AI-assisted clinical decision-making training for pharmacology students

ISRCTN	ISRCTN15993331
DOI	https://doi.org/10.1186/ISRCTN15993331
Sponsor	Tongren Polytechnic College
Funder	Tongren Polytechnic College

Submission date: 23/06/2026
Registration date: 26/06/2026
Last edited: 26/06/2026

Recruitment status: No longer recruiting
Overall study status: Completed
Condition category: Other

Prospectively registered

Protocol

Statistical analysis plan

Results

Individual participant data

Record updated in last year

Plain English summary of protocol

Not provided at time of registration

Contact information

Prof Dandan Wang
Public, Scientific, Principal investigator

School of Pharmacy, Tongren Polytechnic College, No. 2, Ziyou Road, Bijiang District
Tongren
554300
China

Phone	+86-15185112564
Email	wdd12345tree@163.com

Study information

Primary study design	Interventional
Allocation	Non-randomized controlled trial
Masking	Open (masking not used)
Control	Active
Assignment	Parallel
Purpose	Treatment
Scientific title	The Context-Feedback-Visibility (CFV) Framework: mechanisms of AI-empowered clinical decision-making training in pharmacology education
Study acronym	CFV
Study objectives	This study aims to (1) empirically test whether an AI-empowered work logic restructuring model can strengthen clinical decision-making competence in pharmacology education, and (2) identify the underlying mechanisms through which AI and curriculum logic coupling produces its effects.
Ethics approval(s)	Approved 25/05/2024, Institutional Ethics Committee of Tongren Polytechnic College (School of Pharmacy, Tongren Polytechnic College, No. 2, Ziyou Road, Bijiang District, Tongren, 554300, China; +86-856-6909046; Not provided at time of registration), ref: Not provided at time of registration
Health condition(s) or problem(s) studied	Clinical decision-making competence in pharmacology education
Intervention	The study employed a four-group quasi-experimental design over two consecutive semesters. Full Intervention group (n=42): Received a restructured pharmacology curriculum organized around typical medication tasks, supported by four AI technology components (virtual simulation medication decision-making system, intelligent case generator, pharmacology knowledge graph, and natural language interaction module). AI-Only group (n=40): Used identical AI tools but continued with a traditional drug-classification-based curriculum. Curriculum-Only group (n=41): Received the restructured curriculum without AI support. Traditional Instruction group (n=41): Received standard case-based lectures with no AI tools or curriculum restructuring.
Intervention type	Behavioural
Primary outcome measure(s)	Knowledge application ability measured using a standardized case analysis examination containing 10 comprehensive cases, each requiring multi-step medication decision-making, scored by two pharmacology instructors blinded to group assignment, at pre-intervention (Week 0), mid-term (Week 16), end-of-term (Week 32)
Key secondary outcome measure(s)
Completion date	15/07/2025

Eligibility

Participant type(s)
Age group	Adult
Lower age limit	18 Years
Upper age limit	22 Years
Sex	All
Target sample size at registration	164
Total final enrolment	164
Key inclusion criteria	1. Second-year pharmacy students enrolled in a pharmacology course at Tongren Polytechnic College 2. All participants provided informed consent
Key exclusion criteria	Students who did not complete the pre-intervention baseline assessments
Date of first enrolment	01/09/2024
Date of final enrolment	01/09/2024

Locations

Countries of recruitment

China

Study participating centres

Results and Publications

Individual participant data (IPD) Intention to share	No

Editorial Notes

25/06/2026: Study’s existence confirmed by the Tongren Polytechnic College Institutional Ethics Committee, China.