Investigating Student Teacher Engagement with Data-Driven AI and Ethical Reasoning in a Graduate-Level Education Course

This study investigates how student teachers engaged with data science and machine learning (ML) through a collaborative, scenario-based project in a graduate-level online course, AI in STEAM Education. The study focuses on the pilot implementation of the module Responsible AI & Data Science: Ethics, Society, and Citizenship, developed within the EU-funded DataSETUP project. This module introduced student teachers to core data science and AI/ML concepts, with an emphasis on ethical reflection and societal impact. Drawing on qualitative artifacts from the pilot, the analysis applies a five-dimensional framework to examine participants’ thinking across the following dimensions of data engagement: (1) asking questions with data, (2) collecting, cleaning, and manipulating data, (3) modeling and interpreting, (4) critiquing data-based claims, and (5) reasoning about data epistemology. Findings show that student teachers demonstrated growing technical and ethical awareness and, in several cases, made spontaneous pedagogical connections—despite the absence of prompts to consider classroom applications. A supplementary coding lens identified four aspects of emerging pedagogical reasoning: instructional intent, curricular relevance, learning opportunities, and the role of the teacher. These findings highlight the value of integrating critically reflective, practice-based data science education into teacher preparation—supporting not only technical fluency but also ethical, civic, and pedagogical engagement with AI technologies.