21 citations · 26 across the 16 of their papers we have counts for
16 papers
When AI Writes, Who Gets Cited? Evidence of Citation Monoculture Across Language Models
Sina Alemohammad, Denghui Zhang, Bolong Tang +5
As language models move from drafting prose to running literature-search agents with tool calls, fabricated references are becoming easier to catch and constrain. The harder failur…
Not All Synthetic Data Is Yours to Learn From
Sina Alemohammad, Li Chen, Richard G. Baraniuk +1
Can a language model improve from plain text sampled from itself, with no prompts, no teacher, no verifier, and no reward model? Yes, but only when the synthetic corpus is compatib…
Stable and Privacy-Preserving Synthetic Educational Data with Empirical Marginals: A Copula-Based Approach
Gabriel Diaz Ramos, Lorenzo Luzi, Debshila Basu Mallick +1
To advance Educational Data Mining (EDM) within strict privacy-protecting regulatory frameworks, researchers must develop methods that enable data-driven analysis while protecting…
Misconception Acquisition Dynamics in Large Language Models
Naiming Liu, Xinghe Chen, Richard Baraniuk +2
Effective educational AI depends on modeling student misconceptions. Such models enable realistic learner simulation and diagnostic, adaptive tutoring. However, instruction-tuning…
Circuit Complexity of Hierarchical Knowledge Tracing and Implications for Log-Precision Transformers
Naiming Liu, Richard Baraniuk, Shashank Sonkar
Knowledge tracing models mastery over interconnected concepts, often organized by prerequisites. We analyze hierarchical prerequisite propagation through a circuit-complexity lens…
Learning Context Matters: Measuring and Diagnosing Personalization Gaps in LLM-Based Instructional Design
Johaun Hatchett, Debshila Basu Mallick, Brittany C. Bradford +1
The adoption of generative AI in education has accelerated dramatically in recent years, with Large Language Models (LLMs) increasingly integrated into learning environments in the…
MetaCLASS: Metacognitive Coaching for Learning with Adaptive Self-regulation Support
Naiming Liu, Richard Baraniuk, Shashank Sonkar
Large language models can generate fluent explanations, but effective tutoring requires supporting the learner's thought process, not just delivering content. Metacognitive tutorin…
Scalable Generation and Validation of Isomorphic Physics Problems with GenAI
Naiming Liu, Leo Murch, Spencer Moore +4
Traditional synchronous STEM assessments face growing challenges including accessibility barriers, security concerns from resource-sharing platforms, and limited comparability acro…
Learning Context: A Unified Framework and Roadmap for Context-Aware AI in Education
Naiming Liu, Brittany Bradford, Johaun Hatchett +5
We introduce a unified Learning Context (LC) framework designed to transition AI-based education from context-blind mimicry to a principled, holistic understanding of the learner.…
CLEAR-3K: Assessing Causal Explanatory Capabilities in Language Models
Naiming Liu, Richard Baraniuk, Shashank Sonkar
We introduce CLEAR-3K, a dataset of 3,000 assertion-reasoning questions designed to evaluate whether language models can determine if one statement causally explains another. Each…
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…
Do LLMs Make Mistakes Like Students? Exploring Natural Alignment between Language Models and Human Error Patterns
Naiming Liu, Shashank Sonkar, Richard G. Baraniuk
Large Language Models (LLMs) have demonstrated remarkable capabilities in various educational tasks, yet their alignment with human learning patterns, particularly in predicting wh…
The Imitation Game for Educational AI
Shashank Sonkar, Naiming Liu, Xinghe Chen +1
As artificial intelligence systems become increasingly prevalent in education, a fundamental challenge emerges: how can we verify if an AI truly understands how students think and…
Atomic Learning Objectives Labeling: A High-Resolution Approach for Physics Education
Naiming Liu, Shashank Sonkar, Debshila Basu Mallick +2
This paper introduces a novel approach to create a high-resolution "map" for physics learning: an "atomic" learning objectives (LOs) system designed to capture detailed cognitive p…
LLM-based Cognitive Models of Students with Misconceptions
Shashank Sonkar, Xinghe Chen, Naiming Liu +2
Accurately modeling student cognition is crucial for developing effective AI-driven educational technologies. A key challenge is creating realistic student models that satisfy two…
MalAlgoQA: Pedagogical Evaluation of Counterfactual Reasoning in Large Language Models and Implications for AI in Education
Naiming Liu, Shashank Sonkar, Myco Le +1
This paper introduces MalAlgoQA, a novel dataset designed to evaluate the counterfactual reasoning capabilities of Large Language Models (LLMs) through a pedagogical approach. The…