Smart Primer

Narrative-based Learning in Context

What is
Smart Primer?

The Smart Primer is a tablet-based intelligent tutoring system for kids that leverages compelling narratives, intelligent tutoring chatbots, real-world activities, and a child’s physical and educational context.


Researchers at Stanford University have developed a new chatbot — personified as a penguin named Frosty — to improve the way that students study outside the classroom by making learning more engaging and personalized.

One hurdle in designing an educational system is that the computer must be able to recognize correct answers in various forms. That’s where artificial intelligence comes in.
World Economic Forum

AI has as much to do with its interface as it does with the underlying capabilities it provides. If we want to build a future of open possibility and empowerment, it’s vital that our ability to harness AI evolves alongside AI itself.
The Stanford Institute for Human-Centered Artificial Intelligence (HAI)

As artificial intelligence continues to play a bigger role in our lives, so will interest increase in its uses in the field of education.
The Stanford AI Lab Blog


James Landay
Computer Science

Emma Brunskill
Computer Science

Alan Cheng
Computer Science
PhD student

Sherry Ruan
Computer Science
PhD Alumna

Meng Guo
Symsys & Education
Masters Student

William Steenbergen
Masters Student

Abdallah AbuHashem
Computer Science
Coterm Student

Khuyen Le
Computer Science
Coterm Student

Cathy Zhang
Computer Science

Wilmer Zuna Largo
Computer Science

Kyle Nguyen
Computer Science

Catherine Wang
CS, Art & Art History

David Wright
Creative Writing

Jiequan Zhang
Industry Collaborator

Joyce He
Industry Collaborator

Rui Ying
Electrical Engineering
Industry Collaborator


Smart Primer
Intelligent Tutoring System for Children

Virtual Reading Companion

Dragon Companion
Augmented Reality Based Geocaching

Knowledge Tracing
Mining Student Data with Deep Learning

Chatbot for Factual Knowledge

Key Phrase Extraction
Automatic Educational Content Generation


Variational Deep Knowledge Tracing for Language Learning [Paper] [BibTeX]
Sherry Ruan, Wei Wei, James A. Landay
The International Conference on Learning Analytics and Knowledge (LAK), 2021

EnglishBot: An AI-Powered Conversational System for Second Language Learning [Paper] [BibTeX] [Video]
Sherry Ruan*, Liwei Jiang*, Qianyao Xu*, Glenn Davis, Zhiyuan Liu, Emma Brunskill, James A. Landay (* equal contribution)
The International Conference on Intelligent User Interfaces (IUI), 2021

Supporting Children's Math Learning with Feedback-Augmented Narrative Technology [Paper] [BibTeX] [Video]
Sherry Ruan, Jiayu He, Rui Ying, Jonathan Burkle, Dunia Hakim, Anna Wang, Yufeng Yin, Lily Zhou, Qianyao Xu, Abdallah AbuHashem, Griffin Dietz, Elizabeth Murnane, Emma Brunskill, James A. Landay
ACM Interaction Design and Children (IDC) conference, 2020

BookBuddy: Turning Digital Materials to Interactive Second Language Learning Lessons Through a Voice Chatbot [Paper] [BibTeX]
Sherry Ruan, Angelica Willis, Qianyao Xu, Glenn Davis, Liwei Jiang, Emma Brunskill, James Landay
L@S WIP: Proceedings of the Sixth Annual ACM Conference on Learning at Scale, 2019

Key Phrase Extraction for Generating Educational Question-Answer Pairs [Paper] [Dataset] [BibTeX]
Angelica Willis, Glenn Davis, Sherry Ruan, Lakshmi Manoharan, James Landay, Emma Brunskill
L@S: Proceedings of the Sixth Annual ACM Conference on Learning at Scale, 2019

QuizBot: A Dialogue-based Adaptive Learning System for Factual Knowledge [Paper] [Video] [Slides] [BibTeX]
Sherry Ruan, Liwei Jiang, Justin Xu, Bryce Joe-Kun Tham, Zhengneng Qiu, Yeshuang Zhu, Elizabeth L. Murnane, Emma Brunskill, James A. Landay
CHI: ACM Conference on Human Factors in Computing Systems, 2019

Adaptive Natural-Language Targeting for Student Feedback [Paper] [BibTeX]
Y. Alex Kolchinski*, Sherry Ruan*, Dan Schwartz, Emma Brunskill (* equal contribution)
L@S WIP: Proceedings of the Fifth Annual ACM Conference on Learning at Scale, 2018

Sponsors & Collaborators

Work With Us

Who are we looking for?
We are looking for exceptional students who are passionate about improving education with technology to join us. In particular, we are looking for students or researchers who have prior experience in any of the following areas: HCI, AI, Education, Design, and Creative Writing.

How do we usually work with Stanford students?
Stanford undergraduate and masters students usually enroll in research classes such as CS 199 and CS 399 during the school year and apply for summer research programs such as CURIS in the summer.

Can I still apply if I am not Stanford-affiliated?
Yes. We also welcome industry collaborators and classroom teachers outside of Stanford and can provide compensation.

I am interested! How can I apply?
Please send an email to with your resume and/or transcript attached.