in neuroscience, economics, and psychology, Tough shows that the qualities that matter most have less to do with IQ and more to do with character: skills like grit, curiosity, conscientiousness, and optimism.

To help with his class the spring of 2016, a Georgia Tech professor Ashok Goel, hired Jill Watson, a teaching assistant unlike any other in the world. Throughout the semester, she answered questions online for students, relieving the professor’s overworked teaching staff.

But, in fact, Jill Watson was an artificial intelligence bot.

Ashok Goel, a computer science professor, did not reveal Watson’s true identity to students until after they’d turned in their final exams.

With more human-like interaction, Goel expects online learning could become more appealing to students and lead to better educational outcomes.

Just as professors may use AI as teaching assistants, students could use a chatbot to facilitate individual mastery learning, the technology that Bloom suggested in his 1984 research….the two sigma  problem. It would have taken about 40 years to realise Bloom’s vision. 

AI and related technologies can help bring the Oxford/ Cambridge tutorial system to any University that chooses to adopt it. While a good deal of the teaching in Oxford is provided in just the same way as elsewhere – through lectures, seminars, fieldwork and practicals – the tutorial system is what sets Oxford (and Cambridge!) apart.

Universities in future will have a physical campus as well as a campus “in the cloud”. Former faculty and graduated students can continue to take part in the “cloud events” when they have left the Campus. 

Universities will continue to support their students with lifelong learning even after they have received their degrees so that they continue to ( in the words of Alvin Toffler) learn, unlearn and re-learn. In addition to the traditional degrees, such micro-credentials may be given as ‘digital badges’, which in due course may be managed as ‘ Blockchains’. 

In about a decade from now, the impact of the science of learning empowered with Artificial Intelligence will be seen on many features of the present educational landscape that we take for granted. Every student would have access to the best education in the field of choice, in a time schedule that is optimised for the learner. It is extremely tough to determine as to which teaching method resonates with most of the students. A well designed tutoring programme, with the help of Artificial Intelligence and Machine Learning, can overcome this roadblock in the swiftest possible manner. As a matter of fact, a few tutoring programmes based on AI are already in place and they cater to students across different levels and subjects, such as science, mathematics, writing etc. Though such systems and programmes are not that effective when it comes to high-order creativity, they are extremely efficient for clarity on fundamentals and concepts.

The various School leaving Board examining bodies will become irrelevant, as will the entrance examinations such as the various incarnations of the JEE-IIT, NEET, CAT, LSAT etc. Just like the technologies of CAT-scan, MRI or PET did not replace doctors, but made them more effective, the science of learning with technologies of the 4th Industrial Age will help educators to be a very important element of the success of the country and its people in the emerging knowledge economy. 

The current state of assessment:

Of the various formats of questions that are used for assessments, multiple choice questions, fill in the blank or match entries from 2 columns are easily capable of being automated and evaluated with computers. Not only can a large number of students be graded automatically, the items can be evaluated on features such as facility, reliability and discrimination index. With good quality items for the bank of questions, it is relatively straightforward to implement an adaptive testing system.

The essay type questions are a formidable challenge to automated machine testing. But recent developments in machine learning, have made grading and assessments of essay type questions possible. 

This is also a time that shows up the irrelevance of the monolithic terminal examinations. Sir Anthony Seldon in his 2018 book “ The fourth education revolution” has analysed why such examinations will no longer be relevant. Sir Anthony, Vice-Chancellor of the University of Buckingham, added that continuous assessment would sound the death-knell for exams: “The all-conquering cumulative exam is going to die and we should celebrate its death…The monolithic exam is drawing to a spluttering end.”

Micro-credentials are one of the hot rising idea in the education space. To understand the basics, go look at your child’s Xbox or PlayStation.

For most of the major games, there is an accompanying set of achievements, or badges. Every time a player achieves a particular task (kill 50 zombies without reloading, drive over every tree in the enchanted forest, smash every Lego fire hydrant, etc.) they get a small digital badge on their big page of achievements.

Micro-credentials take a similar approach to education. The root of the idea is simple–you demonstrate a very specific skill, and a badge certifying that micro-credential becomes part of your personal digital file. Some of the earliest micro-credentialing involved computer programming skills, but it has grown far beyond that. To see just how many types of micro-credentials are out there, take a look at Digital Promise. 

It offers micro-credentials of its own, but it also provides a platform for other entities to offer their own sets of micro-credentials.

To answer the question of what is worth learning, we need to balance between ‘pure’ learning and ‘applied learning’. The Usefulness of useless knowledge by Abraham Flexner, founding Director of Institute of Advanced Study Princeton: https://www.brainpickings.org/2012/07/27/the-usefulness-of-useless-knowledge/. There are many reports that refer to the decreasing half-lives of useful knowledge. 

To have an experience of what AI can already do for language learning, please explore Duolingo.com. Similar AI powered learning apps are available for some other subjects also. To appreciate how Duolingo uses AI in its design and delivery see : https://venturebeat.com/2020/08/18/how-duolingo-uses-ai-in-every-part-of-its-app/

These are a few educational applications that harness the power of AI to improve learning in students of all ages – from primary school through to college – and empower both learner and teacher with more avenues for reaching their educational goals.

1. Thinkster Math:20 Thinkster Math is a tutoring app that blends real math curriculum with a personalised teaching style. They use artificial intelligence and machine learning in their math tutor app to visualise how a student is thinking as they work on a problem. This allows the tutor to quickly spot areas in a student’s thinking and logic that have caused them to become stuck, and assist them through immediate, personalised feedback.
2. Brainly:21 Brainly is a platform where students can ask homework questions and receive automatic, verified answers from fellow students. The site even allows students to collaborate and find solutions on their own. Brainly uses machine learning algorithms to filter out spam.
3. Content Technologies, Inc.:22 Content Technologies, Inc (CTI) is an AI company that uses Deep Learning to create customised learning tools for students, such as JustTheFacts101, where teachers import syllabi into a CTI engine. The CTI machine then uses algorithms to create personalised textbooks and coursework based on core concepts. Cram101 is another example of their AI-enhanced offering, where any textbook can be turned into a smart study guide, providing bite-sized content that is easy to learn in a short amount of time. It even produces multiple choice questions, saving students time and helping them learn more effectively.
4. MATHiaU:23 Similar to Thinkster Math, Carnegie Learning’s MATHiaU offers AI-based tutoring tools for higher ed students who feel lost in lecturer-sized classrooms. The app is guided by each student’s unique learning process, keeps them aware of their daily progress, and helps teachers tailor lessons to meet each student’s specific struggle.
5. Netex Learning:24 Netex Learning allows teachers to design and integrate curriculum across a variety of digital platforms and devices. The easy-to-use platform allows teachers to create customised student content that can be published on any digital platform. Teachers also get tools for video conferences, digital discussions, personalised assignments, and learning analytics that show visual representations of each student’s personal growth.

These should be seen not as the best that AI can do, but as illustrations of the enormous activity in the field, and to form the view that this is not far away. A co-operative symbiotic pursuit by educators and technology developers may achieve AI Supremacy in a short time, by July 2022, when we will be celebrating India@75.

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