Technology has transformed over the last few years, turning from futuristic ideas into today’s reality. AI is one of these transformative technologies that is now achieving great successes in various real-world applications and making our life more convenient and safe. AI is now shaping the way businesses, governments, and educational institutions doing things and is making its way into classrooms, schools and districts across many countries.

In fact, the increasingly digitalized education tools and the popularity of online learning have produced an unprecedented amount of data that provides us with invaluable opportunities for applying AI in education. Recent years have witnessed growing efforts from AI research community devoted to advancing our education and promising results have been obtained in solving various critical problems in education. For examples, AI tools are built to ease the workload for teachers. Instead of grading each piece of work individually, which can take up a bulk of extra time, intelligent scoring tools allow teachers the ability to have their students work automatically graded. In the coronoavirus era, requiring many schools to move to online learning, the ability to give feedback at scale could provide needed support to teachers. What’s more, various AI based models are trained on massive student behavioral and exercise data to have the ability to take note of a student’s strengths and weaknesses, identifying where they may be struggling. These models can also generate instant feedback to instructors and help them to improve their teaching effectiveness. Furthermore, leveraging AI to connect disparate social networks among teachers, we may be able to provide greater resources for their planning, which have been shown to significantly effect students’ achievement.

Despite gratifying achievements have demonstrated the great potential and bright development prospect of introducing AI in education, developing and applying AI technologies to educational practice is fraught with its unique challenges, including, but not limited to, extreme data sparsity, lack of labeled data, and privacy issues. Hence, this workshop will focus on introducing research progress on applying AI to education and discussing recent advances of handling challenges encountered in AI educational practice.
Workshop Description

In this workshop, we invited AIED enthusiasts from all around the world through the following three different channels:

First, we invited established researchers in the AIED community to give a broad talk that (1) describes a vision for bridging AIED communities; (2) summarizes a well-developed AIED research area; or (3) presents promising ideas and visions for new AIED research directions.

Second, we called for regular workshop paper submissions and cross-submissions (papers that have appeared in or submitted to alternative venues) related to a broad range of AI domains for education.

Third, we hosted a global challenge on Codalab for a fair comparison of state-of-the-art Knowledge Tracing models and invited technical reports from winning teams.

Regular Workshop Paper Submission

We invite high-quality paper submissions of theoretical and experimental nature on topics including, but not limited to, the following:

Emerging technologies in education
Evaluation of education technologies
Immersive learning and multimedia applications
Implications of big data in education
Self-adaptive learning
Individual and personalized education
Intelligent learning systems
Intelligent tutoring and monitoring systems
Automatic assessment in education
Automated grading of assignments
Automated feedback and recommendations
Big data analytics for education
Analysis of communities of learning
Computer-aided assessment
Course development techniques
Data analytics & big data in education
Mining and web mining in education
Learning tools experiences and cases of study
Social media in education
Smart education
Digital libraries for learning
education analytic approaches, methods, and tools
Knowledge management for learning
Learning analytics and educational data mining
Learning technology for lifelong learning
Tracking learning activities
Uses of multimedia for education
Wearable computing technology in e-learning
Smart classroom
Dropout prediction
Knowledge tracing

The workshop solicits 4-7 pages double-blind paper submissions from participants. Submissions of the following flavors will be sought: (1) research ideas, (2) case studies (or deployed projects), (3) review papers, (4) best practice papers, and (5) lessons learned. The format is the standard double-column AAAI Proceedings Style. All submissions will be peer-reviewed. Some will be selected for spotlight talks, and some for the poster session.
Cross-submissions

In addition to previously unpublished work, we invite papers on relevant topics which have appeared in alternative venues (such as other ML or AIED conferences). Accepted cross-submissions will be presented as 15-min oral presentations, with an indication of the original venue. Selection of cross-submissions will be determined solely by the organizing committee. There will be no regular paper reviews for cross submissions.
Submission Website

Submission website: https://easychair.org/conferences/?conf=aaai2023ai4edu. The submission AUTHOR KIT can be found at https://www.aaai.org/Publications/Templates/AnonymousSubmission23.zip.
Global Knowledge Tracing Challenge

In this competition, we would like to call for researchers and practitioners worldwide to investigate the opportunities of improving the student assessment performance via knowledge tracing approaches with rich side information.

A graphical illustration of knowledge tracing

The details of this competition can be found at http://ai4ed.cc/competitions/aaai2023competition.
Important Dates

Nov 27, 2022: Workshop paper submission due AOE
Dec 25, 2022 : Notifications of acceptance
Jan 5, 2023: Deadline of the camera-ready final paper submission
Feb 13 (or 14), 2023: Workshop Date

Organizers

Zitao Liu TAL Education Group, China
Weiqi Luo Guangdong Institute of Smart Education, Jinan University, China
Shaghayegh (Sherry) Sahebi University at Albany – SUNY, USA
Lu Yu Beijing Normal University, China
Richard Tong IEEE AI Standard Committee & Squirrel AI Learning, USA
Jiahao Chen TAL Education Group, China
Qiongqiong Liu TAL Education Group, China