335 citations · 535 across the 88 of their papers we have counts for
11 papers · 2 filters
Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank
Jaewook Lee, Hunter McNichols, Andrew Lan
In this paper, we study an under-explored area of language and vocabulary learning: keyword mnemonics, a technique for memorizing vocabulary through memorable associations with a t…
Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs
Alexander Scarlatos, Ryan S. Baker, Andrew Lan
Recent advances in large language models (LLMs) have led to the development of artificial intelligence (AI)-powered tutoring chatbots, showing promise in providing broad access to…
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions
Nigel Fernandez, Alexander Scarlatos, Wanyong Feng +2
High-quality distractors are crucial to both the assessment and pedagogical value of multiple-choice questions (MCQs), where manually crafting ones that anticipate knowledge defici…
Interpreting Latent Student Knowledge Representations in Programming Assignments
Nigel Fernandez, Andrew Lan
Recent advances in artificial intelligence for education leverage generative large language models, including using them to predict open-ended student responses rather than their c…
Generating Feedback-Ladders for Logical Errors in Programming using Large Language Models
Hasnain Heickal, Andrew Lan
In feedback generation for logical errors in programming assignments, large language model (LLM)-based methods have shown great promise. These methods ask the LLM to generate feedb…
Can Large Language Models Replicate ITS Feedback on Open-Ended Math Questions?
Hunter McNichols, Jaewook Lee, Stephen Fancsali +2
Intelligent Tutoring Systems (ITSs) often contain an automated feedback component, which provides a predefined feedback message to students when they detect a predefined error. To…
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…
Improving Socratic Question Generation using Data Augmentation and Preference Optimization
Nischal Ashok Kumar, Andrew Lan
The Socratic method is a way of guiding students toward solving a problem independently without directly revealing the solution to the problem. Although this method has been shown…
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…
Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education
Nischal Ashok Kumar, Andrew Lan
In computer science education, test cases are an integral part of programming assignments since they can be used as assessment items to test students' programming knowledge and pro…