Using Language Models to Detect Alarming Student Responses
Christopher M. Ormerod Milan Patel Harry Wang
This article details the advances made to a system that uses artificial
intelligence to identify alarming student responses. This system is built into
our assessment platform to assess whether a student's response indicates they
are a threat to themselves or others. Such responses may include details
concerning threats of violence, severe depression, suicide risks, and
descriptions of abuse. Driven by advances in natural language processing, the
latest model is a fine-tuned language model trained on a large corpus
consisting of student responses and supplementary texts. We demonstrate that
the use of a language model delivers a substantial improvement in accuracy over
the previous iterations of this system.