Abstract:

Higher education faces a persistent resource gap in developing practical, hands-on computing curricula. Faculty often lack time to create up-to-date instructional materials, while students require practical skills bridging theory and industry demands. Co-creation initiatives, where students partner with staff in curriculum development, offer a promising solution but frequently encounter barriers including skill gaps, lack of continuity across academic years, and difficulty sustaining momentum. The emergence of Generative Artificial Intelligence presents new opportunities to address these challenges, yet little research has examined how AI can support rather than supplant authentic co-creation partnerships.

This paper presents a case study of student-staff co-creation project. The project aimed to develop a framework to maximise the value of students' productivity and to enable students to engage in a student-led partnership to develop high quality outcomes. The study addresses two research questions:
(1) how co-creative approaches can ensure equitable access to skill development regardless of prior experience, and
(2) how involving students in co-creation of game-based and inquiry-based learning resources can enhance future teaching practices.

The study employs a longitudinal action research framework spanning multiple academic cycles organised into three phases:
1. Foundation, establishing infrastructure and recruitment protocols;
2. Continuous development, validation, peer-learning and implementing the full model with vertically integrated student teams; and
3. Outreach (planned), extending materials across programme and externally.

Central to the methodology is a ‘Tripartite Co-Creation’ model involving three agents: an Academic Facilitator providing strategic direction and mentorship, Vertically Integrated Student Teams contributing contemporary perspectives and peer learning insights, and GenAI tools serving as an acceleration layer bridging knowledge gaps. A ‘collaboration-first’ recruitment protocol prioritised partnership aptitude over technical proficiency, while a structured AI-augmented workflow incorporating visual scaffolding, block-to-Python translation, and responsible AI consultation guided student engagement.

The output exhibits - what we term ‘productive roughness’ - a functional authenticity making materials more accessible to novice learners than professionally produced alternatives. Operational validation through a Robotics Challenge event confirmed that participants not involved in content creation could successfully use the materials to construct line-follower maps and programme robots, demonstrating functional completeness and transferability.

The findings demonstrate that the vertically integrated team structure addresses sustainability by creating a ‘learning ladder’ where returning students - returning to project in phase 2 following completion of phase 1 - mentor newcomers, preserving institutional knowledge across cohorts. AI acceleration enables students to produce functionally sustainable curriculum artefacts when embedded within a structured AI-enabled human-centered co-creation framework. We conclude that successful co-creation depends not primarily on technology or structure, but on a culture of authentic partnership where students exercise genuine ownership whilst benefiting from expert guidance and AI augmentation.

Keywords:

Co-creation, students as partners, generative AI, educational robotics, action research, curriculum development, STEM education.