Artificial intelligence is a technology that gives machines the ability to interact with humans, understand events, learn and react to events the same way humans do.
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Over the past couple of years, the applicability range of AI has expanded tremendously, from surveillance camera systems though to digital data and network management, but its presence in education remains absurdly low, at least compared to other less accommodative areas such as sports.
While the education domain has been known to be sluggish when it comes to adopting new inventions, taking up artificial intelligence is a long overdue move that shouldn’t be delayed any longer.
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Discussed below are ways through which AI can be integrated into our education system to increase the productivity of teacher-student interactions and improve the learning process overall.
Grading
AI, through specialized computer programs such as Automated Grading, can be calibrated to learn and simulate teacher behaviour when marking assignments, for automated grade assigning in the future. Over time, the program can learn the academic ability of different students, and prepare personalized training plans based on its findings.
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Student feedback on teachers
Student feedback is one of the oldest ways of assessing teacher output and how tutors relate with students on different levels.
While most schools have migrated from paper questionnaires to online surveys, which is a massive leap, there is still room for revolutionary improvement with the help of artificial intelligence.
Chatbots can be used to engage with students ‘in person’ and collect information without the candor-influencing presence of a teacher or other human. Chatbots use answers from previous questions to formulate new and more relevant questions. They can also help analyze the feedback and come up with a simple average review of the teacher in question.
Personalized learning
Different students possess different learning abilities, and some students record poor results only because they are slow and not necessarily because they are weak.
A teacher in charge of a class of 30 or 40 may simply not be in a position to take each student by their pace and still complete the syllabus in time. They have to choose a pace that they reckon will suit the majority of the class – an immense injustice to the slow learners and an inconvenience to the top students.
Using artificial intelligence, each student can have a personal robot that will serve as a personal assistant. The bot will record all the information taught in class, break it down if necessary, and teach the student at a later time.
Over time, the robot will form a more personalized relationship with the student and adjust the pace and way of reproducing the information.
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Adaptive learning
Adaptive learning is an AI-based education system that modifies material presentation in response to the student’s academic ability. All learners are given the same information at the beginning of the course, and depending on how they respond to questions, the system feeds them different information and questions.
If, for instance, a student fails to get the right answer for the very first question, the system would give them a more elementary lesson next. If they did exceptionally well, the system would introduce them to more advanced content and questions.
The difference between adaptive learning and personalized learning is that in personalized learning the customization and modification of content to meet a student’s needs is not based on adaptivity.
It, instead, involves the use of a learner’s expertise or background to formulate questions and examples. Adaptive learning, on the other hand, adjusts content based on the learner’s progress through the course.
Trial and error
Trial and error method is one of the most gruelling and time-consuming sides of the traditional human-managed education system. Taking the deductive reasoning out of teaching and learning can help save both teachers and students a lot of time and frustration.
Artificial intelligence systems can be used to solve these intricate problems in an instant, freeing up the teachers and students for other essential activities. The system would also break down the solution to help students tackle similar problems in the future.
Intermediate interval education
This is an existing computer application that may have already booked a place in the projected education system of the future. It enables students to revise knowledge when they are about to forget it.
The application keeps track of what the student learns and when they use the knowledge. If they don’t use it for too long, the application reminds the student that they are about to forget something and recommends them to revise it.
After ‘enough’ revisions, the application estimates when the student has permanently stored the knowledge in their memory and stops reminding them about it. Past experiences help the application know when the time is right to stop reminding the student about specific information.
Virtual facilitators
While AI-based tutors may never be as resourceful as human teachers, the prospect of virtual facilitators assisting in certain educational environments in the foreseeable future looks inevitable. The idea is to create humanoid characters that can act, react, interact and think like humans.
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Evidence shows that even if the technology peaks today, both humans and artificial intelligence systems will be needed to take care of different aspects of students’ social and academic competencies. AI, once espoused, will thus serve as a complementary resource rather than a replacement for the human expert in our education system.
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