The use of artificial intelligence to detect students' sentiments and emotions in gross anatomy reflections

Abstract

Students' reflective writings in gross anatomy provide a rich source of complex emotions experienced by learners. However, qualitative approaches to evaluating student writings are resource heavy and timely. To overcome this, natural language processing, a nascent field of artificial intelligence that uses computational techniques for the analysis and synthesis of text, was used to compare health professional students' reflections on the importance of various regions of the body to their own lives and those of the anatomical donor dissected. A total of 1365 anonymous writings (677 about a donor, 688 about self) were collected from 132 students. Binary and trinary sentiment analysis was performed, as well as emotion detection using the National Research Council Emotion Lexicon which classified text into eight emotions: anger, fear, sadness, disgust, surprise, anticipation, trust, and joy. The most commonly written about body regions were the hands, heart, and brain. The reflections had an overwhelming positive sentiment with major contributing words “love” and “loved.” Predominant words such as “pain” contributed to the negative sentiments and reflected various ailments experienced by students and revealed through dissections of the donors. The top three emotions were trust, joy, and anticipation. Each body region evoked a unique combination of emotions. Similarities between student self‐reflections and reflections about their donor were evident suggesting a shared view of humanization and person centeredness. Given the pervasiveness of reflections in anatomy, adopting a natural language processing approach to analysis could provide a rich source of new information related to students' previously undiscovered experiences and competencies.