Researchers have published a study in the journal Psychiatric Quarterly wherein they have shown that it is possible for artificial intelligence to predict which students are at higher risk of perpetrating school violence.
The researchers found that machine learning is as accurate as a team of child and adolescent psychiatrists, including a forensic psychiatrist, in determining risk for school violence. Scientists are quick to point out that while they were able to predict school violence using AI, they haven’t gathered outcome data to assess whether machine learning could actually help prevent school violence. This is something they will be doing next.
Researchers evaluated 103 teenage students in 74 traditional schools throughout the United States who had a major or minor behavioral change or aggression toward themselves or others. The students were recruited from psychiatry outpatient clinics, inpatient units and emergency departments.
The team performed school risk evaluations with participants. Audio recordings from the evaluations were transcribed and manually annotated. The students, as it turned out, were relatively equally divided between moderate- to high-risk, and low-risk, according to two scales that the team developed and validated in previous research.
There were significant differences in total scores between the high-risk and low-risk groups. The machine learning algorithm that the researchers developed achieved an accuracy rate of 91.02 percent, considered excellent, when using interview content to predict risk of school violence. The rate increased to 91.45 percent when demographic and socioeconomic data were added.
Scientists point out that the machine learning algorithm was almost as accurate in assessing risk levels as a full assessment by the research team, including gathering information from parents and the school, a review of records when available, and scoring on the two scales that were developed by the team.
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