Artificial Intelligence for Video-based Learning at Scale

Video-based learning (VBL) is widespread; however, there are numerous challenges when teaching and learning with video. For instructors, creating effective instructional videos takes considerable time and effort. For students, watching videos can be a passive learning activity. Artificial intelligence (AI) has the potential to improve the VBL experience. This half-day workshop will bring together multi-disciplinary researchers and practitioners to collaboratively envision the future of VBL enhanced by AI. This workshop will be comprised of a group discussion followed by a presentation session. The goal of the workshop is to facilitate the cross-pollination of design ideas and critical assessments of AI approaches to VBL.


Call for Participation (AUG 12, 2020, 12-4 pm EST)

We invite position papers for a half-day workshop to explore the potential of artificial intelligence (AI) for video-based learning (VBL) at scale. This workshop offers an interdisciplinary forum for all interested in applying and / or critiquing AI approaches, such as machine learning, natural language processing, computer vision, and big data analytics. Discussions of the potential ethical challenges of AI in education, such as student privacy, bias and discrimination, and labor, are strongly encouraged. This workshop aims to develop relevant research topics to researchers and practitioners and to build a community around this emerging area.

We welcome two to four page position papers in the CHI Extended Abstracts Format on the workshop themes. All papers will be single blind peer reviewed by the program committee in order to assess relevance, quality, and diversity of submissions. Participants from all backgrounds are welcome. Accepted submissions will be presented at the workshop. All submissions will be submitted via email to Kyoungwon Seo ( by July 12. At least one author of each accepted position paper must virtually attend the workshop.

We intend to publish a special issue paper reviewing how the Learning @ Scale community can move towards realizing the potential of AI for video-based teaching and learning.


Workshop Structure

  • 12:00–12:15 (15 minutes) Welcome

The workshop organizers will introduce the workshop agenda and the goals.

  • 12:15–1:00 pm (45 minutes) Position Paper Presentations

To increase awareness among the attendees, organizers will coordinate a paper presentation session where each attendee will introduce their research interests and the main ideas in their position paper. Each attendee will be given two to three minutes.

  • 1:00–1:15 pm (15 minutes) Break
  • 1:15–2:15 pm (60 minutes) Discussing AI-based VBL

Participants will break out into small groups to define critical challenges and opportunities with VBL. The implications of AI to VBL will be discussed.

  • 2:15–3:00 pm (45 minutes) Synthesizing

Participants will be asked to synthesize what their group discussed (e.g., scenario, skit, visual, Wizard-of-Oz, and so on). The organizers will provide a variety of lightweight methods to help participants share their thoughts.

  • 3:00–3:45 pm (45 minutes) Reporting with Q&A

Participants will present their discussion results and provide take-away messages about the role of AI for future VBL.

  • 3:45–4:00 pm (15 minutes) Closing

The workshop will conclude with a discussion about the outcomes of the day, summarizing the takeaway messages of designing AI for future video-based learning at scale, and future plans.



  • Kyoungwon Seo, University of British Columbia
  • Sidney Fels, University of British Columbia
  • Dongwook Yoon, University of British Columbia
  • Ido Roll, Technion—Israel Institute of Technology
  • Samuel Dodson, University of British Columbia
  • Matthew Fong, University of British Columbia