The third annual workshop on Learnersourcing: Student-generated Content @ Scale is taking place at Learning @ Scale 2025. This full-day hybrid workshop will feature invited speakers, interactive activities, paper presentations, and discussions, as we delve into the field's opportunities and challenges. Attendees will engage in hands-on development of learnersourcing activities suited to their own courses or systems and gain access to various learnersourcing systems and datasets for exploration. This workshop aims to foster discussions on new types of learnersourcing activities, strategies for evaluating the quality of student-generated content, the integration of LLMs with the field, and approaches to scaling learnersourcing to produce valuable instructional and assessment materials.

We believe participants from a wide range of backgrounds and prior knowledge on learnersourcing can both benefit and contribute to this workshop, as learnersourcing draws on work from education, crowdsourcing, learning analytics, data mining, ML/NLP, and many more fields!  Additionally, as the learnersourcing process involves many stakeholders (students, instructors, researchers, instructional designers, etc.), multiple viewpoints can help to inform what future student-generated content might be useful, new and better ways to assess the quality of the content, and spark potential collaboration efforts between attendees. We ultimately want to show how everyone can make use of learnersourcing and have participants gain hands on experience using these tools, creating their own learnersourcing activities using them or their own platforms, and take part in discussing the next challenges and opportunities in the learnersourcing space. Our hope is to attract attendees interested in scaling the generation of instructional and assessment content and those interested in the use of online learning platforms. 

Submission Guidelines

While no submission is required to participate in the workshop, we encourage submissions of various types as stated above. We expanded our submissions to include artifacts such as videos and commentary to express your perspectives on learnersourcing. However, the core submission format is a research, work-in-progression, or position paper, targeting roughly 3 to 5 pages.

​​​​Research, Work-in-Progress, or Position paper [3 to 5 pages, double column, PDF format preferred, Templates: Latex, Word]

Commentary on a past publication(s) [3 to 5 pages, PDF format preferred, Templates: Latex, Word]

The 3 to 5 page submissions will be curated into a learnersourcing proceedings that will be made available via a public proceedings. Submissions should contain mostly novel work, however there can be overlap between the submission and work submitted elsewhere (such as summaries, describing the process of the work, and focusing on the learnersourcing aspect). Each of the submissions will be reviewed by multiple members of the Program Committee.

List of Topics

Here are some questions and ideas applicants may want to considering addressing in their submissions:

• Strategies for engaging and motivating student participation in learnersourcing activities

• Exploration of innovative learnersourcing content formats

• Methods for evaluating the quality of student-generated content

• Incentivizing high-quality student contributions

• Techniques for providing actionable feedback during the learnersourcing process

• Approaches to enable collaboration and content sharing across institutions

• Training students to develop high-quality resources

• Exploring models of co-creating content

• How LLMs can assist in the cold start problem for student-content creation

• Leveraging LLMs to assist in the different stages (creation, evaluation, etc.) of the learnersourcing process

• Guidelines to maintaining pedagogical value in learnersourcing when AI assistance is involved

• Considerations for integrating student and AI contributions in learnersourcing, balancing the dual objectives of improving learning outcomes and enhancing the efficiency of educational content generation

Committees

Organizing committee

• Steven Moore, Carnegie Mellon University
• Anjali Singh, University of Texas at Austin
• Xinyi Lu, University of Michigan 
• Hyoungwook Jin, Korea Advanced Institute of Science & Technology
• Paul Denny, The University of Auckland
• Hassan Khosravi, The University of Queensland
• Chris Brooks, University of Michigan
• Xu Wang, University of Michigan
• Juho Kim, Korea Advanced Institute of Science & Technology
• John Stamper, Carnegie Mellon University

Venue

The conference will be held in Palermo, Italy at the Learning @ Scale 2025 conference.

Contact

All questions about submissions should be emailed to stevenmo@andrew.cmu.edu