With the popularity and extraordinary, and dubious, claims
of the ability of AI (Narayanan & Kapoor, 2024), a heightened concern about
students cheating has occurred. Naturally, proctoring and anti-plagiarism
companies benefit from this fear and the sales of their ‘solutions’
increase. The problem with this policing
solution is that beyond a point, teachers get into an ‘arms race’ between AI
and AI-detection software where the real loser is the institution footing the
bill. One might think that the companies
are happy to ‘stir the pot’ to increase anxiety for the benefit of their
shareholders. Meanwhile, research has indicated that the level of academic
dishonestly has not changed with the prevalence of AI (Lee, et al, 2024) Yet
the focus on student policing places instructors in an adversarial relationship
with the students, instead of an instructive one.
A Better Solution
Low-stakes assignments have the added benefit of deterring
academic dishonesty. Because there is
less risk involved in the assignment, there is less incentive to cheat, which
could bring about sever consequences.
Why take the risk, when there is so little to gain?
Low-stakes assignments, such as threaded assignments, thwart
using AI that quickly generates content.
The low-stakes assignments act as scaffold that requires further
reflection and meta-cognitive skills that are not easily replicated by the
stochastic language models modern AI uses. This will force the would-be culprit
to reflect and expend so much energy producing something to submit that it
isn’t worth the effort to get whatever the AI model can produce.
When assignments:
- offer less stress,
- supply tools for the students to succeed,
- clearly express expectations, and
- encourage the student to take control of their learning,
Low-stakes assignments engage the students in the learning
process. The best way to eliminate
academic dishonestly is to remove the incentive to cheat and be up-front about
the rules. Students are often ‘cheating’
because often they have not received adequate instruction and expectations
(Waltzer, Bareket-Shavit & Dahl, 2023).
To solve this, explicitly state the acceptable level of AI usage and you
will curb the level of unintended violations to your academic expectations. A
group of scaffolded low stakes assignments, often scaffolded to create a large
assignment, then undermines the pay-off from cheating.
References
Futterman, K (2024) Zeitgeist 6.0: Results of the Campus sixth-annual student-body survey. The Middlebury Campus. Dec 13.
Lee, V., Pope, D., Miles, S, and R. Zarate (2024) Cheating in the age of
generative AI: A high school survey study of cheating behaviors before and
after the release of ChatGPT. Computers and Education: Artificial
Intelligence. Vol 7, December.
Losey, R. (2024) Stochastic
Parrots: How to tell if something was written by an AI or a human?
Mintz, S (2023) 10 Ways to
Prevent Cheating. Inside Higher Ed. February 16.
Mollick, E (2023) Centaurs and Cyborgs on the Jagged Frontier: I think we have an answer on whether AIs will reshape work. One Useful Thing. Sep. 16.
Narayanan, A & S. Kapoor (2024) AI Snake Oil: What artificial
Intelligence can do, what it can’t, and how to tell the difference. Princeton
University Press: Princeton, NJ.
Waltzer, T., Bareket-Shavit, C., & Dahl, A. (2023). Teaching the What, Why, and How of
Academic Integrity: Naturalistic Evidence from College Classrooms. Journal
of College and Character, 24(3), 261–284.
Wehlburg, K (2021) Assessment
design that supports authentic learning (and discourages cheating) Times
Higher Education. Nov 24
