Teaching the Atomic Sentence Method for Source-Verified, AI-Assisted Literature Synthesis to Clinical Health Care...

Generative AI can reduce the academic-writing burden on clinical health care professionals, but unsupervised use introduces citation hallucination (the confident fabrication or misattribution of references), which threatens research integrity. When a machine invents a source, it is termed "hallucination," and when a person does it, it is termed "fabrication," yet both are equally unacceptable. Existing health professions education writing workshops have rarely translated this concern into a concrete, reproducible source-verification procedure.

This study aimed to describe the development and delivery of a 2-day workshop teaching the Atomic Sentence method, a source-anchored knowledge-modeling technique for AI-assisted literature synthesis, and evaluate its feasibility, acceptability, and short-term effect on research-idea development among clinical health care professionals.

We conducted a single-cohort educational program evaluation of a 2-day workshop (≈16 contact hours) for 18 health care professionals across 4 campuses of a Buddhist medical network in Taiwan. Curriculum development used the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model; outcomes were framed with the Kirkpatrick model (levels 1-2). The workshop implemented a published 7-step AI-assisted research workflow (topic exploration, literature search, knowledge-base management, reading, synthesis, writing, and peer-review simulation). Citation integrity was protected by confining citation generation to a source-grounded tool (NotebookLM) combined with atomic-sentence extraction and mandatory cross-reference verification; other, nongrounded tools supported discovery, reading, and writing. Feasibility was assessed by the workshop completion rate and the questionnaire response rate. Outcomes were an acceptability questionnaire covering 5 domains (usefulness of instructional materials, curriculum planning, time allocation, personal-goal attainment, and administrative support; each rated 0-10) and a pre/post research-topic-transformation analysis (4 categories; 2 independent coders, Cohen κ); responses to an open-ended item on improvement suggestions were analyzed by qualitative content analysis. Reporting follows the GAMER (Guidance for AI Use in Medical Education Reporting) guidance for AI use in medical education.

All 18 (100%) participants completed the workshop and evaluation. Acceptability was high across all 5 domains (each median 10; overall median 10, IQR 9.3-10). The most variable domain was time allocation (range 5-10). Short-term improvement in the research topic (substantial transformation or refinement) occurred in 11 (61%; κ=0.77) participants; 2 (11%) had no structured question before or after. All 18 (100%) would recommend the workshop.

A short workshop teaching the Atomic Sentence method was feasible and well accepted and was associated with short-term research-idea development in a multidisciplinary clinical cohort. Whether the method reduces citation hallucination or improves citation accuracy was not tested and requires controlled studies with objective outcomes, blinded raters, and longitudinal follow-up.