  Engaging students in creating novel content, also referred to as
learnersourcing, is increasingly recognised as an effective approach to
promoting higher-order learning, deeply engaging students with course material
and developing large repositories of content suitable for personalized
learning. Despite these benefits, some common concerns and criticisms are
associated with learnersourcing (e.g., the quality of resources created by
students, challenges in incentivising engagement and lack of availability of
reliable learnersourcing systems), which have limited its adoption. This paper
presents a framework that considers the existing learnersourcing literature,
the latest insights from the learning sciences and advances in AI to offer
promising future directions for developing learnersourcing systems. The
framework is designed around important questions and human-AI partnerships
relating to four key aspects: (1) creating novel content, (2) evaluating the
quality of the created content, (3) utilising learnersourced contributions of
students and (4) enabling instructors to support students in the
learnersourcing process. We then present two comprehensive case studies that
illustrate the application of the proposed framework in relation to two
existing popular learnersourcing systems.
