Generative Artificial Intelligence and Project-Based Learning Outputs in Technology-Enhanced Mathematics Education

Background: Background: Generative Artificial Intelligence (GAI) is reshaping technology-enhanced mathematics education, yet little is known about how master’s students orchestrate GAI with dynamic and computational tools in project-based learning. Objectives: To analyse the mathematical, technological and pedagogical characteristics of products developed by master’s-level mathematics education students in a GAI-supported project-based learning sequence. Design: Qualitative content analysis combining deductive categories, informed by recent guidance on qualitative and thematic analysis, with inductive, data-driven coding. Setting and Participants: A master’s programme in mathematics education with five dyads engaged in a three-week technology-enhanced project in which GAI was used. Data collection and analysis: The corpus comprised two GeoGebra books and thirteen PDF artefacts (presentations and individual reflections). Data were coded using deductive categories and refined inductively to capture patterns in mathematical reasoning, technological orchestration and task design. Results: The study identifies ways in which dyads orchestrated GAI with computational and dynamic geometry tools, and describes patterns of mathematical correctness, justification and classroom-oriented task types. Conclusions: The findings indicate the potential of GAI to support task design and work with digital tools, while underscoring the need for critical verification practices and intentional pedagogical integration in project-based learning.