Evaluating Generative AI-Enhanced Content: A Conceptual Framework Using Qualitative, Quantitative, and Mixed-Methods Approaches
This addresses the need for rigorous evaluation frameworks to benchmark generative AI tools against traditional processes in high-stakes domains like healthcare and scientific research, though it appears incremental as it applies existing research methods to this new context.
The paper tackles the problem of evaluating generative AI-enhanced content by proposing a conceptual framework using qualitative, quantitative, and mixed-methods approaches, with results demonstrating how these methods provide insights into improvements in coherence, readability, and quality for scientific writing applications.
Generative AI (GenAI) has revolutionized content generation, offering transformative capabilities for improving language coherence, readability, and overall quality. This manuscript explores the application of qualitative, quantitative, and mixed-methods research approaches to evaluate the performance of GenAI models in enhancing scientific writing. Using a hypothetical use case involving a collaborative medical imaging manuscript, we demonstrate how each method provides unique insights into the impact of GenAI. Qualitative methods gather in-depth feedback from expert reviewers, analyzing their responses using thematic analysis tools to capture nuanced improvements and identify limitations. Quantitative approaches employ automated metrics such as BLEU, ROUGE, and readability scores, as well as user surveys, to objectively measure improvements in coherence, fluency, and structure. Mixed-methods research integrates these strengths, combining statistical evaluations with detailed qualitative insights to provide a comprehensive assessment. These research methods enable quantifying improvement levels in GenAI-generated content, addressing critical aspects of linguistic quality and technical accuracy. They also offer a robust framework for benchmarking GenAI tools against traditional editing processes, ensuring the reliability and effectiveness of these technologies. By leveraging these methodologies, researchers can evaluate the performance boost driven by GenAI, refine its applications, and guide its responsible adoption in high-stakes domains like healthcare and scientific research. This work underscores the importance of rigorous evaluation frameworks for advancing trust and innovation in GenAI.