The Effectiveness of Using Artificial Intelligence Tools in Developing Data Interpretation and Visualization Skills of Biology Teachers

Aibar Orazbek, Makhabbat Amanbaeva, Ayazhan Otarova

2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.59941/2960-0642-2026-3-256-275
Data confidence 70 medium
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Abstract

Artificial intelligence is transforming biology education and increasing the demand for teachers who can interpret biological data, create meaningful visualizations, and integrate AI into teaching. However, many biology teachers still face difficulties in analyzing complex datasets and presenting them effectively. This study evaluated the effectiveness of an AI-supported MOOC in developing biology teachers’ competencies in data interpretation, data visualization, and AI-supported educational practice.A quasi-experimental pre-test–post-test control group design was conducted with 76 secondary school biology teachers from Almaty, Kazakhstan. Participants were equally assigned to an experimental group (n = 38), which completed an eight-week AI-supported MOOC, and a control group (n = 38), which participated in conventional professional development. The MOOC integrated ChatGPT, NotebookLM, Research Rabbit, JustDone AI, Napkin AI, Whimsical AI, Canva AI, Grammarly, QuillBot, Qualtrics, Kampus.ai, and Examka to support data interpretation, visualization, and educational content development. Competencies were assessed using 30-item pre- and post-tests and a 30-item Likert-scale questionnaire. Quantitative data were analyzed using IBM SPSS Statistics and independent-samples t-tests (p < 0.05).The results demonstrated significant improvements. In the experimental group, the proportion of teachers with high data interpretation competency increased from 15% to 50%, while low competency decreased from 40% to 10%. High data visualization competency increased from 10% to 42%, and low competency declined from 52% to 16%. Overall competency scores improved from 45% to 80% for data interpretation, from 40% to 75% for data visualization, and from 30% to 85% for AI competency. Post-test scores for all competency domains were significantly higher than those of the control group (p < 0.001).The findings indicate that integrating AI tools into a structured MOOC effectively strengthens biology teachers’ competencies in data interpretation, data visualization, and AI-supported educational practice, supporting the wider adoption of AI-based professional development in biology teacher education.