Colin L. Widmer

Efficacy of a Web-based Intelligent Tutoring System for Communicating Genetic Risk of Breast Cancer: A Fuzzy-Trace Theory Approach

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.

The development and analysis of tutorial dialogues in AutoTutor Lite

The goal of intelligent tutoring systems (ITS) that interact in natural language is to emulate the benefits that a well-trained human tutor provides to students, by interpreting student answers and appropriately responding in order to encourage elaboration. BRCA Gist is an ITS developed using AutoTutor Lite, a Web-based version of AutoTutor. Fuzzy-trace theory theoretically motivated the development of BRCA Gist, which engages people in tutorial dialogues to teach them about genetic breast cancer risk. We describe an empirical method to create tutorial dialogues and fine-tune the calibration of BRCA Gist’s semantic processing engine without a team of computer scientists. We created five interactive dialogues centered on pedagogic questions such as “What should someone do if she receives a positive result for genetic risk of breast cancer?” This method involved an iterative refinement process of repeated testing with different texts and successively making adjustments to the tutor’s expectations and settings in order to improve performance. The goal of this method was to enable BRCA Gist to interpret and respond to answers in a manner that best facilitated learning. We developed a method to analyze the efficacy of the tutor’s dialogues. We found that BRCA Gist’s assessment of participants’ answers was highly correlated with the quality of the answers found by trained human judges using a reliable rubric. The dialogue quality between users and BRCA Gist predicted performance on a breast cancer risk knowledge test completed after exposure to the tutor. The appropriateness of BRCA Gist’s feedback also predicted the quality of answers and breast cancer risk knowledge test scores.

Tutorial dialogues and gist explanations of genetic breast cancer risk

The intelligent tutoring system (ITS) BRCA Gist is a Web-based tutor developed using the Shareable Knowledge Objects (SKO) platform that uses latent semantic analysis to engage women in natural-language dialogues to teach about breast cancer risk. BRCA Gist appears to be the first ITS designed to assist patients’ health decision making. Two studies provide fine-grained analyses of the verbal interactions between BRCA Gist and women responding to five questions pertaining to breast cancer and genetic risk. We examined how “gist explanations” generated by participants during natural-language dialogues related to outcomes. Using reliable rubrics, scripts of the participants’ verbal interactions with BRCA Gist were rated for content and for the appropriateness of the tutor’s responses. Human researchers’ scores for the content covered by the participants were strongly correlated with the coverage scores generated by BRCA Gist, indicating that BRCA Gist accurately assesses the extent to which people respond appropriately. In Study 1, participants’ performance during the dialogues was consistently associated with learning outcomes about breast cancer risk. Study 2 was a field study with a more diverse population. Participants with an undergraduate degree or less education who were randomly assigned to BRCA Gist scored higher on tests of knowledge than those assigned to the National Cancer Institute website or than a control group. We replicated findings that the more expected content that participants included in their gist explanations, the better they performed on outcome measures. As fuzzy-trace theory suggests, encouraging people to develop and elaborate upon gist explanations appears to improve learning, comprehension, and decision making.

Understanding genetic breast cancer risk: Processing loci of the BRCA Gist Intelligent Tutoring System

The BRCA Gist Intelligent Tutoring System helps women understand and make decisions about genetic testing for breast cancer risk. BRCA Gist is guided by Fuzzy-Trace Theory, (FTT) and built using AutoTutor Lite. It responds differently to participants depending on what they say. Seven tutorial dialogues requiring explanation and argumentation are guided by three FTT concepts: forming gist explanations in ones own words, emphasizing decision-relevant information, and deliberating the consequences of decision alternatives. Participants were randomly assigned to BRCA Gist, a control, or impoverished BRCA Gist conditions removing gist explanation dialogues, argumentation dialogues, or FTT images. All BRCA Gist conditions performed significantly better than controls on knowledge, comprehension, and risk assessment. Significant differences in knowledge, comprehension, and fine-grained dialogue analyses demonstrate the efficacy of gist explanation dialogues. FTT images significantly increased knowledge. Providing more elements in arguments against testing correlated with increased knowledge and comprehension.

A signal detection analysis of gist-based discrimination of genetic breast cancer risk.

Pervasive biases in probability judgment render the probability scale a poor response mode for assessing risk judgments. By applying fuzzy trace theory, we used ordinal gist categories as a response mode, coupled with a signal detection model to assess risk judgments. The signal detection model is an extension of the familiar model used in binary choice paradigms. It provides three measures of discriminability—low versus medium risk, medium versus high risk, and low versus high risk—and two measures of response bias. We used the model to assess the effectiveness of BRCA Gist, an intelligent tutoring system designed to improve women’s judgments and understanding of genetic risk for breast cancer. Participants were randomly assigned to the BRCA Gist intelligent tutoring system, the National Cancer Institute (NCI) Web pages, or a control group, and then they rated cases that had been developed using the Pedigree Assessment Tool and also vetted by medical experts. BRCA Gist participants demonstrated increased discriminability for all three risk categories, relative to the control group; the NCI group showed increased discriminability for two of the three levels. This result suggests that BRCA Gist best improved discriminability among genetic risk categories, and both BRCA Gist and the NCI website improved participants’ ability to discriminate, rather than simply shifting their decision criterion. A spreadsheet that fits the model and compares parameters across the conditions can be downloaded from the Behavior Research Methods website and used in any research involving categorical responses.

Pumps and Prompts for Gist Explanations in Tutorial Dialogues About Breast Cancer

Fuzzy-Trace Theory (FTT) generalizes research on discourse to predict how health messages can be better understood and remembered, thereby influencing decision making. Applying FTT, BRCA Gist delivers messages interactively through tutorial dialogues and is the first Intelligent Tutoring System designed to help laypeople make sound medical decisions. Previous research indicates that BRCA Gist helps people form useful “gist explanations,” which leads to improved knowledge, comprehension, and risk assessment. The present research examined the effectiveness of different BRCA Gist dialogue moves, including general pumps for information and prompts for specific information. Participants were randomly assigned to a control group or one of four BRCA Gist conditions evoking gist or verbatim representations crossed with general pumps or specific information prompts. Gist-evoking pumps by themselves produced significant gains in knowledge and risk assessment. Specific verbatim prompts increased knowledge without affecting risk assessment. Results are explained in light of memory research and FTT principles.

Active engagement in a web-based tutorial to prevent obesity grounded in Fuzzy-Trace Theory predicts higher knowledge and gist comprehension

We used Sharable Knowledge Objects (SKOs) to create an Intelligent Tutoring System (ITS) grounded in Fuzzy-Trace Theory to teach women about obesity prevention: GistFit, getting the gist of healthy eating and exercise. The theory predicts that reliance on gist mental representations (as opposed to verbatim) is more effective in reducing health risks and improving decision making. Technical information was translated into decision-relevant gist representations and gist principles (i.e., healthy values). The SKO was hypothesized to facilitate extracting these gist representations and principles by engaging women in dialogue, “understanding” their responses, and replying appropriately to prompt additional engagement. Participants were randomly assigned to either the obesity prevention tutorial (GistFit) or a control tutorial containing different content using the same technology. Participants were administered assessments of knowledge about nutrition and exercise, gist comprehension, gist principles, behavioral intentions and self-reported behavior. An analysis of engagement in tutorial dialogues and responses to multiple-choice questions to check understanding throughout the tutorial revealed significant correlations between these conversations and scores on subsequent knowledge tests and gist comprehension. Knowledge and comprehension measures correlated with healthier behavior and greater intentions to perform healthy behavior. Differences between GistFit and control tutorials were greater for participants who engaged more fully. Thus, results are consistent with the hypothesis that active engagement with a new gist-based ITS, rather than a passive memorization of verbatim details, was associated with an array of known psychosocial mediators of preventive health decisions, such as knowledge acquisition, and gist comprehension.