AI: A Key to Better Classroom Learning
Written by: Ric Campbell, Executive Director, Teacher Leader Institute
The emergence of artificial intelligence (AI) is viewed by many teachers and educational leaders as a challenge to the work of school learning and it presents a problem that undermines the general framework or designs for school learning.
If students can turn to AI resources to complete assignments, how will they learn to think for themselves or develop knowledge that builds a new and deeper understanding of the world?
And beyond school, if AI can take the place of humans across a multitude of jobs, what are we teaching towards?
The challenge of these kinds of questions needs to be seen as an opportunity to pose questions that we, as educators, have been needing to answer since schooling became the preferred, if not fully common, pathway to becoming productive adults and thoughtful citizens. Schooling has consistently failed to meet the aspirational goals that proliferate in educational mission statements, goals that many of us believe are key to achieving the best hopes of mankind. Terms like critical thinking, self-determination, lifelong learning, innovation, problem-solving, and compassion are just a few examples of words that seem to have lost their meaning. They point to educational outcomes that we see with very little frequency.
AI offers us a chance to revisit our goals and anchor them in practices that yield the outcomes we seek. The first step in embracing AI is recognizing that it is a human construct with great power in marshalling huge amounts of data to build knowledge. As such, AI is a powerful partner for learning, providing ideas models and knowledge that learners can interact with to build the very capacities that schools seek to develop. If we position learners (students, teachers, and school leaders) as active agents of their learning, AI becomes a partner in thinking and knowledge-building across the disciplines. But if the spirit of learning is scepticism, if the best kinds of learning are sponsored by the kinds of questions we ask and seek to answer, then it is essential that we recognize that AI must be challenged by the same questions.
The simple example of a student using AI to write an essay requires an iterative process in which the student advances each draft through increasingly focused questions or commands that reflect the evolving sophistication of the student writer as a thinker. There are questions of word choice, tone, coherence, and more. But there are also important questions about the writer’s own opinions and feelings. How much of what I am writing with my AI partner is true to my intentions, my felt sense of what I am trying to say?
In the work of building knowledge and deeper understanding in any discipline, the learner must question what AI has to offer, remembering that AI is a human construct, subject to the biases of the programmers who write the guiding algorithms, as well as the data sources that these algorithms digest. In the teaching of history, for example, we want students to learn how to read, interpret, and evaluate primary sources. This analytic process applies equally to AI as a source and the four major sources of AI bias suggest a menu of questions or heuristics that allow learners to interact meaningfully with AI as yet another partner in the learning community, a partner to be questioned and held accountable.
There is every reason to embrace AI as another agent in classroom learning processes; more responsive than a textbook, it is an affordance we can leverage for deeper learning. As educators, we must simultaneously recognize the limits and possibilities of AI. In exploring and pushing at those limits we learn and grow beyond our limits. In leveraging the possibilities, we take advantage of a source of learning that expands the reach of our learning community. It is time for us to include AI in our curriculum designs and build communities of learners in our classrooms that engage fully with knowledge-making practices that mirror the best practices of the various human disciplines that shape our world. In this, there is much to be learned from the “machine learning” that sustains AI that can be applied to the cognitive and affective tasks that define school learning.
