Efficacy of a web-based intelligent tutoring system for communicating genetic risk of breast cancer: a fuzzy-trace theory approach

Abstract

Background: . Many healthy women consider genetic testing for breast cancer risk, yet BRCA testing issues are complex.

Objective: . To determine whether an intelligent tutor, BRCA Gist, grounded in fuzzy-trace theory (FTT), increases gist comprehension and knowledge about genetic testing for breast cancer risk, improving decision making.

Design: . In 2 experiments, 410 healthy undergraduate women were randomly assigned to 1 of 3 groups: an online module using a Web-based tutoring system (BRCA Gist) that uses artificial intelligence technology, a second group read highly similar content from the National Cancer Institute (NCI) Web site, and a third that completed an unrelated tutorial.

Intervention: . BRCA Gist applied FTT and was designed to help participants develop gist comprehension of topics relevant to decisions about BRCA genetic testing, including how breast cancer spreads, inherited genetic mutations, and base rates.

Measures: . We measured content knowledge, gist comprehension of decision-relevant information, interest in testing, and genetic risk and testing judgments.

Results: . Control knowledge scores ranged from 54% to 56%, NCI improved significantly to 65% and 70%, and BRCA Gist improved significantly more to 75% and 77%, P < 0.0001. BRCA Gist scored higher on gist comprehension than NCI and control, P < 0.0001. Control genetic risk-assessment mean was 48% correct; BRCA Gist (61%) and NCI (56%) were significantly higher, P < 0.0001. BRCA Gist participants recommended less testing for women without risk factors (not good candidates; 24% and 19%) than controls (50%, both experiments) and NCI (32%), experiment 2, P < 0.0001. BRCA Gist testing interest was lower than in controls, P < 0.0001.

Limitations: . BRCA Gist has not been tested with older women from diverse groups.

Conclusions: . Intelligent tutors, such as BRCA Gist, are scalable, cost-effective ways of helping people understand complex issues, improving decision making.

Keywords: Fuzzy-Trace Theory; Intelligent Tutoring System; breast cancer risk; genetic testing.

© The Author(s) 2014.

Figures

Figure 1

Screen shot of BRCA Gist avatar explaining the relationship between incidence of breast cancer and BRCA mutations using a figure grounded in fuzzy-trace theory.

Figure 2

Mean percent rated “should be tested” by risk level, condition, and experiment (error bars for 95% confidence intervals).