Gilan M. El Saadawi|Eugene Tseytlin|Elizabeth Legowski|D.M. Jukic|Melissa Castine|Jeffrey L. Fine|Robert Gormley|Rebecca S. Crowley
A natural language intelligent tutoring system for training pathologists: implementation and evaluation
University of Pittsburgh|University of Pittsburgh|University of Pittsburgh|University of Pittsburgh|University of Pittsburgh|University of Pittsburgh|University of Pittsburgh|University of Pittsburgh
developed evaluated natural language interface nli intelligent tutoring system diagnostic pathology system teaches residents examine pathologic slides write accurate pathology reports providing immediate feedback errors make slide review diagnostic reports residents ask help point case receive contextspecific feedbackwe evaluated 1 performance natural language system 2 effect system learning 3 effect feedback timing learning gains 4 effect reporttutor performance selfassessment correlationsthe study uses crossover 2 x 2 factorial design recruited 20 subjects 4 academic programs subjects randomly assigned one four conditionstwo conditions immediate interface two delayed interface expert dermatopathologist created reference standard 2 board certified apcp pathology fellows manually coded residents assessment reports subjects given opportunity self grade performance used survey determine student response interfacesour results show highly significant improvement report writing one tutoring session 4fold increase learning gains interfaces effect feedback timing performance gains residents used immediate feedback interface first experienced feature learning gain correlated number cases viewed correlation performance selfassessment either condition