For many reasons, higher education is grappling with how to effectively incorporate Artificial Intelligence (AI) into learning environments. The rapid emergence of AI means that students often possess comparable knowledge to their instructors, making it challenging for educators to claim expertise in a constantly evolving field. Moreover, teaching students how to use AI should not be a top-down exercise, as the ethical implications of AI demand a democratic dialogue between faculty and students. These dynamics all present an opportunity to rethink traditional pedagogical structures, emphasizing collaboration and shared learning.

In response to these changes and challenges, this project focuses on developing a student-led syllabus design for a first-year Introduction to Artificial Intelligence (AI) course, using an action research methodology to ensure a participatory and iterative process. The approach unfolds in three iterative cycles: planning, implementation, and evaluation, with an emphasis on reflection and adaptation. The framework facilitates collaborative decision-making among students and faculty on course objectives, content, assessments, and policies. This presentation will detail the planning phase, focusing on the methodological and conceptual structures that foster constructive participation and collaboration. Conference participants will learn about a replicable framework for implementing student-led syllabus design, insights into balancing student autonomy with emergent expertise, and strategies for integrating student-led tutorials and near-peer learning. Finally, the presentation will discuss leveraging Open Educational Resources (OERs) to enhance accessibility, impact, and scalability of student-led course design initiatives.

Keywords: Artificial Intelligence Education, Student-Led Syllabus Design, Participatory Pedagogy, Action Research, Collaborative Course Design, Higher Education Innovation, Ethical AI Discussions, Open Educational Resources (OERs), Peer-Led Tutorials, Democratic Learning Models.