Search and Filter

Repository Feedback

Your feedback helps us improve the repository's content relevance and usability. Please share your thoughts to help us better serve researchers and practitioners.

Submit feedback

Submit a research study

Contribute to the repository:

Add a paper

Methodologies For Improving The Quality Of AI Tutoring In K-12 Education

Authors
Tushar Udeshi,
Anna Khazenzon,
Kabir Khan,
Nick Breen,
RJ Corwin,
Chris DiGiano,
Kodi Weatherholtz,
Marek Zaluski
Date
Publisher
arXiv
Many AI tutors leverage large language models (LLMs) today. Given that LLMs are opaque black boxes, robust evaluation and live experimentation to measure the impact of every change are essential. We pioneered AI-powered tutoring for K-12 with the launch of Khanmigo (Khan Academy, 2023). We describe the metrics we use to measure AI tutoring quality and student engagement as well as various experiments we have run. We highlight the changes that have moved our metrics, including models, prompting, personalization and agents.
What is the application?
Who is the user?