Learnersourcing in the Age of AI: Student, Educator and Machine Partnerships for Content Creation

TL;DR

This paper proposes a comprehensive framework integrating human-AI partnerships to enhance learnersourcing, addressing quality, engagement, and system challenges, supported by case studies of existing systems.

Contribution

It introduces a novel framework that combines insights from learning sciences and AI to improve learnersourcing systems and their adoption.

Findings

Framework addresses content creation, evaluation, utilization, and instructor support.

Case studies demonstrate practical application of the framework.

Highlights potential for AI-human collaboration to overcome learnersourcing challenges.

Abstract

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.