When Rating Scales Fall Short: LLM-Assisted Discovery of ADHD Signals in Turkish Teacher Narratives
2026-06-01 • Computation and Language
Computation and Language
AI summaryⓘ
The authors studied how teachers' written stories about students and their structured rating scores each help identify ADHD. They found that the written narratives contain useful information that rating scales might miss, and vice versa, meaning both methods provide different helpful clues. They used a large language model to explore themes in the teacher narratives, uncovering behavior and family-related patterns important for ADHD detection. This suggests that combining these two types of teacher input can improve understanding in diagnosing ADHD.
ADHDConners' Teacher Rating ScaleTeacher narrativesNatural Language ProcessingLarge Language ModelClinical assessmentBehavioral patternsScreening tools
Authors
Baris Karacan, Irem Aktar Songur, Ahmet Ozaslan, Elvan Iseri
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in childhood, and its diagnosis relies on assessments combining clinician judgment with standardized rating scales and reports from parents and teachers. While structured instruments such as the Conners' Teacher Rating Scale-Revised Short Form (CTRS-R:S) quantify ADHD-related behaviors, teachers also provide open-ended narratives that may contain complementary signals not captured by structured assessments. However, it remains unclear to what extent teacher narratives encode signals overlooked by rating scales. In this study, we analyze de-identified Turkish teacher evaluation forms collected during clinical ADHD assessments, including both CTRS-R:S scores and open-ended teacher narratives. We compare predictive signals from structured scores and narrative text and identify cases where structured assessments fail to clearly distinguish ADHD from non-ADHD students while narrative-based models capture distinct behavioral patterns. Notably, these cases show minimal overlap with those missed by the narrative model, suggesting that structured and narrative information encode complementary signals. To interpret these differences, we apply a large language model (LLM)-assisted theme discovery pipeline that reveals distinct attention, behavioral, and family-related patterns, highlighting the potential of natural language processing (NLP) to uncover clinically relevant signals from teacher narratives and to complement traditional ADHD screening tools.