The role of generative algorithms in changing the structure of knowledge production and improving the quality of measurement tools in digital media research

Abstract

This study aims to examine the role of generative algorithms in transforming the structure of knowledge production and improving the quality of measurement tools in digital media research. It is situated within the rapid transformations brought about by generative artificial intelligence in scientific research and digital media studies. The study is grounded in a central research problem concerning the growing use of generative algorithms in producing academic texts, analyzing data, and designing measurement tools, and the epistemological and methodological questions this raises regarding the reliability of generated knowledge, the limits of machine intervention in research processes, and the impact of these technologies on the validity, reliability, and objectivity of research instruments.

The study adopts a descriptive-analytical approach, using an electronic questionnaire as the main tool for data collection. The sample consists of researchers, faculty members, and postgraduate students in media and communication departments who have interest or experience in digital media research and generative artificial intelligence applications. The study focuses on measuring the role of generative algorithms in reshaping knowledge production, improving the quality of measurement tools such as questionnaires and content analysis instruments, and identifying the methodological and ethical challenges associated with their use, including algorithmic bias, excessive reliance on automated outputs, and the need for human oversight in maintaining research context and scientific rigor.

The findings indicate that generative algorithms contribute to improving the quality of measurement tools by enhancing item clarity, organizing research instrument components, increasing internal consistency, and generating multiple alternatives for measurement design. The results also show that these algorithms may help reduce certain methodological errors; however, they do not eliminate the need for the researcher’s role in interpretation, critical analysis, and scientific validation. The significance of this study lies in presenting a scholarly framework for understanding the complementary relationship between human intelligence and artificial intelligence in digital media research, contributing to the development of more accurate measurement tools and strengthening the quality of knowledge production in the digital media environment.