335 citations · 535 across the 88 of their papers we have counts for
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Principal Trait Analysis: Towards Deriving "Skills" in Human-AI Collaboration
Hunter McNichols, Kai Du, Andrew Lan
Large Language Model-powered agents are increasingly used in the workplace via human-artificial intelligence (AI) collaboration. In this new era of work, it is important to underst…
When Rubrics Change: Cross-Rubric Generalization for Critical Thinking Essay Scoring
Nischal Ashok Kumar, Payu Wittawatolarn, Sana Kang +7
Automated essay scoring (AES) research has largely focused on cross-prompt generalization, where essays from unseen prompts are scored while the scoring criteria are typically held…
Who Am I? History-Aware Profiles for Student Simulation in Tutoring Dialogues
Zhangqi Duan, Shuyan Huang, Alexander Scarlatos +3
A key part of developing large language model (LLM)-powered, automated tutoring tools is student simulation, i.e., using LLMs to role-play as students, which can facilitate tutor m…
Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues
Shuyan Huang, Alexander Scarlatos, Jaewook Lee +1
Recent advances in large language models (LLMs) have led to the development of AI-powered tutoring systems that provide interactive support via dialogue. To enable these tutoring s…
Using LLMs for Knowledge Component-level Correctness Labeling in Open-ended Coding Problems
Zhangqi Duan, Arnav Kankaria, Dhruv Kartik +1
Fine-grained skill representations, commonly referred to as knowledge components (KCs), are fundamental to many approaches in student modeling and learning analytics. However, KC-l…
CodeGENCAT: Generative Computerized Adaptive Testing for Open-ended Coding Problems
Wanyong Feng, Alexander Scarlatos, Ruochen Sun +1
Existing Computerized Adaptive Testing (CAT) frameworks typically select questions based on the predicted likelihood that the student will answer correctly. This design ignores inf…
Letting Tutor Personas Speak Up for LLMs: Learning Steering Vectors from Dialogue via Preference Optimization
Jaewook Lee, Alexander Scarlatos, Simon Woodhead +1
With the emergence of large language models (LLMs) as a powerful class of generative artificial intelligence (AI), their use in tutoring has become increasingly prominent. Prior wo…
Simulated Students in Tutoring Dialogues: Substance or Illusion?
Alexander Scarlatos, Jaewook Lee, Simon Woodhead +1
Advances in large language models (LLMs) enable many new innovations in education. However, evaluating the effectiveness of new technology requires real students, which is time-con…
Interpretable Mnemonic Generation for Kanji Learning via Expectation-Maximization
Jaewook Lee, Alexander Scarlatos, Andrew Lan
Learning Japanese vocabulary is a challenge for learners from Roman alphabet backgrounds due to script differences. Japanese combines syllabaries like hiragana with kanji, which ar…
Exploring LLMs for Predicting Tutor Strategy and Student Outcomes in Dialogues
Fareya Ikram, Alexander Scarlatos, Andrew Lan
Tutoring dialogues have gained significant attention in recent years, given the prominence of online learning and the emerging tutoring abilities of artificial intelligence (AI) ag…
PhoniTale: Phonologically Grounded Mnemonic Generation for Typologically Distant Language Pairs
Sana Kang, Myeongseok Gwon, Su Young Kwon +4
Vocabulary acquisition poses a significant challenge for second-language (L2) learners, especially when learning typologically distant languages such as English and Korean, where p…
SMART: Simulated Students Aligned with Item Response Theory for Question Difficulty Prediction
Alexander Scarlatos, Nigel Fernandez, Christopher Ormerod +2
Item (question) difficulties play a crucial role in educational assessments, enabling accurate and efficient assessment of student abilities and personalization to maximize learnin…
Training LLM-based Tutors to Improve Student Learning Outcomes in Dialogues
Alexander Scarlatos, Naiming Liu, Jaewook Lee +2
Generative artificial intelligence (AI) has the potential to scale up personalized tutoring through large language models (LLMs). Recent AI tutors are adapted for the tutoring task…
Whose story is it? Personalizing story generation by inferring author styles
Nischal Ashok Kumar, Chau Minh Pham, Mohit Iyyer +1
Personalization is critical for improving user experience in interactive writing and educational applications, yet remains understudied in story generation. We study the task of pe…
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…