COVID-19 has brought upon us the inevitable transformation towards virtual education. The ensuing need for scalable, personalized learning systems has led to an unprecedented demand for understanding large-scale educational data. However, the field of Artificial Intelligence in Education (AIEd) has received relatively little academic attention compared to the mainstream machine learning areas like vision, natural language processing, and healthcare. In this workshop, we plan to invite AIEd enthusiasts from all around the world through three different channels. First, we will call for papers related to important AIEd topics that can help us imagine what new education will look like. Second, we propose a shared task about various AIEd problems on the largest public dataset, EdNet, which contains 123M interactions from more than 1M students. Finally, we host a global challenge on Kaggle for a fair comparison of state-of-the-art Knowledge Tracing models and invite technical reports from winning teams. Through these initiatives, we aim to provide a common ground for researchers to share their cutting-edge insights on AIEd and encourage the development of practical and large-scale AIEd methods of lasting impact.

Submission Guidelines

We welcome four categories of papers:

• Regular Workshop Papers: We invite high-quality paper submissions of theoretical and experimental nature on topics including, but not limited to, the following list of topics
• Shared Task Papers: We invite novel and interesting research work on the Shared Task resource we release. The papers from the Shared Task track will also be peer-reviewed.
• Extended Abstracts: Preliminary but interesting ideas that have not been published before may be submitted as extended abstracts. These should be up to 2 pages long and would benefit from additional exposure and discussion but is not ready for publication. Selection of extended abstracts will be determined solely by the organizing committee.
• Cross-submissions: In addition to previously unpublished work, we invite papers on relevant topics which have appeared in or submitted to alternative venues (such as other ML or AIEd conferences). Accepted cross-submissions will be presented as posters, with an indication of the original venue. Selection of cross-submissions will be determined solely by the organizing committee.

All submissions should follow the AAAI format and should be between 4~8 pages excluding references and appendices (2 pages for extended abstracts) following AAAI 2021 formatting guidelines. Submissions should be made in PDF format through EasyChair. Papers will be peer-reviewed and selected for oral or poster presentations at the workshop. Attendance is open to all, and at least one author of each accepted submission must be present at the workshop.

List of Topics

• Deploying Educational Systems in the Real World
• Pre-deployment Considerations of Educational Systems
• Behavioral Testing of Intelligent Tutoring Systems
• User Interface for Interactive Educational Systems
• A/B Testing of Educational Systems
• Interpretability in AIEd
• AI for Formative Learning
• Knowledge Tracing (Response Prediction & Correctness Prediction)
• Educational Content Recommendation
• Question Difficulty Prediction
• Score Prediction
• Personalized Curriculum Generation
• Deep Learning in Learning Sciences
• Role of Artificial Intelligence in Remote Learning
• Student Monitoring
• Teacher-Educational System Integration
• Automated Essay Scoring

Contact

All questions about submissions should be emailed to ...

• Jineon Baek: jineon.baek@riiid.co
• Jamin Shin: jamin.shin@riiid.co