Hello, my name is Silas. And my name is Tiffany.
Tiffany and I are the first authors of this work. So let’s jump in.
Computers grading students’ work is nothing new
but nowadays deployments of AI graders have become popular
especially systems that automatically grade and give feedback for written work.
This has happened on the well-known GRE, and in various states in the US.
These autograders generally match humans in accuracy.
And there’s no denying it can save a lot of work.
But just because something works well on paper doesn’t mean people will accept it.
Both humans and AI make mistakes
but trust in an algorithm drops especially fast when it makes mistakes.
There’s fairness concerns -- we have no shortage of stories about AI discrimination.
And, people will form folk theories of how algorithms operate
and adjust their behavior to match.
In education, folk theories may impact how
students answer questions or even gaming attempts.
Given these phenomena, the important question nowadays is less of “Can AI grading be done?”
and more “How should it done?”
The automated classroom ecosystem currently lacks insights into this crucial question.
Faced with this question, we studied a short-answer AI autograder.
We asked students about how they thought the autograder worked and their answer construction strategies.
We examined their various attitudes and what explained those attitudes.
And based on our findings, we propose guidelines
for deploying imperfect AI autograders.
Here’s the autograder we studied.
In this intro CS class, students described a block of code in plain English
and their descriptions were automatically graded.
In both homework and exams, right and wrong submissions,
exemplar answers would always appear as feedback.
Students could appeal decisions they thought were made in error.
The homework allowed unlimited attempts, but exams only allowed one attempt.
The grader wasn’t perfect. Around 10% of correct answers were marked wrong.
And around 15% of wrong answers were marked right.
Notably, in terms of raw grading accuracy, humans matched the autograder.
We gathered the experiences of 71 students through surveys and interviews.
We then used linear regression to find associations between the student attitudes under study
and used a grounded-theory approach to uncover themes in the qualitative data.
Now, we will present some result highlights.
First, folk theories. A majority of students thought the algorithm looked for keywords.
However, only a few of them expressed details of how this matching was performed.
Misalignment between folk theories and how the autograder actually worked could lead
to suboptimal answer construction strategies.
For example, a student reported they wrote long answers thinking that it had a higher
chance to hit more keywords, like in this contrived example.
But they were not aware that the system assigned negative weights to certain words
and longer answers had more of a chance to hit those words too.
Next, we examined students’ perceived autograding accuracy,
fairness, educational value, and satisfaction towards the autograder,
all expected to be related to their acceptance of the autograder.
We found that students overall overestimated the likelihood of false negatives,
or correct answers being graded as wrong,
and many of them had not considered the possibility of wrong answers being graded as correct
but still thought they [false positives] were harmful to learning.
At the same time, higher perceived false negative rates
statistically predicted lower perceptions of fairness
and lower satisfaction on exams.
From our findings, we propose several classroom deployment guidelines.
First, instructors should allow students to practice with the autograder
in a low-stake setting before high-stake deployment
and prepare robust error management processes for high-stake deployments.
Second, transparency could dispel harmful folk theories and improve students’ attitudes
but finding the proper messaging is critical and still an open question.
Third, we shouldn’t focus only on minimizing false negatives to make students happier
if it increases false positives, which could be harmful to learning.
We need to consider both the impact on learning and students’ attitudes
when we make tradeoffs on these error rates.
In conclusion, our paper discusses guidelines on how to deploy an AI autograder.
But even with our tips, you should first consider:
“Should I deploy an AI autograder?”
a critical question raised in a recent scandal in the UK.
Educators should carefully consider the ability and resources to address errors,
algorithm aversion, fairness concerns, and more.
We hope our work encourages further discussion
on the deployment of AI in a manner considerate of students’ needs.
Thanks to our participants, NSF and CapitalOne for funding.
And thank you for listening!