Artificial intelligence has the potential to meet many of the challenges facing practical teaching and learning. In this third part of a blog series on AI in education, we examine the contribution of AI use in the future of education.
One of the big concerns around the use of AI in education has been the extent to which technology – and particularly AI – will shape the future of teaching and learning.
Technologists and researchers, including UCL EDUCATE director Professor Rose Luckin, are generally of one mind that AI in any guise, whether it be robots or some other application, will not replace human teachers. Learners will always need the empathetic ear and guiding hand of a human teacher who knows their student personally and understands their needs.
This is nowhere more apparent than in existing classroom structures, where students learn within their age cohort and progress towards government-set benchmarks. The advent of technology, and particularly AI, are revealing this traditional approach as outdated and belonging to a bygone age.
Machines can already complement and support the work they do by facilitating that task. AI can augment learning with real-time personal interventions and feedback, including one-to-one tutoring through Intelligent Tutoring Systems (ITS). These are able to choose appropriate tasks for the learner that correspond with their known strengths and weaknesses and support the students in completing them.
A major factor in the machine-based approach is the ability of the technology to adapt to the learning and teaching, instead of the learner having to adapt to the teaching approach. This capability to be flexible and adaptable particularly lends itself to communication and language learning. For example, AI technology can produce real-time subtitling for lecturers, to help students with their writing by providing formative feedback.
Another, and perhaps most important, place for AI is in its uses in assessment and examinations. It is here that the technology can reach beyond what traditional testing is able to measure. Exams have been described by educationalists as “memory tests”, measuring recall and working on the assumption that every question must have a correct answer. It can measure knowledge but falls short when it comes to creative problem-solving, collaboration or empathy.
Traditional examination methods reward subject knowledge and skills such as good memory and recall. AI can supplement this by monitoring progress, providing feedback and identifying misconceptions and lack of understanding.
What it cannot do, at least for now, is to understand our human non-cognitive skills, such as memory, emotional maturity and communication.
For more information, download the third paper of our Byte-sized edtech research on AI in education.
You can read the two other blogs on this series here: