TY - JOUR
T1 - Summary of “Towards the Future of AI-augmented Human Tutoring in Math Learning”
AU - Thomas, Danielle R.
AU - Aleven, Vincent
AU - Baraniuk, Richard
AU - Brunskill, Emma
AU - Crossley, Scott
AU - Demszky, Dora
AU - Fancsali, Stephen
AU - Gupta, Shivang
AU - Ritter, Steve
AU - Woodhead, Simon
AU - Xing, Wanli
AU - Koedinger, Kenneth
N1 - Funding Information:
Emma is an Associate Professor in the Computer Science Department at Stanford University where she aims to create AI systems that learn from a few samples to robustly make good decisions. Her work is inspired by the positive impact AI may have in education and healthcare, with interests in large language models to advance AI-assisted human tutoring. Emma is part of the Stanford AI Lab, the Stanford Statistical ML group, and AI Safety @Stanford. She has received an NSF CAREER award, Office of Naval Research Young Investigator Award, and many other awards. Emma and her lab have received multiple best paper nominations for their AI and machine learning work.
Publisher Copyright:
© 2023 Copyright for this paper by its authors.
PY - 2023
Y1 - 2023
N2 - We summarize the proceedings of a full-day, hybrid workshop at the International Conference of Artificial Intelligence in Education hosted in Tokyo, Japan on July 3, 2023. The workshop, “Towards the Future of AI-augmented Human Tutoring in Math Learning,” focuses on the use of artificial intelligence (AI)assisted human tutoring in math learning. This workshop emphasizes attention to equity and improving access to high-quality learning opportunities among historically marginalized students, with a focus on obstacles to scaling. Among the six accepted papers and moderated panel discussion, we highlight the following key findings: 1) a greater general focus on identifying or diagnosing student’s needs and less so on the interventions or remedies that might follow, 2) large language models are the focal point among the vast exploration of applications occuring, and 3) human mentoring remains a strong, irreplaceable influence. Challenges and takeaways from this workshop sparked interest among the AIED community in the development of human-AI hybrid tutoring systems.
AB - We summarize the proceedings of a full-day, hybrid workshop at the International Conference of Artificial Intelligence in Education hosted in Tokyo, Japan on July 3, 2023. The workshop, “Towards the Future of AI-augmented Human Tutoring in Math Learning,” focuses on the use of artificial intelligence (AI)assisted human tutoring in math learning. This workshop emphasizes attention to equity and improving access to high-quality learning opportunities among historically marginalized students, with a focus on obstacles to scaling. Among the six accepted papers and moderated panel discussion, we highlight the following key findings: 1) a greater general focus on identifying or diagnosing student’s needs and less so on the interventions or remedies that might follow, 2) large language models are the focal point among the vast exploration of applications occuring, and 3) human mentoring remains a strong, irreplaceable influence. Challenges and takeaways from this workshop sparked interest among the AIED community in the development of human-AI hybrid tutoring systems.
KW - AI-assisted tutoring
KW - Personalized learning
KW - Tutoring
UR - http://www.scopus.com/inward/record.url?scp=85174944864&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174944864&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85174944864
SN - 1613-0073
VL - 3491
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2023 Workshop on International Conference of Artificial Intelligence in Education, AIED Human-AI Tutoring 2023
Y2 - 3 July 2023
ER -