Though we've been expecting AI to get good enough at writing to actually perform a decent number and variety of writing tasks "by itself" (it still needs humans to engineer prompts), we hadn't been confronted with that reality until recently, when ChatGPT became more widely used. To test it out, I gave it some of the (relatively simple) prompts I give my undergraduate film history students and saw what it came up with. Then, I asked it to write a pilot episode of a teen drama set in Montana, a grant proposal for a research study on social media use and political polarization, a research paper on the environmental impact of microplastics, a quiz on 1970's media cultural studies, a review of Beck's new album, an amicus brief on microplastics, and a summary of the 1998 NBA Finals in the style of Proust. The results, as many have noted, aren't perfect but are a lot closer to perfect than what we're used to seeing from programs. It's hard to objectively convey the quality of the output and the limitations of the tool; it's best to just try it out yourself.
I'm sure such tools will improve, but it's hard to say at what rate and to what degree, so I'll try to just stick to evaluating the impact of the current iteration of generative text programs like ChatGPT on two facets of writing: assessment (specifically, educators' ability to assess students' understanding of concepts, thinking, and communication skills) and its applications in various domains (i.e., journalism, creative writing, scientific writing).
In terms of assessment, the ability of students to avoid actually doing the work of writing for an assignment and to avoid detection has increased significantly. However, the truth of this statement is highly dependent on the assignment. If the assignment just requires students to gather information (particularly older info on an extensively covered topic, person, or event) and synthesize it into a simple, readable essay or article, then ChatGPT seems to be quite good at that. But what about, say, watching a particular video on YouTube and applying something from a particular reading to that video? I would assume that ChatGPT can't easily extract the information that's in the video, so it would have a tough time with this task, and that it's ability to do anything with a particular reading is contingent upon that reading being freely available and accessible online somewhere. If the reading is in a textbook that doesn't appear online (or only appears on a few hard-to-access pirating sites), then this will be harder for ChatGPT to work with than, say, the Magna Carta or any other text that's in the public domain. But in any of these cases, I think that A.I. co-authoring (where ChatGPT does some of the work and the student changes and adds a few things) will be essentially impossible to detect.
Another approach to ChatGPT's threat to assessment is to simply proctor writing assignments. That's easier to do for short writing assignments but difficult for longer ones, and impractical for online classes. Still, as a professor who has an interest in fairly and accurately assessing his student's writing, I'm leaning toward more in-class writing assignments and fewer outside-of-class writing assignments because of how challenging it will be to tell the difference between A.I.-generated writing and my students' writing outside the classroom.
But the topic of applications of AI is more interesting to me, and in some sense pre-empts questions about assessment. If AI can accomplish much of what we're trying to train our students to accomplish, what is the point of such training? The rapid adoption of ChatGPT has forced me to reconsider the relationship between writing and thinking. Writing is often the medium through which thought is expressed, though not the only one; spontaneous discussions and conversation are a good alternative. When is writing chiefly a vehicle for thought and when is it more an integral part of the process of thought? Those who defend the sanctity of human writing, I surmise, would say either that writing is very often part of thinking, or that we may not be very good at being able to tell the difference between writing-as-vehicle-for-thought and writing-as-thinking. Therefore, ceding the task of any but the most formulaic types of writing to AI risks sacrificing the ability to think.
We still want to get better at thinking, and we still want our students to get better at thinking (e.g., critical thinking, creative thinking, logical reasoning, problem solving). Even in a world of prevalent AI, having those abilities will make us better at prompt engineering, better at judging the value of what ChatGPT and similar AI spit out, and better at making refinements to that output. The same would apply for non-linguistic AI: prompting an AI architectural design program and judging/refining the results; prompting an AI coding program and judging/refining the results. In any application I can think of, humans are still part of the process. Whether AI is producing a kind of structural scaffold for ideas, identifying sources of information, or generating new ideas, it has to be set in motion, its output needs to be evaluated, and in its current iteration, its output requires a fair amount of refinement. That process of prompting and refining can be similar to a good conversation or collaborative improvisation ("yes, and..." thinking).
I sense that there's a widespread antipathy toward applying AI in creative domains. I don't consider myself an AI optimist. I acknowledge how tempting it will be for students, businesses, creators, and governments to use AI in an indiscriminate manner, not bothering to vet or refine its output. I acknowledge that such use could cultivate a kind of passivity and (further) overreliance on technology. More people would get out of the habit of "thinking for themselves." Creativity would be increasingly derivative in the name of efficiency.
But does this warrant an outright ban on AI? What would such an approach sacrifice?
I feel as though we've been good at articulating the risks of AI (aside from the aforementioned, there's scaling up spam and disinformation, and deceiving job recruiters or potential partners on dating sites). We're less good at articulating its potential upside. I think of that upside chiefly in terms of resource reallocation (which, yes, sounds very cold and business-speaky, but hear me out).
Let's treat ChatGPT and the like as co-authors. They generate first drafts or templates but don't write the entire text or finished product. Human writers' role in the creative process shifts from crafting sentences and paragraphs to gathering primary original sources and identifying needs in their audiences and communities. What if writers could take the time they would have spent struggling to write formulaic copy and instead spent it cultivating twice as many primary sources as they would have otherwise, or co-writing twice as many stories, or ten times as many stories? Consider all the un-written stories or accounts of events; consider what's missing from historical accounts and news coverage, in part because the people who know how to create thorough and entertaining writing only have so much time. Some people's stories are told, some people and events are remembered, but the vast majority are forgotten. What if we could fill in some of those blanks? We don't have to be indiscriminate in our use of AI to turn data into stories that no one cares to read. Remember, humans and their understanding of what is valued are still part of the process. And what if we could offer those stories in twice as many accessible formats? If editors and readers valued these qualities more than they feared AI's downsides, we could use AI to improve our information environment.
The genie is out of the bottle. If one country or group of people wants to spend time trying to stuff the genie back into the bottle, that won't stop other countries or groups of people from using it. We needn't be totalizing in how we conceive of the application of AI in creative domains. We have been discriminate in our use of technologies in the past, often making mistakes, attempting to correct those mistakes by introducing limitations or redesigning the technology, fitfully moving along but rarely un-inventing anything. Maybe we should look around and see where the successes and failures are right now.
