Professors and cognitive researchers frequently depend on test scores to determine how well students comprehend lessons. However, this practice ignores many critical aspects of learning, such as the engaging effect of classroom discussion or interests and motivations of classroom learners. By convention, a neutral observer would be required to recognize these unquantifiable moments of a great teaching experience but human observations are time-consuming and expensive. One can videotape classrooms, but that would be just as cumbersome and costly, requiring an expert to interpret and analyze the recordings afterwards.
Because of advances in Artificial Intelligence, education researchers and computer scientists have come up with ways to create smart systems that can observe and listen in on classrooms, and instantaneously analyze the quality of a teacher’s classroom delivery. This technology has gone through several enhancements as a result of improvements in natural language processing and automatic speech recognition. With current technology, however, it takes a huge amount of work simply to teach these robots how to observe one micro aspect of a classroom at a time.
Can Algorithms replace Expert Observers?
Observing human behavior is different from measuring variables in a physics experiment. Rational assumptions made on the basis of observations of large groups would break down at the level of the individual. Further, due to our limited understanding of all the facets of great pedagogy, we might miss out on an aspect that is crucial to motivating a particular type of learner. This is because questions as complicated as morality and religious belief are not universally agreed upon. Humans respond to each other. A smile or an act of kindness at the appropriate moment cannot be enacted by a machine. Quite often, small gestures make a world of difference to the choices an impressionable mind will make.
AI does enhance the learning experience
Hard data can indeed help identify learning challenges for individual students. Virtual reality can enliven a science lesson visually, and for engineering students, in particular, simulate and break down connections between moving parts in ways that even the most imaginative teacher cannot put together in a lecture. Engineering education in India is being criticized for churning out unemployable graduates in large numbers. Most of them seem to lack communication skills and find themselves at a loss when asked to solve practical challenges in the workplace. Technologies such as AI and Virtual Reality can help monitor and identify personal preferences and aptitudes. And they can do this much faster than any human, providing the opportunity for much needed intervention at exactly the stage at which it is required. That is the crux of providing students with a complete vocational experience and making their education relevant to what is required by industry.
Inherent Bias of Algorithm Designer
One of the advantages of letting machines decide the capacity of learners is that they can process large amounts of data with precision. However, the parameters on which that measurement is made have still been created by a human. That means that a social researcher has made a choice about which attributes are important and which are not. One cannot penalize teachers with low-performance ratings based on a subjective scale if her students have gained great learning experiences. What AI can do in this scenario, however, is that it can provide insights into good teaching practices.
From the perspective of the student, not gifted in language, visual imagery can enhance grasping powers using spatial relationships between objects and their relative sizes. AI monitored intelligence can enable the student to know where she stands in respect of defined learning outcomes. Lessons can be paced, repeated and modified to match a learner’s rate of comprehension. In this age of short attention spans and social distractions, making learning relevant to real life situations encountered by a particular age group can mitigate some of the dreariness of a technical module.
From the point of view of industry, AI finds applications in screening resumes and rank candidates by proficiency. It can also be used to predict which candidate would be successful in an assigned role. AI programs are being used to tag, organize and visually search content by labeling features of an image or video for market researchers. A report published in The Economist, estimates that advances in robotics, AI and automation could potentially cost 800 million jobs worldwide within a few decades. However, in the best-case scenario, engineers or technical workmen in a small number of at-risk occupations would find themselves jobless.
It is the ability to introspect and innovate outside the box that gives humans the edge over automatons. While AI may be able to enhance productivity and improve upon mechanical tasks, it cannot learn independent of experience.
Guest post by Dr. RL Raina, Vice Chancellor, JK Lakshmipat University, Jaipur.
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