AAAI2020 Workshop on Artificial Intelligence for Education
Artificial Intelligence (AI) has dramatically transformed a variety of domains. However, education, a crucial component of our society still remains a relatively under-explored domain. In fact, the increasingly digitalized education tools and the popularity of the massive open online courses 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. Although it is still in the early stage, promising results have been achieved in solving various critical problems in education. For example, knowledge tracing, which is a intrinsically difficult problem due to the complexity under human learning procedure, has been solved successfully with powerful deep neural networks that can fully take the advantages of massive student exercise data. Besides the achievement in improving the student learning efficiency, similar excitement has been generated in other areas of education. For instance, researchers have also devoted to reducing the monotonous and tedious grading workloads of teaching professionals by building automatic grading systems that are underpinned by effective models from natural language process fields. Despite aforementioned success, developing and applying AI technologies to education is fraught with its unique challenges, including, but not limited to, extreme data sparsity, lack of labeled data, and privacy issues. Therefore, it is timely and necessary to provide a venue, which can bring together academia researchers and education practitioners to (1) to discuss the principles, limitations and applications of AI for education; and (2) to foster research on innovative algorithms, novel techniques, and new applications to education.
Call For Paper
We invite the submission of novel research paper (6 pages plus references), demo paper (4 pages plus references), visionary papers (4 pages plus references) as well as extended abstracts (2 pages plus references). Submissions must be in PDF format, written in English, and formatted according to the AAAI
camera-ready style. All papers will be peer reviewed,
single-blinded. Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. All the papers are required to be submitted via
EasyChair system. For more questions about the workshop and submissions, please send email to
We encourage submissions on a broad range of AI technologies for various education domains. Topics of interest include but are 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
- Learning technology for lifelong learning
- Course development techniques
- Mining and web mining in education
- Learning tools experiences and cases of study
- Life long education
- MOOC’s and data analytics
- Social media in education
- Smart education
- Education analytic approaches, methods, and tools
- Knowledge management for learning
- Learning analytics and educational data mining
- Smart classroom
- Dropout prediction
- Knowledge tracing
- Tracking learning activities
- Uses of multimedia for education
- Wearable computing technology in e-learning
- Analysis of communities of learning
- Computer-aided assessment
- Course development techniques
- Automated feedback and recommendations
- Big data analytics for education
- December 04, 2019: Workshop paper submission due (23:59, Pacific Standard Time)
- December 15, 2019: Workshop paper notifications
- January 15, 2020: Camera-ready deadline for workshop papers
- February 08, 2020: Workshop Date
Best Poster Award
Where AI meets the learner: Classroom as a mediator
Shiyi Shao, Beata Beigman Klebanov, Anastassia Loukina, Priya Kannan and Paola Heincke
Best Paper Award (tie)
Ranking Distractors for Multiple Choice Questions Using Multichannel Semantically Informed CNN-LSTM Networks
Tirthankar Dasgupta and Manjira Sinha
Identifying NGSS-Aligned Ideas in Student Science Explanations
Brian Riordan, Cahill Aoife, Jennifer King Chen, Wiley Korah, Allison Bradford, Libby Gerard and Marcia Linn
Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning. Byungsoo Jeon, Namyong Park and Seojin Bang.
Question Generation by Transformers. Kettip Kriangchaivech and Artit Wangperawong.
Interpreting Models of Student Interaction in Immersive Simulation Settings. Nicholas Hoernle, Kobi Gal, Barbara Grosz, Leilah Lyons, Ada Ren and Andee Rubin.
The Impact of Training Data Quality on Automated Content Scoring Performance. Lili Yao, Aoife Cahill and Daniel McCaffrey.
Automated Anonymisation of Visual and Audio Data in Classroom Studies. Ömer Sümer, Peter Gerjets, Ulrich Trautwein and Enkelejda Kasneci.
edBB: Biometrics and Behavior for Assessing Remote Education. Javier Hernandez-Ortega, Roberto Daza, Aythami Morales, Julian Fierrez and Javier Ortega-Garcia.
Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos. Byungsoo Jeon, Namyong Park and Seojin Bang.
Where AI meets the learner: Classroom as a mediator. Shiyi Shao, Beata Beigman Klebanov, Anastassia Loukina, Priya Kannan and Paola Heincke.
Personalized Technical Learning Assistance for Deaf and Hard of Hearing. Sameena Hossain, Ayan Banerjee and Sandeep Gupta.
Identifying NGSS-Aligned Ideas in Student Science Explanations. Brian Riordan, Cahill Aoife, Jennifer King Chen, Wiley Korah, Allison Bradford, Libby Gerard and Marcia Linn.
Towards Instance-Based Content Scoring with Pre-Trained Transformer Models. Kenneth Steimel and Brian Riordan.
Ranking Distractors for Multiple Choice Questions Using Multichannel Semantically Informed CNN-LSTM Networks. Tirthankar Dasgupta and Manjira Sinha.
Intelligent Tutoring Strategies for Students with Autism Spectrum Disorder: A Reinforcement Learning Approach. Stephanie Milani, Zhou Fan, Saurabh Gulati, Thanh Nguyen, Fei Fang, and Amulya Yadav.
Clustering Skills for Industrial Learning. Rajiv Srivastava, Swapnil Hingmire and Girish Palshikar.
Cloud enabled Multi-modal Knowledge Modeling for Learning and Skill Management (KaaS). Mahdi Bohlouli and Sebastian Hellekes.
An Application of Automated Scoring and Feedback to Support Student Writing of Scientific Arguments. Mengxiao Zhu, Ou Lydia Liu and Hee-Sun Lee.
Automatic Generation of Programming Word Problems. Rajas Vanjape, Vinayak Athavale and Manish Shrivastava.
A Deep Model for Predicting Online Course Performance. Hamid Karimi, Jiangtao Huang and Tyler Derr.
FACT: An Automated Teaching Assistant
Dr. Kurt VanLehn, Arizona State University
AI Singapore: AI in Education Grand Challenge
Dr. Bryan Low and Su Su Ma, AI Singapore
AI the Next Step for Education: Tech Innovations Making Our Classrooms Smarter
Dr. Zitao Liu, TAL Education Group
The Smart Classroom of the Future: Progress and Open Challenges
Dr. Vincent Aleven, Carnegie Mellon University
Algorithmic Openness in Data Intensive Education Analytics: K-12 Early Warning Systems, > Prediction, Accuracy, and Visual Data Analytics
Dr. Alex Bowers, Columbia University
Teachers in Social Media: Applications in Computational Education Science
Dr. Kaitlin Torphy, Michigan State University
AI in Education
Dr. Salil Mehta, ETS
- 08:30 - 08:45 – Opening Remarks
- 08:45 - 09:30 – Keynote: Kurt VanLehn, Arizona State University
- 09:30 - 10:00 – Keynote: Bryan Low and Su Su Ma, AI Singapore
- 10:00 - 10:30 – Keynote: Zitao Liu, TAL Education Group
- 10:30 - 11:00 – Coffee Break
- 11:00 - 12:00 – Paper Poster Session (will select the best paper and the best presentation awards)
- 12:00 - 13:00 – Lunch
- 13:00 - 13:45 – Keynote: Vincent Aleven, Carnegie Mellon University
- 13:45 - 14:30 – Keynote: Alex Bowers, Columbia University
- 14:30 - 15:00 – Keynote: Kaitlin Trophy, Michigan State University
- 15:00 - 15:30 – Keynote: Salil Mehta, ETS
- 15:30 - 16:00 – Coffee Break
- 16:00 - 17:00 – Award Announcement and Panel on “Ethics in AIED”
- Jiliang Tang Michigan State University
- Zitao Liu TAL Education Group
- Kaitlin Torphy Michigan State University
- Ken Frank Michigan State University
- Zhiwei Wang Michigan State University