Purpose: To develop and test the efficacy of a web-based Intelligent Tutoring System (ITS) based on fuzzy-trace theory (FTT) that engages women in a tutorial dialogue to help them understand and make decisions about genetic testing for breast cancer risk.
Methods: This interactive tutorial of about one hour appears to be the first use of an ITS in medical decision-making. Tutorial dialogues address questions such as, "what should someone do if she finds out that she has inherited an altered BRCA gene?" Using a set of "expectations texts" and Latent Semantic Analysis, a conversational agent (avatar) tries to "understand" what participants are saying and respond appropriately. Information pertaining to breast cancer and genetic risk was taken from the National Cancer Institute (NCI) web site, and vetted by medical experts. Three female avatars appearing to be of varying ethnicities present the information orally, visually, in brief video clips and in writing. The figure is a screen shot from the tutorial. The efficacy of the ITS was tested in a randomized, controlled experiment equating time on task. Participants were randomly assigned to one of three conditions: the ITS; studying pages from the NCI web site covering comparable materials; or studying irrelevant information (control). Participants were then given two tests of declarative knowledge about breast cancer and genetic risk, and twelve scenarios applying their knowledge assessing breast cancer risk. These tasks were first pilot tested and vetted by medical experts.
Results: In two tests of declarative knowledge about breast cancer, one from the research literature, and one on the NCI web site content, participants in the ITS group scored significantly higher than both comparison groups. The NCI group also scored significantly higher than the control group. Effect sizes are considered large following Cohen's conventions. Participants assessed breast cancer risk on twelve scenarios providing gist-based ordinal rankings (low, medium, high) of breast cancer including conditional probabilities. A multiple signal detection theory analysis provided independent measures of sensitivity to risk, (d') and criteria for distinguishing among risk levels. The ITS group was significantly more sensitive in distinguishing among all levels of risk than the control group.
Conclusions: This ITS may be fruitfully applied in educating laypeople and assisting their medical decision-making by enhancing gist-based comprehension and reducing class-inclusion interference.
