| AI-Supported Researcher Fidelity in Quasi-Experimental Studies |
| Paper ID : 1793-SPORTCONGRESS |
| Authors |
|
Zahra Nasiri * Department of Behavior and cognitive sciences in sports, Faculty of Sport Sciences and Health, University of Tehran, Tehran, Iran |
| Abstract |
| Introduction: In quasi-experimental research, the reliability of results and the integrity of study implementation depend heavily on researchers’ ethical commitment and methodological fidelity. Recent advances in artificial intelligence (AI) offer new opportunities to enhance adherence to intervention protocols, monitor execution, and analyze complex datasets with improved accuracy. This study aims to investigate researcher fidelity in quasi-experimental studies while highlighting the role of AI in supporting ethical practices, protocol adherence, and reliable reporting of outcomes (1). Methods: A descriptive-analytical mixed-methods approach is adopted. The target population includes researchers in behavioral sciences, psychology, and sports sciences who have conducted quasi-experimental studies in the past five years. Data collection involves a researcher-developed questionnaire assessing methodological fidelity, semi-structured interviews exploring experiences with intervention implementation, and AI-assisted analysis of research logs and reported results. Quantitative data are analyzed using exploratory factor analysis and structural equation modeling, while qualitative data are examined through thematic analysis, complemented by AI-driven pattern recognition. Results: Preliminary analysis indicates that researchers’ fidelity to intervention protocols is positively associated with their experience, ethical awareness, and engagement with AI tools for monitoring and analysis. AI integration enables real-time detection of deviations from protocols and provides objective insights, enhancing both transparency and reliability in quasi-experimental research. Conclusion: Integrating AI into quasi-experimental research strengthens ethical commitment and methodological fidelity, thereby improving the validity and credibility of research findings. The proposed framework highlights the potential of AI as a tool to support rigorous, transparent, and reproducible scientific practices in social and behavioral research. |
| Keywords |
| Artificial Intelligence, Researcher Fidelity, Methodological Fidelity, Quasi-Experimental Studies, Research Ethics, Research Quality |
| Status: Abstract Accepted (Poster Presentation) |
