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.

翻译：让学生参与创作新内容（即学习者溯源）正逐渐被认可为一种有效方法，用于促进高阶学习、深度引导学生参与课程材料，并开发适合个性化学习的大型内容库。尽管有这些益处，学习者溯源仍伴随一些常见问题和批评（例如，学生创建资源的质量、激励参与的挑战，以及缺乏可靠的学习者溯源系统），这限制了其推广。本文提出一个框架，综合现有学习者溯源文献、学习科学的最新见解以及人工智能的进展，为开发学习者溯源系统提供有前景的未来方向。该框架围绕关键问题和人机协作设计，涉及四个核心方面：（1）创作新内容，（2）评估创作内容质量，（3）利用学生的学习者溯源贡献，以及（4）使教师能在学习者溯源过程中支持学生。随后，我们通过两个综合性案例研究，展示了所提框架在两种现有流行学习者溯源系统中的应用。