Photo by Andras Vas on unsplash

Previous posts in this series have highlighted the importance of human intelligence and emotion in education. We have traversed several emerging ideas, including the use of virtual teaching assistants (chatbots), ultra-personalised learning and machine intelligence, but the most important component in education is still the human element.

Other jobs in society may already have been supplanted by robotics and artificial intelligence. Mostly, they are repetitive, low level or dangerous jobs, but replacing teachers with computers is neither desirable nor expedient. However, replacing some aspects of what teachers do is both effective and inevitable.

“AI is not about replacing the human with a robot, it is about taking the robot out of the human” says Usama Fayyad, and this will prove to be a key theme in the future of AI enhanced education.

The future of education, in all sectors, will be a marriage of convenience between teachers and technology. In many parts of the world this won’t happen at all, usually because of what has been called the digital divide. Even in the Western industrialised nations of the world, AI and other advanced forms of technology may not be deployed, for a variety of reasons including professional reluctance, lack of infrastructure, political resistance or lack of funding resources. Where it is implemented, AI will rapidly transform the design of courses, providing educators with vast amounts of new insight into what students do, how they study and what is most effective.

But what about regions of the world where AI will become commonplace?

AI enhanced course design is supposedly the next big development in digital education. It will not replace good teaching, but will improve the ability of teachers to create and curate resources, and will have an impact on the learning environments they operate within. Futurist Nicholas D Evans suggests that AI tools will enable course developers to discover shortcuts, and improve their efficiency. He writes:

“As an example, course developers at leading institutions will utilize AI technologies to help them quickly assemble their courseware and reduce the manual effort required by up to 80 percent. This frees up course developers and administrators to be more strategic and plan their portfolio roadmaps in close collaboration with business and community partners.”

This is due in part, he claims, to the capability of emerging AI technologies to be able to ‘learn’ based on the massive array of data patterns they can access. Evans is optimistic about how computer vision may play a constructive part in observing and interpreting human behaviour:

“Trainable AI will be able to observe a physical class via machine vision and thereafter create a template or blueprint for the online equivalent of the course. This will speed online course creation and the ability for leading institutions to produce up-to-the-minute courseware.”

I’m more sceptical about the next prediction from Evans. He suggests that automatic grading of student work will be sufficiently advanced to determine fair grades for all:

“In addition, AI-enabled course grading will be at highly advanced stage in 5 to 10 years where course administrators will be confident of the fairness and accuracy of automated free-text grading algorithms due to advances in machine learning, semantics and natural language processing. If they have questions about a student’s grade, they will be able to speak with the AI system, understand the rationale, and adjust as and where necessary.”

It’s not so much the prediction about AI development that is problematic – AI is advancing at a pace, and is likely to reach this level soon. Rather, it is the persistent assumption that ‘grading’ is paramount for the measurement of student understanding, and that we should focus the power of AI to support this kind of pedagogy. If teachers are still grading students’ work in 10 years, then AI can indeed remove the tedium of interpreting marking rubrics and wading through pages of written text. However, if we believe that assessment should focus on providing students with constructive criticism and feedback on their work so that they can learn and improve, we will question this. Teachers provide assessment for learning, alongside assessment of learning.

Computers are excellent at storing and processing vast arrays of metrics and performing complex calculations very quickly, but when it comes to providing empathetic, intuitive and personalised feedback to students, teachers will come out on top every time.

Previous posts in this series:
1: Telecommunications
2: Classrooms
3: Music
4: Enhanced vision
5: Robot teachers?
6: Home learning
7: Work
8: Artificial Intelligence
9: Omni-choice learning
10: Cognitive courseware

Our digital future 11: AI enhanced course design by Steve Wheeler was written in Plymouth, England and is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.