DEVELOPMENT OF DATA INTERPRETATION METHODS IN THE COURSE OF «ORGANIZATION AND PLANNING OF SCIENTIFIC RESEARCH IN BIOLOGY» USING ARTIFICIAL INTELLIGENCE TOOLS

Abstract

The growing complexity of biological data and the need for efficient data analysis methods have led to the increased integration of artificial intelligence (AI) tools in scientific research education. This study investigates the impact of AI-based data interpretation methods on the analytical competencies of undergraduate students enrolled in the 'Organization and Planning of Scientific Research in Biology' course at Abai Kazakh National Pedagogical University. A mixed-methods research design was employed, involving 50 participants divided into two groups: the experimental group, which received training in AI tools such as TensorFlow, Keras, and Orange Data Mining, and the control group, which applied traditional statistical methods using SPSS and Excel.

Data were collected through pre-test and post-test assessments, survey questionnaires, semi-structured interviews, and data analysis tasks. Quantitative analysis revealed that the experimental group achieved a 30.9% increase in post-test scores, while the control group exhibited a 15.2% improvement. The qualitative data indicated that participants perceived AI tools as effective in enhancing data visualization and interpretation capabilities. However, challenges such as the complexity of AI interfaces and the steep learning curve were noted.

The findings suggest that AI tools significantly improve data interpretation skills, enabling students to effectively analyze complex datasets and generate more accurate visual representations. Despite initial difficulties, the majority of participants acknowledged the transformative potential of AI in scientific research education. This study recommends the incorporation of structured AI training modules to maximize the pedagogical impact of AI tools in biology research courses. Future research should focus on expanding the sample size, integrating diverse datasets, and assessing the long-term effects of AI-based training on academic performance and research outcomes