Revolution in research: KI transforms the data analysis!

Revolution in research: KI transforms the data analysis!

empirical researchers are faced with the challenge of adapting to the rapid development of generative AI technology. This challenge requires comprehensive know-how, practical experience as well as methodological reflections and critical objections. Prof. Uwe Krähnke from Medical School Berlin, Dr. Thorsten Dresing and Dipl.-Päd. Thorsten Pehl (audio transcription) have developed an innovative procedure of the hybrid text interpretation that uses several dialogically integrated LARGE Language models (LLMS) to open up new opportunities in qualitative research. These technical and epistemological foundations of the AI ​​are dealt with in detail in a current publication. The authors discuss the methodological and ethical data protection consequences of their research, which was published as Open Access: Hybrid interpretation text-based data .

The aim of this new methodology is to use generative AI technology for methodological reflection in qualitative data analysis. Through iterative interaction with LLMS, research offers and hypotheses can check, elaborate and validate. It is important that the researchers control the analysis process and thus secure methodical standards. In their study, the authors also determine that established principles such as discursive validation, abductive heuristics and sensitizing concept are essential.

innovations by AI in science

artificial intelligence has established itself as a revolutionary force in science, which significantly accelerates and simplifies the research process. According to the experts on the website ki-echo , scientists use AI to efficiently process large amounts of data and to gain new knowledge. AI technologies recognize patterns and relationships that remain invisible to the human eye and enable faster hypothesent tests and more precise predictions.

The integration of AI is not only necessary in research, but also promises new discoveries. AI-based data analyzes optimize the planning, implementation and evaluation of experiments. This automates routine tasks and saves resources. Likewise, AI tools students allow improved organization of tasks and appointments, which optimizes time management.

ethical considerations and challenges

Despite the many advantages that AI brings in research, there are ethical considerations that should not be ignored. Transparency of algorithms, data protection and responsibility for decisions are central topics that researchers have to take into account in the application of AI systems. The experts also warn of potential bias in the systems, which underlines the need for responsible data handling.

The development and application of hybrid interpretation techniques with AI shows that the future of these technologies is promising in science. However, researchers must learn to use AI effectively and to take into account the associated ethical implications. The work of Krähnke, Dresing and Pehl remains of particular interest in this context and provide important impulses for further development in qualitative research.

For further information and the complete text of the publication, the website Berlin to be referred.