We must help our students navigate large language models (LLMs), such as chatGPT(Illustration photo: Shuterstock / NTB)
Let’s not vilify our students over chatGPT
OPINION: With the advent of ever-more capable artificial intelligence (AI) tools such as chatGPT, we once again have an opportunity to choose a rational, humane future over clinging to an irrational, suspicious past.
StåleEllingsenProfessor, Department of Biological Sciences, University of Bergen.
AnneBjuneProfessor, Department of Biological Sciences, University of Bergen
SehoyaCotnerProfessor, Department of Biological Sciences, University of Bergen
In the miasma
of confusion we as educators are facing regarding large language models, we can be fairly
certain of one thing: these tools will be a part of our current students’
will likely be a transformative part
of their lives, in ways we cannot fully anticipate. Any conscientious educator
will realize what this means: we must help our students navigate LLMs.
Or perhaps it is more accurate
to say that we must help our students
Us versus them?
educational research have indicated what educators should be doing, including co-constructing our curriculum and knowledge with our students (students as
partners); reconsidering our assessments to be more 'authentic', with
significance beyond the classroom; and anticipating change rather than shying
away from it.
In the miasma of confusion we as educators are facing regarding large language models, we can be fairly certain of one thing: these tools will be a part of our current students’ working lives.
of the recent dialogue surrounding chatGPT (and anticipating others such as
Bard and Bing’s tool) has resulted in the following:
It has created an 'us versus
them' dichotomy with our students, essentially vilifying those we are obliged
to assist; and doubled-down on the unwarranted assumption that high-stakes, school exams are our best option for assessment.
cannot reject this particular new technology and still somehow remain relevant
in our spheres of influence.
the student perspective
conducted a survey of students and educators in the Faculty of Mathematics and
Natural Sciences at the University of Bergen, essentially to understand how our
colleagues and students are defining, using, and perceiving LLMs (specifically,
among students include a positive, but balanced view, about chatGPT. Students
also reflect on how chatGPT will affect their learning. One student said:
"Can we cheat using
chatGPT? Then maybe the assessment form needs to be adjusted?"
ChatGPT can be a
useful tool that to a certain extent can be compared to a mathematical
calculator. It streamlines parts of the work, but one still has to use it in
the right way and understand the 'output'.
"Before the end of the 2020's, …
what previously was a literature search will become a conversation with a
virtual assistant that can provide sources etc. Every student will have an AI
research assistant, and they will spend a greater amount of their time thinking
about higher level tasks."
students generally have a more positive attitude about the future of chatGPT
than do educators (see figure 1), while their responses also show various
misunderstandings about the capabilities and limitations of LLM usage,
including a reliance on factual accuracy.
This gap between
reality and perceptions should suggest to instructors an opportunity for in-class discussions and activities involving
LLMs ('is the generated response accurate? If not, why not? How can we best use
these generated responses?').
So, how can we embrace these new LLMs in
Here we offer
some suggestions for constructively working with LLMs in our courses and
First, we can help our students (and
ourselves) understand what these LLMs are. LLMs are pattern-matching algorithms based on
probabilities, not humans in silico
. Their responses address the task of 'what
would an answer look like', not 'what is the answer'.
encourage students to use these tools in our classroom activities, and then
lead a discussion on the information that was provided, its reliability and
accuracy, and whether they can identify likely sources of information.
One of us
developed an in-class activity in which students use chatGPT to figure out how
to do simple bioinformatics tasks like DNA sequence retrieval and analysis.
the students afterwards about the usefullness of such an approach, most
respondents (78 per cent) said it was very useful, followed by 17 per cent rating it as
moderately useful, and none saying it was not useful at all.
In sum, students
seem eager to use these tools constructively, and we have an opportunity to
model for them cautious and critical exploration.
Helping to prepare our students for an uncertain future is an
Second, we should partner with our students. Our students have valuable perspectives, but
also look to us for guidance, and want to be prepared for the future workforce, in or out of academia.
Further, a significant and growing body of literature
on 'students as partners' indicates that this approach leads to enhanced
learning, higher engagement in class, and positive interactions between
students and teachers (Bovill, 2020; Kaur and Noman, 2020; Glessmer and Daae
simple pathway to entry would be to solicit two or three student ambassadors from each course that we teach (Cook-Sather et al., 2019). These individuals
can facilitate communication between students and instructors, help develop
classroom activities, and represent their peers in providing consistent
feedback on the course.
The value of
this type of student feedback will be especially potent as new tools, with new
features, are introduced and incorporated into their (and our) daily lives.
Critically, student partnerships help avoid the 'us versus them' mentality that
characterizes much of the current dialogue around LLMs in education.
An opportunity for better assessments
Third, we should reconsider our assessments. The dominant assessment model in Norwegian
higher education is an end-of-term, high-stakes test that we know is
These exams do an excellent job of telling us which
of our students is a skilled test-taker, but they certainly aren’t good for
overall motivation or learning, and they may exacerbate existing inequities in
education (Högberg and Horn 2022, Salehi et al., 2019).
Much of the
dialogue around 'dealing with' LLMs focuses on how we can retain these exams in
light of anticipated, pervasive cheating. But are we assessing for learning—using assessments to
support student self-correction and deep learning? And are our assessments authentic—do our students create
products (e.g., portfolios) that have relevance beyond the classroom (Harlap et
If we can create assessments informed by principles of constructive
alignment and authenticity, and if we
can communicate this rationale to our students, it should reduce the need to
obsess so much over potential cheating.
Students who want to learn—and who seek
end-of-term deliverables to share with potential employers—will have more
internal motivation to do well on these assessments. And some students will
cheat. Nothing has actually changed.
We have had
opportunities in the past to choose student-centered, evidence-based
instruction that embraces the affordances of modern technology (e.g., the
development of remote instruction during covid).
Some of us failed that test.
With the advent of ever-more capable AI tools such
as chatGPT, we once again have an opportunity to choose a rational, humane
future over clinging to an irrational, suspicious past. And it might be fun!