Balancing Scaffolding and Autonomy: A Case Study in Designing a Scalable Undergraduate Machine Learning Research Course - Chen, Xinyue; Jessica, Sharon; Wang, Xu; Kutty, Sindhu (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Undergraduate research experiences are often limited to small-scale apprenticeship models, leaving many students without accessible entry points into research practice. This paper presents the design and evaluation of a semester-long course for undergraduates to gain research experience in Machine Learning. The course, led by one faculty instructor, enables nearly a hundred students to engage in structured research through a scaffolded replication-and-extension project, where students first replicate a published research project and then implement novel additions. The course integrates instructional modules (e.g., guided paper reading, proposal writing, public presentation) with project milestones (e.g., replication, extension, poster) to support research learning for students with diverse backgrounds. Every component of research is visited several times, with each iteration having progressively increased autonomy coupled with simultaneously decreased scaffolding. We find that the scaffolding modules help students develop foundational conceptual and procedural understanding of doing research, and the project milestones on replication and extension help them gain execution skills gradually. Students also report developing a researcher mindset and feeling like they understand the research process better. We discuss the principles used to design a scalable research-based course: balancing scaffolding to provide foundational understanding with autonomy for students to “feel like real researchers”.