Using Language Models to Detect Alarming Student Responses
This addresses student safety by enhancing threat detection in educational assessments, but it appears incremental as it builds on existing systems.
The paper tackles the problem of detecting alarming student responses, such as threats or mental health risks, using a fine-tuned language model, resulting in a substantial improvement in accuracy over previous system iterations.
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.