32 citations · 98 across the 26 of their papers we have counts for
17 papers · 1 filter
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…
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…
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…
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…
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…
RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning
Alexander Scarlatos, Andrew Lan
Recent developments in large pre-trained language models have enabled unprecedented performance on a variety of downstream tasks. Achieving best performance with these models often…
Tree-Based Representation and Generation of Natural and Mathematical Language
Alexander Scarlatos, Andrew Lan
Mathematical language in scientific communications and educational scenarios is important yet relatively understudied compared to natural languages. Recent works on mathematical la…
UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language
David Koleczek, Alex Scarlatos, Siddha Karakare +1
Patronizing and condescending language (PCL) is everywhere, but rarely is the focus on its use by media towards vulnerable communities. Accurately detecting PCL of this form is a d…