AI In The Learning Domain

AI In The Learning Domain

Seeing the manner with which the millennials and generation Z are absorbed in their electronic devices, the role of a human teacher will become all the more critical and ingenious.


Technological progressions have reformed numerous functions, allowing one to accomplish more in less time. Google Maps is enabling us to reach new destinations with relative ease, text editors have introduced autocorrect features in our devices, digital assistants, chatbots are available 24/7 to respond to basic queries, search and recommendation algorithms are saving our time etc. All of these are seeking to enhance user experience. Artificial Intelligence (AI) enabled online shopping bestows an amazing experience by offering recommendations that match our taste by optimising previous purchase data. Advanced software brilliantly assist and provide doctors with diagnosis and treatment options once the patient’s symptoms have been recorded. When it comes to gathering, analysing and interpreting data and determining the recommended course of actions, machines can sometimes excel and do better than human beings. AI software development is an enormous market which is expanding continually.


Humanoid Robots


The field of robotics is also growing rapidly. Humanoid robots – robots whose body shape resemble humans - are being deployed in domains like research, space exploration, personal assistance, caregiving, education, entertainment, manufacturing, maintenance, public relations, and healthcare. In January 2020, ISRO unveiled Vyyommitra, a female humanoid robot that can switch panel operations, environment control and life support systems, conversing with astronauts, recognising them, and responding to their queries. The robot is said to be capable of performing multiple tasks. It is expected to fly in the first unmanned flight as a part of the first human spaceflight programme (Gaganyaan) scheduled to take place in the ensuing days.


At CES 2020, Samsung’s Technology and Advanced Research Labs (STAR) unveiled Neon, a computationally created virtual being. Apart from resembling humans, Neon is capable of displaying emotions, communicating with a human touch, learning from experience and creating new memories. We may also recall the introduction of Sophia as the world’s first robot citizen and the first robot innovations ambassador for UNDP. Combining cutting-edge work in symbolic AI, neural networks, expert systems, machine perception, conversational natural language processing, adaptive motor control and cognitive architecture, Sophia can recognise human faces, absorb emotional expressions and recognise several hand gestures. It can estimate a person’s feelings during a conversation and try to find ways to achieve goals collaboratively. A 2019 report by Oxford Economics indicates that by 2030, robots are expected to displace 20 million human workers worldwide.


The various types of AI in operation based on functionality can be classified into : -


♦  Reactive Machines


♦  Machines with limited memory


♦  Theory of mind


♦  Machines with consciousness


♦ Reactive machines: Reactive machines are the most basic type of AI systems. They cannot form memories and can be used to automatically respond to a limited set or combination of inputs. Deep Blue, IBM’s chessplaying supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of such a type of machine. Deep Blue could predict the possible moves for itself and its opponent, and choose the best move from the available options.


♦ Machines with limited memory: The second class of AI, machines with limited memory such as self-driving cars, function using simple pieces of information from the past. However, such information is only transient, created from the observations and added to the car’s pre-programmed representations of the world. So, in addition to having the capabilities of purely reactive machines, they are also capable of learning from historical data to make decisions.


♦ Theory of Mind: Theory of Mind is the next level of AI systems undergoing innovation where machines can form representations about the world and other entities to some extent. Theory of Mind is the ability to predict the actions of self and others (Leslie, A.M, 1987). The ability to read others’ mind is not only a distinguishing human quality but also a fine bridge between the existing machines and the desired ‘state of the art’ futuristic technology. The actual development of Theory of Mind generally follows an agreed upon sequence of steps (Wellman, H. M. & Liu, D, 2004). There are several developmental precursors that infants need such as the concept of attention, understanding others’ intentions, ability to imitate, understanding of false beliefs and hidden feelings for the development of the Theory of Mind. Around the age of four, when children start to think about others’ thoughts and feelings, the true theory of mind emerges. Theory of Mind helps us communicate or offer our services considering the recipient’s needs. Relying on this with an understanding of self and others’ motives, we communicate, keeping in mind what others already know or what they do not know. Theory of Mind, besides influencing social interactions are also critical to how societies are formed and shaped.



 A relevant question at this point is whether the relationship between human beings and humanoid robots can be characterised by a mode of interaction like the relation among humans that captures mental states and information - the expressed, implicit and unsaid. With advancements in robotics attempting to create models with appropriate coordination of a large number of perceptual, sensory-motor, attentional, and cognitive processes; it remains as a challenge about how we build an artificial theory of mind in a robot akin to a human.


Machines with Consciousness: Consciousness refers to an individual’s awareness of one’s unique thoughts, memories, feelings, sensations making sense of one’s internal and external environment for a purposeful movement (Blackmore, 2004). This awareness of oneself is a subjective and unique experience. Building machines that can form representations about themselves and the world and who have this element of consciousness is being seen as the final step in the research on Artificial Intelligence. An AI of such a type will be capable of understanding and evoking emotions in others and also have feelings, needs, beliefs and desires of their own. Creating this kind of selfaware machines shall mark the pinnacle of success in AI research.


Appreciating the burgeoning discipline of AI as discussed above, it is indeed intriguing to study the influence of AI in the domain of education and also whether humanoid robots can take up the role of teachers in the classroom.


 According to a review published in the Science Robotics by Tony et al. (2018), “Robots can free up precious time for human teachers, allowing the teacher to focus on what people still do best: providing a comprehensive, empathic, and rewarding educational experience.” This can be best illustrated through Jill Watson, a robot created by Professor Ashok Goel from Georgia Tech specifically for the online course, Knowledge Based Artificial Intelligence. According to Goel, every time this course was offered, the 300-odd students who enrolled for it would post over 10,000 queries. Apart from being taxing on the teaching assistants, often the questions were repetitive. This led Goel to create Jill Watson, whose efficient handling of the student queries not only relieved them of excessive workload, it positively influenced student satisfaction.


AI in education can deftly manage tasks such as marking attendance and grades. It could also help teachers in improvising course design promptly by generating new lesson plan suggestions and offering assistance while navigating online teaching resources. Besides this, it can also assist teachers in giving them significant insight over the student’s needs. Classrooms equipped with language processors, speech and gesture recognition technology, eyetracking, and other physiological sensors can collect and analyse information about each student. The same information can then be utilised by the teachers to tailor their teaching strategies, matching it to student needs.


An article in the Wall Street Journal (2019) best illustrates how modern technologies such as AI and robotics are being incorporated into some of China’s classrooms. Students at the Jinhua Xiaoshun Primary School in Eastern China begin their lessons by putting on headbands that use three electrodes, one on the forehead and two behind the ears to detect electrical activity in the brain, sending the data to a teacher’s computer. The software generates real-time alerts about students’ attention levels and gives an analysis at the end of each class. A light at the front of the headband changes colour to reflect a student’s concentration levels.



Concerns are, however, being raised as to how well the technology can track concentration, the risk of false readings and the impact of AI surveillance on children’s mental wellbeing. Being constantly watched by an electronic eye in the classroom can have an impact on children’s psychological health, putting undue pressure on them. Apparently, some parents were concerned with the possible misuse of student data, thereby making cybersecurity a key issue for consideration. AI is also being applied to education as a part of Intelligent Tutoring System (ITS) where a pedagogical agent can be designed to model interactions in the learning environment by assigning different roles to it such as a tutor or a co learner, depending on the desired purpose of the agent (V.J. Shute, D. Zapata-Rivera,2010).


 Undoubtedly, when used wisely, AI and robots can complement and enrich the teaching experience for students and teachers. It is interesting to imagine humanoid robots replacing humans as teachers in the classrooms since digital teachers can come with unique benefits. They will not seek holidays, monetary reinforcements or be late for work. A humanoid robot programmed for teaching a specific subject can be a vast reservoir of information as its system can be continually updated on the given subject from the beginning to the most recent literature. A human teacher’s knowledge is limited to their education or training and efforts made by them to stay updated with the latest developments in their respective fields. On many occasions, owing to excessive teaching load, administrative responsibilities at work, family commitments or paucity of time and resources, it becomes challenging for them to invest in consistent efforts for self-growth or knowledge building.


However, despite the above benefits, an advanced humanoid robot taking up the role of a teacher in entirety not only remains as a distant goal, but is not greatly desirable as well. The role of a teacher is likely to remain irreplaceable by a machine for a long time to come for the below mentioned reasons.


Dependence on social interaction and EI skills


Teaching is heavily dependent on active human interaction; be it verbal or nonverbal communication. Humanoid Robots may be efficient in sharing the relevant content and theories, but the way a teacher facilitates the class keeping in mind the nature of the subject, differing student abilities, and creates engagement is heavily dependent on human interaction and social intelligence skills.


For instance, when it comes to teaching subjects like psychology, philosophy, literature and management, student learning is not confined to a textbook, course material and lectures. The free discussions, deliberations, debates, reflective exercises, carefully planned and executed group activities stimulating active participation from students, facilitated by a teacher all play a significant role in knowledge acquisition and skill building. Such a participative experiential learning environment in higher education or adult learning leads to personal growth and learning of both the students and the teacher.


However, it calls for optimising multiple skills beyond lecturing like empathy, patience, enthusiasm, self management and playing a mediating role as a facilitator. Also, an able teacher modifies his teaching approach and methodology, observing the response and progress of pupils to achieve learning outcomes. So, the same teacher may adopt a different pace and teaching style for different students going by observational cues and learner’s preferences in the classroom. Considering these subtle aspects, behavioural skills, and high dependence on emotional intelligence, the success of robots taking up the role of a teacher in entirety is still a matter of doubt.


Vicarious learning


 As per the social learning theory proposed by Albert Bandura (1965), most of the learning happens through observation. Observational learning can happen at any time through environmental, social and cognitive interactions and influences. Students are more likely to imitate or learn behaviours from a teacher they perceive as a role model who is a fellow human rather than a machine with differing constitution and abilities.


For example, an extremely sincere university professor was left deeply displeased when someone entered late in class though he made it apparent only through facial expression. The environment of the university was relatively relaxed, and the professor could have adopted a similar attitude, but he was always on time for class. The teacher was greatly admired and loved by students for his excellent teaching style, disciplined attitude and earnest efforts he made for each session. The example and standard set by the teacher for two consecutive semesters influenced the student’s attitude and behaviour for the better. Even after several years, the teacher is fondly remembered with gratitude by students not only for his teaching, but for his integrity and for instilling the virtue of discipline in life. Vicarious learning is more applicable in the case of younger children who are keenly observing the actions of those around them, be it caregivers, teachers, siblings or friends. As technology continues to surface every sphere of our life, it will be worth reflecting in the near future the kind of ‘models’ we want for our childrenhumans or humanoid robots as also its implications.


Inspire and motivate for high performance


The Pygmalion effect or Rosenthal effect, is a psychological phenomenon wherein high expectations lead to improved performance in a given area. Robert Rosenthal and Lenore Jacobson’s study (1968) showed that, if teachers were led to expect enhanced performance from children, then the children’s performance was enhanced. Lady Bird Johnson once said, “Children are apt to live up to what you believe of them.” Great teachers are able to appreciate students’ individuality, their performance in the development graph and encourage them by communicating realistic expectations.


 If humanoid robots are well programmed to teach and grade student performance, they can well be used to communicate expectations to the students. However, instilling faith in the student to strive for success in a given sphere in life requires an ability to see the student beyond his/ her grades. Each student has a unique aptitude, competencies, motivation, limitations and ambitions. One can wonder if a humanoid robot can successfully replicate the way a human teacher can understand, connect and motivate him to pursue in a given field communicating inside and outside the classroom engaging in formal and informal communication.



Student engagement


Student engagement manifests in the classroom by the degree of attention, interest, participation, curiosity and involvement shown by students in the classroom. Meece et al. (1988) set a model for cognitive engagement seen in the learner’s participation, and interaction with the learning material, learning activities and learning community. In a world where information is available at the click of a button, teachers strive for developing and sustaining student engagement in multiple ways. They provide authentic and specific feedback, remove any barriers to learning, use creative methods to deliver content, clarify concerns, provide reinforcements, discuss real-life examples etc. Understanding the association between student engagement and performance, great teachers listen, tell stories, communicate to the best of their ability to stimulate student’s creativity.


Enhancing student engagement requires the teacher to not only optimise the knowledge of the subject, but how that knowledge is transferred. Tony et al. (2018) in their article on social robots for education write that in order to build a fluent and contingent interaction between social robots and learners the seamless integration of a range of processes in AI and robotics is required. Starting with the input to the system, the robot needs a sufficiently correct interpretation of the social environment for it to respond appropriately. This requires heavy reliance and development in speech recognition, source - visual social signal processing so that a robot can access the social environment. Speech recognition is still not so effective when it comes to understanding spoken utterances from young children. Making use of a touch screen to overcome this shortcoming can disturb the natural flow of the interaction. For robots to be autonomous, they must make decisions, take actions in a pedagogical environment with an understanding of the learner’s ability and progress. Building artificial social interaction among two humans in this manner is an essential prerequisite for enhancing student engagement that involves a wide range of cognitive and affective components, which is still a challenge in AI and robotics.


AI is enabling machines to do tasks requiring human intelligence, machine learning is providing systems with the ability to learn and improve automatically and researches in deep learning inspired by the structure, function and interconnections between billions of neurons is likely to lead to automation of many traditional roles and routine tasks. AI may bring newer opportunities and increased efficiency, but emotional intelligence is one area that machines find hard to emulate.


According to a recent report published by Capgemini Research Institute (2019), Emotional Intelligence will be a must-have skill in the future, with its demand likely to rise six-fold within the next five years. The role of a teacher is not merely confined to sharing knowledge; but requires one to don multiple hats depending on the situation-be it a mentor, counsellor, motivator, guide and ally drawing heavily on emotional intelligence skills adapting one’s approach as per the unique needs of different students. To conclude, AI can be a very helpful assistant in making academic functions more robust, freeing up precious time for teachers and also complement the teacher in the classroom but taking up the role of the teacher completely seems inexpedient. In fact, seeing how the millennials and generation Z are absorbed in their electronic devices, the role of a human teacher will become all the more critical and ingenious.




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Dr. Farah Naqvi is a writer, academician and behavioural scientist. She started her career with Indian Institute of Management, Ahmedabad and has worked with institutions like ICFAI Hyderabad, IBA Bangalore and Center for Organization Development, Hyderabad as Asst. Professor. Currently she is associated with the Indian Institute of Business Psychology (IIBP) as a Senior Researcher. Her website is


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