The genAI-enabled patient presented pertinent positive findings consistently with the ePBLM but deviated on non-essential content¶
Claim · All claims
Evidence · 1 study · causalr1·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Deviations occurred when the AI answered questions for which ePBLM content was minimal or non-pertinent, e.g., reporting occasional recreational marijuana use where the ePBLM said "Rarely a beer or two on the weekend." → Allison Mool · Jacob Schmid · Thomas Johnston · Kodi J. Smith McCoy · Zachary Patterson · Hannah Feldt · William Thomas · Emma Fenner · Kevin Lu · Raya Gandhi · Adam Western · Brendan Seabold · Daniel Vollmer · Roshan Nallaveettil · Anthony Fanelli · Logan Schmillen · Shelley Tischkau · Anna T. Cianciolo · Pinckney Benedict · Richard Selinfreund 2026
Evidence¶
Allison Mool · Jacob Schmid · Thomas Johnston · Kodi J. Smith McCoy · Zachary Patterson · Hannah Feldt · William Thomas · Emma Fenner · Kevin Lu · Raya Gandhi · Adam Western · Brendan Seabold · Daniel Vollmer · Roshan Nallaveettil · Anthony Fanelli · Logan Schmillen · Shelley Tischkau · Anna T. Cianciolo · Pinckney Benedict · Richard Selinfreund 2026¶
Allison Mool · Jacob Schmid · Thomas Johnston · Kodi J. Smith McCoy · Zachary Patterson · Hannah Feldt · William Thomas · Emma Fenner · Kevin Lu · Raya Gandhi · Adam Western · Brendan Seabold · Daniel Vollmer · Roshan Nallaveettil · Anthony Fanelli · Logan Schmillen · Shelley Tischkau · Anna T. Cianciolo · Pinckney Benedict · Richard Selinfreund. (2026). Using Generative AI to Simulate Patient History-Taking in a Problem-Based Learning Tutorial: A Mixed-Methods Study. Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-025-09929-4
q2 · i? · causal · r1
Descriptive observation comparing the AI's answers to the ePBLM's scripted content across both genAI groups. Pertinent positive findings matched; deviations appeared on headaches and drug/alcohol queries, and the AI sometimes volunteered incidental pertinent findings or embellished with rapport-building language.
"The genAI-enabled patient presented pertinent positive findings consistently with the ePBLM. Deviations occurred when the AI responded to questions for which the ePBLM content was minimal or provided a non-pertinent negative finding."
Discussion¶
Related Claims¶
- GenAI tutorials took longer than ePBLM tutorials, with approximately 10 additional minutes spent on history-taking — related
- GenAI-condition students rated their PBL tutorial significantly higher after the tutorial than they rated their previous ePBLM-based experiences, especially clinical accuracy — related
- Students treated the genAI avatar as a sophisticated question base rather than a virtual patient, using predominantly closed-ended, efficiency-focused questioning — related
- Patient history recall was near ceiling in both conditions, with significant decline over two weeks but no difference between genAI and ePBLM conditions — related
- Controlled GenAI virtual-patient and communication studies reported short-term skill gains, but positive findings were not universal — related
- GenAI students collaboratively phrased and troubleshooted questions, with collaborative problem-solving varying substantially between the two genAI groups — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
genai-accurate-pertinent-findings-nonessential-deviations |
title |
The genAI-enabled patient presented pertinent positive findings consistently with the ePBLM but deviated on non-essential content |
description |
The genAI-enabled patient presented pertinent positive findings consistently with the ePBLM but deviated on non-essential content |
status |
review |
evidence_strength |
weak |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — allison-mool-jacob-schmid-thomas-johnston-kodi-j-smith-mccoy-zachary-patterson-hannah-feldt-william-thomas-emma-fenner-kevin-lu-raya-gandhi-adam-western-brendan-seabold-daniel-vollmer-roshan-nallaveettil-anthony-fanelli-logan-schmillen-shelley-tischkau-anna-t-cianciolo-pinckney-benedict-richard-selinfreund-2026 |
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