While artificial intelligence (AI) has been a relatively silent partner in higher education’s early warning systems, personalized learning platforms, and more for some time now, we might fairly say that ChatGPT is the big boom heard ‘round the university. The AI chatbot is taking many of us by surprise and startling more of us to attention, not in small measure by its charming, eager extroversion: it “talks” to us. What’s happening here? Is ChatGPT a threat? What happens next?
Diffusion of ChatGPT
ChatGPT has been quite the busybot, going to business school, law school, the office, Congress, and more. We are experiencing the unfolding of Rogers’ (1962) innovation diffusion in real time. Since OpenAI released ChatGPT to the public in research preview on November 30, 2022, we’ve been busy ourselves, curating links and disseminating our treasuries to each other. We’re also creating artifacts such as the Advancements in AI Timeline developed by the Center for eLearning Initiatives at Penn State Behrend. The twin goals of all of our awareness-building activities are to hasten the development of our individual and collective opinions about whether ChatGPT is aide or adversary and to decide our next steps accordingly.
The truth is that the AI is a flawed facsimile of human intelligence, but depending on the task before it, it can be a remarkably capable one. For that reason, we’ve been putting it to fledgling use. The danger lies in the risk of distilling our efforts into empty “best practices” rather than informed recommendations because we’re building the empirical evidence as we fly. Not everyone is aware that Artificial Intelligence in Education (AIEd) is a decades-old field of study. (The term artificial intelligence was coined in 1955 by Dartmouth Professor of Mathematics John McCarthy, and the International Journal of Artificial Intelligence in Education published its inaugural issue in 1989.) The historically limited capabilities of the field’s principal subject of study may have contributed to what could be characterized as its stunted growth, until most recently.
Now as the big boom continues to echo, we’ll begin to subject our theories to empirical examination, and as practical evidence mounts, we’ll then be better equipped to confirm whether we’ve decided correctly.
An “Objective” View
ChatGPT’s name refers to one type of neural network machine-learning model, but many different AI models “generate” new information as they respond to a prompt. The word generate is often found in lists of suggested verbs that many find useful for crafting learning objectives. Such lists are based on Bloom’s Taxonomy, a framework originally designed and later revised and updated to provide a common vocabulary for educational assessment. Interestingly, the highest level of Bloom’s Revised Taxonomy is to create. Under create on verb lists, generate typically appears as one of many possible ways to demonstrate creation. Both verbs have Latin roots; both impart a sense of bringing something into being that did not previously exist. However, generate conveys a cause-and-effect, even mechanistic, process while create is evocative of a sense of growth, of development, of invention, of imagination, of the extraordinary.
Human-generated responses to ChatGPT might discuss its ethics, debate the legal concerns, rewrite our academic integrity policies, and brainstorm ideas for implementing it in our classes. Amusingly, ChatGPT can generate these, too, given the right prompts. But what can ChatGPT not do? At present, ChatGPT cannot access video content or refrain from hallucinating references, though both of these shortcomings may not exist for much longer. ChatGPT cannot see, smell, hear, taste, or touch. It cannot conduct research, observe results, or perform demonstrations. It cannot interrupt; it waits. It cannot initiate; it responds. It cannot think; it computes based on algorithms. It cannot let its mind wander through the stages of creative process because it has no mind. In short, it simply generates.
The pedagogical implications are clear. If all our assignments need to be revamped in order to mitigate the use of ChatGPT to complete them, can it mean that our educational system has taught students merely to generate rather than operate within the wider context and to the full extent of creation? It is currently impossible to know with certainty what is happening inside a learner’s head. To assess, we ask students to generate an output that serves as proxy evidence for cognitive activity. Have we been confusing a generated output with creation all along?
What’s Next
The same technology sector that has led current AI innovations will continue to bring ideas to market. Imagine a cottage industry to guarantee written products are created by human intelligence. Local agents might offer “100% Human Intelligence” certification guarantees. Perhaps in the future, writers will be surety bonded as an additional layer of protection to reputations. The next edtech company could produce an assessment platform that enables easy oral video examinations. Textbook companies may already be planning to train a bot on a textbook to offer as a companion product. Imagine we, the adopters of those texts, could purchase such a textbook bot at the course level with a “personalization” option that allows us to further train it on our syllabus. I do not currently foresee a full in loco magister state of affairs. There is too much money to be made to replace us, the potential consumers of such tools.
What I hope to see is an explosion of studies in the scholarly literature of AIEd to inform sound classroom practice. I dream of a spontaneity movement in education that not only encourages but expects students to interrupt and initiate—and they learn to reawaken those natural urges. I yearn for a supratheory of learning. If we leave the generation tasks to ChatGPT, we can focus on implementing immediate and local changes to someday create futures like these.
Consider engaging in the scholarship of teaching and learning in your classroom. Partner with an instructional designer who will support and extend your efforts and who will think deeply with you outside of the boxes that can impede our visions. Together, we can explore alternative teaching models, strategies, and methods. Perhaps we’ll create a new educational paradigm.
Related:
How to build the AI workforce through accessible education
This article was created entirely by human intelligence.
Dr. Lee Ann Dickerson is an instructional designer at Penn State Erie, The Behrend College.
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