In 2016 ‘Sophia’, a female social humanoid from Hanson Robotics, gained notoriety as the first robot to be granted citizenship. People have started to take notice of robots as having a primary practical role in changing the nature of the modern workplace. High-tech automation is seen as a genuine business proposal across many sectors of work and employment. Calling to preserve the human undertaking, education experts could argue that the social process of learning is something best guided by ‘human’ teachers.
The data-driven machine learning of artificial intelligence quickly adapts to showcase a bank of coherent information adhering to the matter at hand. The unbeatable database can pull references, surveys, and sources in a time-efficient method to save cumulative research. In contrast, human teaching is ‘pedagogical’, with many teaching methods to elevate the absorption of coherent information. It can be further argued that data alone does not bolster learning that adapts to the diverse ways students comprehend information. To combat that, human teaching has a strategic approach and implements a theoretical approach to target unique learners.
With the increased integration of robots and artificial intelligence into everyday life, debates have emerged on whether AI could be fully intertwined with human teachers. This issue has to be understood in light of the technical development of AI and its function in education. While popular culture often visualises robots entering classrooms, such as R2-D2 or Wall-E, the real involvement of AI in education is far from that simple.
From a scientific dream in the 1950s to building machinery that is able to think intelligently, AI has come a long way. Actually, in their most recent versions, when the surge of machine learning began during the 2010s, AI systems started learning and improving.
With the rapid development of AI, a question is thrown up whether the role of AI is essentially to support the teacher or to replace the teacher altogether. Some believe teaching should be left to humans, but others say AI could be a powerful tool for education in the future.
AI in Education is no longer a concept; it’s a reality. Recent strides with deep learning now enable AI models to personalise learning experiences, monitor student progress in real-time, and even facilitate the development of critical thinking. However, the one thing that has remained at the centre of the conversation and probably will continue to do so is: should AI replace teaching or supplement human instructors?
Researchers at universities study ways in which AI could solve more specific classroom challenges, such as improving engagement for students with autism or serving as a peer learning companion. With continuing improvements in these AI systems, many dream that learning will be more personalised, efficient, and flexible for all students. Generally, teaching is regarded as a profession that develops not only the mind but also the heart and soul of students. Educational theorists such as Dewey and Cohen emphasise the complexity of teaching and the value of human influence, while supporters of AI insist it will dramatically improve the education world. While AI might be used for grading and analysis, human teachers build relationships based on mutual trust and curiosity that call out the best in creative ways. Some visionaries believe it will take less than ten years before robots replace human teachers, while others claim AI will be a trusted assistant that will support the work of teachers, not take over. Whether AI proves to be a helper or a rival, it will play a significant role in the future of education.
The concept of AI in education has shifted from a question of “if” to “how” it can bring a paradigm change in teaching. As AI grows, educators and policy framers will also have to strike a balance between technological advancement and the human touch that is intrinsic to education. Whether AI is going to act as a supporting tool or try to replace teachers is yet to be seen, but technology will leave an indelible mark on the classrooms of the future.
Classrooms and teachers may turn out to be the old manual labourers of workers in factories, something from a bygone era we reminisce about. This does not devalue the teacher’s talent or intellect, only that one day, AI may surpass them in some respects.
It’s easy to see the appeal of robotic teachers. As Kristin Houser notes, digital teachers would never need time off, would never be late, and would never make mistakes. Properly programmed AI would also avoid biases related to gender, race, or personality preferences.
Still, it’s important to recognise that not all humans make great teachers. Some aspects of teaching, like managing information and keeping attendance, could easily be done by AI. However, the bigger question is whether AI can perform the more complex work of a good teacher, such as inspiring and mentoring students.
AI in education raises two key questions: (1) What makes teaching “good”? (2) What aspects of teaching are best suited to technology?
The ability to differentiate between ‘technology’ and ‘humans’ becomes increasingly contentious. One of the first issues to emerge in any informed discussion of AI is the fact that all technology is in its origins and execution-‘human’. Any ‘robot teacher’ is, in real terms, a composite product created from people, machinery, the physical world, encoded frameworks, and social contexts. Hence, robotics are configured and designed by human designers, and algorithms by human programmers. Likewise, most ‘machine learning’ involves computers trying to make out patterns from the aggregated actions of millions of humans. Accordingly, if we are to make sense of the use of AI technology in education, we need to take a ‘socio-technical’ approach; we see technology as a combination of technical and scientific factors alongside economic, political, social, and cultural issues. Notably, the distinction between ‘human teachers’ and ‘robot teachers’ is not one of people versus machines. Instead, we focus on how multiple groups of people are interconnected with machines and software in complex and increasingly connected ways.
This socio-technical perspective certainly pushes us to think about the wider connotations of AI-driven education. These are not advances that arise through the intellectual curiosity of a few technologists, developers, and researchers alone. Instead, the enthusiasm for putting AI systems into classrooms corresponds with the broader political struggles over the future of education and the nature of the emerging ‘digital age’. It is part of some substantial wider agendas that ought to be kept in mind as our discussions continue.
First, efforts to introduce forms of AI into education are just one of many broad reforming activities of Big Tech companies, reflecting a distinct Silicon Valley set of values that is an increasingly prominent component of global capitalism. Alongside interests in everything from low-income healthcare to high-speed public transportation, these influences are pushing for substantive shifts in how the improvement and reform of education are conceived. Such work thereby presupposes a radical belief in what Evgeny Morozov has termed ‘technological solutionism’, i.e., believing that digital technologies provide a ready-made ‘problem-solving infrastructure’ through which complex social problems can be addressed.
These influences are pushing for substantive shifts in how the improvement and reform of education are conceived. Such work involves a fundamental faith in what Evgeny Morozov has termed ‘technological solutionism’—i.e., the belief that digital technologies offer a ready ‘problem-solving infrastructure’ that is capable of tackling complex social problems. Such thinking underpins the assumption that the issues in education can be addressed through applying AI-driven operational logic that has proven successful elsewhere (such as Uber and Netflix). The key here is a willingness to approach education change in an ‘entrepreneurial’ fashion—experimenting with substantially funded educational interventions that can be rapidly modified and terminated if not proven successful. This approach is celebrated as embodying a ‘fail fast, fail often’ mentality of software development, with an emphasis on ‘beta-testing’ possible solutions that might later be scaled up on a system-wide basis.
In addition, current support for AI-driven education chimes with a wider belief that schools and universities will benefit from high-tech innovation and ‘digital disruption.’ This itself feeds into the growing corporate impatience to reform what is perceived as outdated and inefficient education systems. Indeed, an increasingly prominent argument within the hyperbole that surrounds AI in education is the notion that current forms of school and university are ‘broken,’ out of date, and rapidly becoming not ‘fit for purpose.’ As a result, IT corporations, philanthropic foundations, venture capitalists, and other ‘edu-preneurs’ are investing substantial amounts of time, finance, and publicity in attempts to ‘fix’ and ‘disrupt’ traditional ideas of what a school or university is.
