This study aims to explore a new way of delivering health information using an AI-generated podcast. The podcast, created with Google NotebookLM, uses verified content from the American Academy of Periodontology website to provide easy-to-understand information on gum health and prevention. The goal is to determine whether this AI-generated podcast is a useful, engaging, and clear tool for educating the general public about health topics. Traditional health podcasts often feature expert interviews and can be lengthy, which sometimes limits their appeal and accessibility. By using AI to generate the podcast, investigator hope to offer a more standardized and concise presentation that avoids technical jargon. To evaluate the podcast, investigator developed a questionnaire based on the Questionnaire for Assessing Educational Podcasts (QAEP). This questionnaire was adapted to better suit a non-specialist audience and covers four key areas: how easy the podcast is to access and use, the design and structure of the podcast, the clarity and completeness of the content, and the podcast's value as a learning tool. Before using this questionnaire with the general public, investigator sent it to 10 experts in dentistry, public health, and communication for their review and feedback. Their input helped us make minor modifications to ensure the questionnaire is both clear and scientifically sound. After these revisions, investigator conducted a pilot study with 30 members of the general public who listened to the podcast and completed the questionnaire. This study will assess the feasibility and validity of using an AI-generated podcast as a health education tool. The results will help determine if this approach can effectively improve public understanding of health information and may guide the future design of digital health communication strategies.
Background and Rationale Recent advances in digital media have underscored the potential of podcasts as an innovative medium for disseminating healthcare information. Traditional health podcasts, while valuable, often suffer from limitations such as lengthy duration, inconsistent quality, and the use of complex medical jargon that may hinder public understanding. In contrast, artificial intelligence (AI)-driven content generation offers an opportunity to create concise, standardized, and accessible audio content. This study leverages Google NotebookLM to generate a podcast on gum health and prevention using publicly available information from the American Academy of Periodontology (AAP). By doing so, the research aims to explore whether an AI-generated health podcast can effectively enhance public health literacy. Objectives The primary objective of this pilot study is to evaluate the feasibility and validity of an AI-generated health podcast as an educational tool for the general public. Specific objectives include: 1. Questionnaire Development: Develop and validate a structured questionnaire-adapted from the Questionnaire for Assessing Educational Podcasts (QAEP)-that captures public perceptions regarding podcast accessibility, design, content adequacy, and educational value. 2. Reliability and Validity Testing: Assess the reliability and content validity of the newly developed questionnaire through expert evaluation and statistical analysis. 3. Public Evaluation of Podcast Quality: Assess the general public's perceptions of the AI-generated health podcast, focusing on its accessibility, design, content quality, and overall educational value, using the validated questionnaire. Study Design and Methods This study is designed as a prospective, observational feasibility pilot. It consists of two phases: Phase 1: Instrument Development and Expert Validation A questionnaire was developed using QAEP as a benchmark and then refined based on feedback from 10 experts (including dental, public health, and communication specialists). Statistical analyses (Cronbach's Alpha, Content Validity Index, and Exploratory Factor Analysis) were conducted to ensure the tool's reliability and validity. Phase 2: Public Pilot Evaluation The validated questionnaire was administered via an online Google Form to 30 general public participants. Demographic data-including age, gender, educational background, and English audio comprehension-were collected to contextualize the findings.
Study Type
OBSERVATIONAL
Enrollment
30
This intervention involves inviting a group of subject matter experts-including dental specialists, public health professionals, and communication experts-to evaluate the adapted questionnaire. The questionnaire, derived from the Questionnaire for Assessing Educational Podcasts (QAEP) and tailored for assessing an AI-generated health podcast, covers four dimensions: Access and Use, Design and Structure, Content Adequacy, and Value as an Aid to Learning. Experts will complete a structured online survey (via Google Form) to rate each item's clarity, relevance, and necessity. Their feedback is integral to refining and validating the instrument prior to its use with the general public.
Participants in this group will first listen to a 4-minute AI-generated health podcast on gum health and prevention. The podcast was produced using Google NotebookLM and is based on publicly available information from the American Academy of Periodontology, with appropriate source credit. After listening, participants will complete a validated questionnaire (administered via Google Form) that assesses the podcast's accessibility, design, content adequacy, and educational value. This intervention is designed to measure public perception, engagement, and overall feasibility of using AI-generated podcasts as an educational tool for health communication.
College of Applied Medical Sciences
Al Kharj, Al Qassim, Saudi Arabia
RECRUITINGContent Validity Index (CVI) of the Adapted Questionnaire
Description: This outcome measure evaluates the content validity of the questionnaire adapted from QAEP, as determined by expert ratings. The Content Validity Index (CVI) quantifies the proportion of experts who rate questionnaire items as relevant or highly relevant. Unit of Measure: Score on a scale from 0 to 1, where scores above 0.78 indicate acceptable content validity
Time frame: From 7th February 2025 to 25th February 2025
Internal Consistency Reliability of the Adapted Questionnaire
Description: This outcome measure assesses the internal consistency reliability of the adapted questionnaire using Cronbach's Alpha coefficient, which measures how closely related the items are as a group. Unit of Measure: Cronbach's Alpha coefficient on a scale from 0 to 1, where values above 0.7 indicate acceptable reliability
Time frame: From 7th February 2025 to 25th February 2025
Public Perception Score of the AI-Generated Health Podcast
Description: This outcome measure assesses the general public's overall perception of the AI-generated health podcast using the validated questionnaire. Unit of Measure: Composite score on a 5-point Likert scale (1-5), where 1 represents "Strongly Disagree" and 5 represents "Strongly Agree"
Time frame: From 27th February 2025 to 15th April 2025
Domain-Specific Evaluation Scores of the AI-Generated Health Podcast
Description: This outcome measure assesses the general public's evaluation of specific domains of the AI-generated health podcast, including accessibility, design, content adequacy, and educational value. Unit of Measure: Mean scores for each domain on a 5-point Likert scale (1-5), where 1 represents "Strongly Disagree" and 5 represents "Strongly Agree"
Time frame: From 27th February 2025 to 15th April 2025
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