Smoking Cessation Counseling Performance Among Medical Interns

June 11, 2026 updated by: Waleed Gamal Elddin Khaleel, Assiut University

Effect of Artificial Intelligence-Assisted Interactive Case-Based Training on Smoking Cessation Counseling Performance Among Medical Interns

Smoking remains one of the leading preventable causes of morbidity and mortality worldwide and is strongly associated with chronic respiratory diseases, cardiovascular disease, cancer, and premature death. Physicians play a central role in tobacco control through the delivery of smoking cessation counseling, and even brief physician advice has been shown to significantly increase smoking quit rates. The evidence-based 5A's model (Ask, Advise, Assess, Assist, and Arrange) is widely recommended as the standard framework for smoking cessation counseling.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Despite the availability of effective counseling strategies and pharmacological interventions, smoking cessation counseling remains infrequently used in routine clinical practice. Recent studies have demonstrated gaps in physicians' knowledge, confidence, and implementation of smoking cessation interventions. In Egypt, a recent study among resident physicians reported deficiencies in smoking cessation knowledge and counseling practices, while another study demonstrated low rates of referral for smoking cessation counseling among healthcare workers.

Traditional educational approaches often rely on passive learning methods that may not adequately develop practical counseling skills. Interactive case-based learning has been shown to improve clinical communication skills and smoking cessation counseling performance among healthcare trainees. Furthermore, recent advances in artificial intelligence have enabled the development of interactive educational tools capable of simulating realistic clinical meeting and providing structured feedback. AI-assisted simulation has shown promising results in smoking cessation education and medical training.

However, evidence regarding the effectiveness of AI-assisted interactive case-based training for improving smoking cessation counseling performance among practicing physicians remains limited. Therefore, this study aims to evaluate the effect of AI-assisted interactive case-based training on smoking cessation counseling performance among medicals interns using a randomized controlled educational design.

Study Type

Interventional

Enrollment (Estimated)

140

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • Egypt
    • Assuit
      • Asyut, Assuit, Egypt, 71515

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • Medical interns enrolled in the internship training program at the Faculty of Medicine, Assiut University during the study period.
  • Able to attend the training session and complete all study assessments, including the pre-test and post-test evaluations.

Exclusion Criteria:

  • Previous formal structured training in smoking cessation counseling based on the 5A model.
  • Previous participation in a smoking cessation counseling educational program within the preceding 12 months.
  • Failure to complete the assigned educational intervention.
  • Failure to complete either the pre-test or post-test assessment.
  • Withdrawal of consent at any stage of the study.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Other
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Artificial Intelligience assisted interactive case-based training
Participants will receive AI-assisted interactive case-based training in addition to the standard educational materials. The intervention will consist of a series of standardized clinical scenarios related to smoking cessation counseling, followed by structured AI-generated educational feedback based on the 5A model
Other: Artificial Intelligence assisted interactive case-based training for smoking cessation counselling
Participants will receive AI-assisted interactive case-based training in addition to the standard educational materials. The intervention will consist of a series of standardized clinical scenarios related to smoking cessation counseling, followed by structured AI-generated educational feedback based on the 5A model
Active Comparator: standard guideline-based smoking cessation training
Participants will receive standard guideline-based smoking cessation training consisting of educational materials covering the 5A smoking cessation counseling model, nicotine dependence, pharmacological treatment options, and smoking cessation referral strategies
Other: standard guideline-based smoking cessation training
Participants will receive standard guideline-based smoking cessation training consisting of educational materials covering the 5A smoking cessation counseling model, nicotine dependence, pharmacological treatment options, and smoking cessation referral strategies.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in smoking cessation counseling performance score
Time Frame: 1 month
Smoking cessation counseling performance will be assessed using standardized clinical cases and a predefined 5A a standardized scoring system (Ask, Advise, Assess, Assist, and Arrange). Each case scenario will be scored out of 10 points, with a total possible score of 30 points for three clinical cases. The primary outcome will be the change in total 5A performance score from baseline to post-intervention assessment
1 month

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Assiut University

Investigators

  • Principal Investigator: waleed gamal, ass. prof, Assiut University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

June 30, 2026

Primary Completion (Estimated)

September 1, 2026

Study Completion (Estimated)

October 1, 2026

Study Registration Dates

First Submitted

June 11, 2026

First Submitted That Met QC Criteria

June 11, 2026

First Posted (Actual)

June 16, 2026

Study Record Updates

Last Update Posted (Actual)

June 16, 2026

Last Update Submitted That Met QC Criteria

June 11, 2026

Last Verified

June 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • WGEK-AI-Cessation

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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