27 citations · 36 across the 10 of their papers we have counts for
10 papers
A Causal Framework for Estimating Heterogeneous Effects of On-Demand Tutoring
Kirk Vanacore, Danielle R Thomas, Digory Smith +3
This paper introduces a scalable causal inference framework for estimating the immediate, session-level effects of on-demand human tutoring embedded within adaptive learning system…
Misconception Diagnosis From Student-Tutor Dialogue: Generate, Retrieve, Rerank
Joshua Mitton, Prarthana Bhattacharyya, Digory Smith +3
Timely and accurate identification of student misconceptions is key to improving learning outcomes and pre-empting the compounding of student errors. However, this task is highly d…
AI tutoring can safely and effectively support students: An exploratory RCT in UK classrooms
LearnLM Team, Eedi, : +33
One-to-one tutoring is widely considered the gold standard for personalized education, yet it remains prohibitively expensive to scale. To evaluate whether generative AI might help…
PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues
Matthew Zent, Digory Smith, Simon Woodhead
Personally identifiable information (PII) anonymization is a high-stakes task that poses a barrier to many open-science data sharing initiatives. While PII identification has made…
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank
Alexander Scarlatos, Wanyong Feng, Digory Smith +2
Multiple-choice questions (MCQs) are commonly used across all levels of math education since they can be deployed and graded at a large scale. A critical component of MCQs is the d…
Math Multiple Choice Question Generation via Human-Large Language Model Collaboration
Jaewook Lee, Digory Smith, Simon Woodhead +1
Multiple choice questions (MCQs) are a popular method for evaluating students' knowledge due to their efficiency in administration and grading. Crafting high-quality math MCQs is a…
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models
Wanyong Feng, Jaewook Lee, Hunter McNichols +5
Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in assessments and practices.…
Improving the Validity of Automatically Generated Feedback via Reinforcement Learning
Alexander Scarlatos, Digory Smith, Simon Woodhead +1
Automatically generating feedback via large language models (LLMs) in intelligent tutoring systems and online learning platforms has the potential to improve the learning outcomes…
Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning
Hunter McNichols, Wanyong Feng, Jaewook Lee +4
Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable form of assessment. An important aspe…
NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education
Wenbo Gong, Digory Smith, Zichao Wang +5
In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data. The first is to identify th…