Co-Authorship and Co-Ownership: The End of the Human-AI Binary

A. Laura Stef-Praun, PhD

03-19-2026

I started my lecture on AI and writing with a creativity exercise for my students. Holding up a paper clip, I asked, “How many uses can you list for this paper clip? You can use it for anything, just think broadly and creatively.”

As a group, we came up with 20 uses in about 5 minutes. Yet, if you ask an extroverted five-year-old, they will go on and invent many more uses, such as hanging a unicorn on a rainbow. When you enter this question in ChatGPT, you get 100 uses in a few seconds with the additional offer “If you want, I can push this past 200+ ideas.”

What’s happening? As people, we become increasingly specialized, educated and trained as our lives evolve. As a result, our creativity decreases and the curse of knowledge kicks in, making everyone think in boxed-in ways, contained within the paradigmatic frameworks that we all acquired through our institutionalized education. That’s why a five-year-old can be more creative than an entire class of college students.

Now, what if the paper clip assignment had been a take-home assignment? Would students have come up with ideas on their own, or would they have used AI, immediately to get more uses, faster, and also — a better grade? This is a very likely scenario and one that many instructors think about at this crossroads in higher education. What do students learn for themselves? And what part of their learning do they outsource to an AI?

The problem is that AI is just a bigger box, but it is still a box. The question that I pose to my students is, “Are you going to let this box (an AI) be your main paradigm or frame for your entire intellectual product? Or are you going to use it as an amplifying boombox for your ideas?”

When it comes to AI use and intellectual contributions, two issues come to mind: 1) the conceptualization of original work (hence, the connection with creativity) and 2) its counterpart, plagiarism (cheating, appropriating the work of others).

As teachers, where we stand on these issues matters to students because we model: fear versus courage, ethical action versus groupthink; and, through our action or inaction as college instructors, we are preparing our students to face their future as the next workforce generation.

Let’s be honest: the separation between AI and human authorship is over, and the future will require us to reframe our understanding of original contributions. In many fields, if not all, everyone will operate on a human-AI collaboration continuum. In the future, the only differentiator in various academic or industry contributions will be the human percentage of the collaboration versus the AI percentage. The main field-specific topic for debate will be what is acceptable, ethical and practical.

So, today, we need to admit that our task is no longer to decide whether AI belongs in the classroom because we no longer have full control over that decision. Rather, we need to adopt the learning objective of teaching students how to engage with AI deliberately and reflectively, in order to reach a comfortable and ethical position on this human/AI authorship spectrum.

We can encourage critical thinking while using AI and give students a safe space to practice, which will motivate them and make them the least likely to cheat, while also understanding how to avoid plagiarizing with AI.

In doing this, we will not only allow students to feel agency, but also encourage them to see clear connections between classroom work and their future careers. By modeling our own AI use, teaching prompt engineering as a critical thinking practice, we replace fear of plagiarism accusations and policing the classroom with ownership and accountability. Ultimately, that is the kind of preparation their future workplaces will demand.