Artificial intelligence and machine learning protocols are clearly set to undergo a renewed burst of interest and proliferation as witnessed by events that have shaken up the tech industry in the past year. From language models to advanced image generation software, AI has gotten a lot better than it used to be. Complex neural networks and better computing capabilities due to enhanced natural language processing have empowered artificial intelligence algorithms to better communicate and place together computations that humans might want to automate. This has seemingly resulted in rising fears surrounding automation and the dehumanization of several industries as a consequence. While AI is several ways away from even coming close to human traits and skills, fears of automation still abound, and think tanks have even called for the pausing of the development of AI technologies. But how does this fare in the case of teachers and education? Will AI teaching ever replace human teachers? While this seems rather unlikely, AI education will certainly end up altering the way teaching functions as a whole.
AI automation and replacement of the human workforce have been the object of speculation and concern ever since modern computing came into the picture. So far, the risks of automation are often graded from high to low based on the degrees of intuitive and creative input invested in tasks. While repetitive and mechanical processes are at the higher end of the risk scale, “humane” professions such as teaching have remained at the opposite end. However, this does not mean AI will not influence the profession either, with the groundwork for AI education and intelligent learning algorithms being laid down every passing minute. The further sections delve into these prospects and how automation might seem different in the teaching domain.
Teaching and AI Automation: Areas of Interest
There are a number of important domains where teaching AI can be useful for current educators.
While classrooms and the overall education system in itself are bound to undergo major levels of change in the coming years, there are a few domains that learning automation can change. Some of these are outlined below.
1. Automated Tests and Quizzes
Several teachers prefer gauging their students’ performance from time to time and conducting quizzes and tests. Ever since the pandemic and the subsequent digital learning paradigms that had to be adopted, electronic quizzes and tests became a popular option. With the pandemic now behind us, AI education algorithms can be used to put together quizzes and tests based on the curriculum to lessen the burden of drafting, grading, and maintaining test records. While AI testing is a developing concept, the use of automation for simplifying regular class tests and quizzes can be useful for both students and teachers.
2. Submissions and Assignments
Students are expected to turn in several assignments and other submissions centered around their coursework. Keeping track of these submissions manually can be taxing. Straightforward algorithms that can track submissions will make things easier and also reduce the burden of maintaining paper records. For simpler assignments, advanced machine learning models can also be utilized for evaluations.
3. Communication and Collaboration
Learning automation can also bring about ease in communication and efficiency. This applies especially to K-12 learning programs where teachers and students’ parents or guardians often need to communicate. Apart from the scheduled meetings, frequent communication can be made possible by sending out automated surveys and questionnaires that allow parents to cite their concerns and expectations. Tools like big data and analytics can help with making sense of responses and addressing common concerns.
4. Enrollment and Registration
A lot of energy and resources are spent on streamlining enrollments and registration processes at educational institutions. While using teaching and administrative staff in more of their core roles, schools and universities can deploy easy-to-use interactive chatbots that can address frequently asked questions alongside simple conversations. Initial application processes can then be handled digitally for a smooth and traceable process for both the institution and the applicants.
Will AI Teachers Become a Norm?
Human teachers are integral to education, and it is highly unlikely that they will be replaced by automated alternatives.
Despite the low risk of automation to the teaching profession, outstanding fears regarding the seemingly multifarious nature of artificial intelligence and machine learning persist. However, it must be noted that automating teaching as a whole would result in detrimental outcomes for students as well as the economy. While proponents of a machine-based learning system are quick to point out elements such as human error and other limitations of regular teachers, AI is not as proficient in its tasks as some would hold it to be. The concerns surrounding hallucination in language models are very real, and the lack of critical thought makes it only an extension of a computing algorithm. While AI is no doubt highly efficient and precise in the tasks that it is trained for, it is still prone to several pitfalls, with limitations of data sets leading to bias being one among them.
Regardless, when used as an adjunct to human teachers, AI teaching can be of great value. It has the potential to enhance adaptive learning and raise the standard of personalized education. Moreover, such systems are especially valuable when catering to students with special needs in assistive learning modules. Learning automation models can also make course content available to students not physically present in the classroom. Similarly, it can also make a classroom accessible to a teacher remotely via methods such as screening and other virtual learning protocols. Systematic approaches provided by machine learning can greatly streamline current learning availability for students while simplifying tasks for teachers.
AI Automation and Learning in Tomorrow’s World
Learning will be revamped by the induction of AI and intelligent automotive models for teaching and education.
While automation is a potential outcome of AI that people have been fearfully anticipating for quite some time now, it might not quite bear the traits that people have in their minds. On a more practical level, artificial intelligence can only be a supportive tool for teachers to provide a higher standard of learning to students. Concepts such as AI teaching and other learning automation models will ultimately be managed and operated under human supervision to ensure the component of empathy, situational awareness, and a grounding in psychology remain imbued in teaching. Though concepts such as responsible AI do promise ethical and progressive creation and streamlining of AI protocols, the centrality of human intelligence and thinking will not find a complete substitute in algorithmic models and autonomous systems.