The effectiveness of the ‘teacher–neural network–student’ interaction in research-based and project-based learning in physics
DOI:
https://doi.org/10.33910/Keywords:
artificial intelligence, project-based learning, school education, physics, neural network, digital learning environment, pedagogical technologiesAbstract
Introduction. The modern education system is focused on developing students’ critical thinking, independent information processing, communication, and the ability to apply knowledge to real-life and academic problem solving. Project-based learning, being a mandatory component of physics education according to the Federal State Educational Standard, contributes to the formation of these competencies. Artificial intelligence (AI) is a new tool within the educational environment that can enhance the effectiveness of project-based learning. This study aims to identify how AI influences the efficiency of the ‘teacher–AI–student’ interaction in research-based and project-based activities.
Materials and Methods. The study includes a theoretical analysis of research literature by Russian and international scholars devoted to the use of AI in education. A survey was conducted among physics teachers in St. Petersburg regarding the use of AI in project-based learning. An example of a 10th-grade student using AI in a research project is provided. Questionnaires, comparative analysis, and content analysis were used.
Results. The analysis showed that the potential of AI as a supportive tool is highly recognized by teachers; however, its use by students remains fragmented and is most common at the stages of information search and text structuring according to required templates. The risks include a possible reduction in students’ independence and inaccuracies in AI-generated data, which require verification. The survey confirmed that AI reduces time spent on project formatting and contributes to students’ motivation.
Conclusions. Effective integration of AI into project-based learning requires the development of methodological recommendations and teacher training for working with new digital tools. Future research may involve clarifying the roles of participants in the ‘teacher–AI–student’ interaction and developing pedagogical models for integrating AI into the teaching of project-based activities in physics. While the preliminary data obtained in this study make it possible to draw certain conclusions, a larger research sample is required to gain a more representative understanding of teacher and student attitudes toward the use of AI in physics project-based learning.
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