A Research Course to Develop AI Tools for K–12 Learning

Key findings from this study

• The study found that university students successfully developed functioning software products deployed with K-12 users, providing authentic experience with real-world educational impact.
• The authors report that nearly all course participants gained their first introduction to full-cycle educational research, including experimental design, data collection, and analysis.
• The researchers demonstrate that the course generated a growing collection of reusable AI/ML teaching tools available for adoption by curriculum developers and practitioners.

Overview

University students in a semester-long course developed original software products to teach K-12 learners artificial intelligence and machine learning concepts. The course combined AI/ML knowledge development, software engineering skills, and educational research training. Students conducted school-based testing through AI Expos at partner public schools and completed full-cycle research projects examining student learning outcomes.

Methods and approach

The course integrated human-subjects research training with collaborative institutional review board approval processes. University students designed pre/post surveys and conversation protocols for K-12 participants. Software tools incorporated live interaction data collection capabilities. Each university student completed a final paper documenting tool design and presenting evidence of student conceptual understanding derived from collected data. The researchers conducted four course iterations across the described experience.

Results

University students successfully created functioning software products deployed with K-12 users, providing direct experience with real-world software impact. Data collection from K-12 interactions enabled students to analyze and present evidence of learning outcomes in their research papers. Many students extended their work beyond the semester, submitting projects to academic conferences. The course cultivated a growing collection of reusable AI/ML teaching tools available for curriculum developers.

Implications

The course structure provides university students with integrated technical and research competencies simultaneously. Participants gain practical experience in experimental design, data analysis, and research communication through authentic educational contexts. The development of deployable software tools creates tangible artifacts that satisfy participants and reinforce learning value across educational levels.

The accumulating software tool repository addresses a curricular gap in K-12 AI/ML instruction. University students' engagement with real end-users and authentic research cycles models professional practice in educational technology. The course demonstrates feasibility of embedding rigorous research methodology training within applied software development contexts.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.