Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics. - Andrew S. Lan; Christoph Studer; Andrew E. Waters; Richard G. Baraniuk (EDM, 2013)

Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data. - Andrew S. Lan; Christoph Studer; Andrew E. Waters; Richard G. Baraniuk (EDM, 2013)

Test-size Reduction for Concept Estimation. - Divyanshu Vats; Christoph Studer; Andrew S. Lan; Lawrence Carin; Richard G. Baraniuk (EDM, 2013)

Sparse probit factor analysis for learning analytics. - Andrew E. Waters; Andrew S. Lan; Christoph Studer (ICASSP, 2013)

Quantized Matrix Completion for Personalized Learning. - Andrew S. Lan; Christoph Studer; Richard G. Baraniuk (EDM, 2014)

Matrix recovery from quantized and corrupted measurements. - Andrew S. Lan; Christoph Studer; Richard G. Baraniuk (ICASSP, 2014)

Time-varying learning and content analytics via sparse factor analysis. - Andrew S. Lan; Christoph Studer; Richard G. Baraniuk (KDD, 2014)

Sparse factor analysis for learning and content analytics. - Andrew S. Lan; Andrew E. Waters; Christoph Studer; Richard G. Baraniuk (J. Mach. Learn. Res., 2014)

Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions. - Andrew S. Lan; Divyanshu Vats; Andrew E. Waters; Richard G. Baraniuk (L@S, 2015)

Optimal ranking of test items using the Rasch model. - Divyanshu Vats; Andrew S. Lan; Christoph Studer; Richard G. Baraniuk (Allerton, 2016)

A Contextual Bandits Framework for Personalized Learning Action Selection. - Andrew S. Lan; Richard G. Baraniuk (EDM, 2016)

Dealbreaker: A Nonlinear Latent Variable Model for Educational Data. - Andrew S. Lan; Tom Goldstein; Richard G. Baraniuk; Christoph Studer (ICML, 2016)

Near-Isometric Binary Hashing for Large-scale Datasets. - Amirali Aghazadeh; Andrew S. Lan; Anshumali Shrivastava; Richard G. Baraniuk (CoRR, 2016)

Behavior-Based Latent Variable Model for Learner Engagement. - Andrew S. Lan; Christopher G. Brinton; Tsung-Yen Yang; Mung Chiang (EDM, 2017)

Personalized Feedback for Open-Response Mathematical Questions using Long Short-Term Memory Networks. - Joshua J. Michalenko; Andrew S. Lan; Richard G. Baraniuk (EDM, 2017)

Data-Mining Textual Responses to Uncover Misconception Patterns. - Joshua J. Michalenko; Andrew S. Lan; Andrew E. Waters; Phillip Grimaldi; Richard G. Baraniuk (EDM, 2017)

A Latent Factor Model For Instructor Content Preference Analysis. - Jack Z. Wang; Andrew S. Lan; Phillip Grimaldi; Richard G. Baraniuk (EDM, 2017)

Short-Answer Responses to STEM Exercises: Measuring Response Validity and Its Impact on Learning. - Andrew E. Waters; Phillip Grimaldi; Andrew S. Lan; Richard G. Baraniuk (EDM, 2017)

Contextual multi-armed bandit algorithms for personalized learning action selection. - Indu Manickam; Andrew S. Lan; Richard G. Baraniuk (ICASSP, 2017)

RHash: Robust Hashing via L_infinity-norm Distortion. - Amirali Aghazadeh; Andrew S. Lan; Anshumali Shrivastava; Richard G. Baraniuk (IJCAI, 2017)

D.TRUMP: Data-mining Textual Responses to Uncover Misconception Patterns. - Joshua J. Michalenko; Andrew S. Lan; Richard G. Baraniuk (L@S, 2017)

Data-Mining Textual Responses to Uncover Misconception Patterns. - Joshua J. Michalenko; Andrew S. Lan; Richard G. Baraniuk (CoRR, 2017)

BLAh: Boolean Logic Analysis for Graded Student Response Data. - Andrew S. Lan; Andrew E. Waters; Christoph Studer; Richard G. Baraniuk (IEEE J. Sel. Top. Signal Process., 2017)

Learner Behavioral Feature Refinement and Augmentation Using GANs. - Da Cao; Andrew S. Lan; Weiyu Chen; Christopher G. Brinton; Mung Chiang (AIED (2), 2018)

Learning Informative and Private Representations via Generative Adversarial Networks. - Tsung-Yen Yang; Christopher G. Brinton; Prateek Mittal; Mung Chiang; Andrew S. Lan (IEEE BigData, 2018)

PhaseLin: Linear phase retrieval. - Ramina Ghods; Andrew S. Lan; Tom Goldstein; Christoph Studer (CISS, 2018)

Linearized binary regression. - Andrew S. Lan; Mung Chiang; Christoph Studer (CISS, 2018)

Behavioral Analysis at Scale: Learning Course Prerequisite Structures from Learner Clickstreams. - Weiyu Chen; Andrew S. Lan; Da Cao; Christopher G. Brinton; Mung Chiang (EDM, 2018)

Textbook annotations as an early predictor of student learning. - Adam Winchell; Michael Mozer; Andrew S. Lan; Phillip Grimaldi; Harold Pashler (EDM, 2018)

Linear Spectral Estimators and an Application to Phase Retrieval. - Ramina Ghods; Andrew S. Lan; Tom Goldstein; Christoph Studer (ICML, 2018)

An Estimation and Analysis Framework for the Rasch Model. - Andrew S. Lan; Mung Chiang; Christoph Studer (ICML, 2018)

Learning Cloud Dynamics to Optimize Spot Instance Bidding Strategies. - Mikhail Khodak; Liang Zheng 002; Andrew S. Lan; Carlee Joe-Wong; Mung Chiang (INFOCOM, 2018)

QG-net: a data-driven question generation model for educational content. - Zichao Wang 001; Andrew S. Lan; Weili Nie; Andrew E. Waters; Phillip J. Grimaldi; Richard G. Baraniuk (L@S, 2018)

Personalized Thread Recommendation for MOOC Discussion Forums. - Andrew S. Lan; Jonathan C. Spencer; Ziqi Chen; Christopher G. Brinton; Mung Chiang (ECML/PKDD (2), 2018)

Insense: Incoherent sensor selection for sparse signals. - Amirali Aghazadeh; Mohammad Golbabaee; Andrew S. Lan; Richard G. Baraniuk (Signal Process., 2018)

On the Efficiency of Online Social Learning Networks. - Christopher G. Brinton; Swapna Buccapatnam; Liang Zheng 002; Da Cao; Andrew S. Lan; Felix Ming Fai Wong; Sangtae Ha; Mung Chiang; H. Vincent Poor (IEEE/ACM Trans. Netw., 2018)

IdeoTrace: a framework for ideology tracing with a case study on the 2016 U.S. presidential election. - Indu Manickam; Andrew S. Lan; Gautam Dasarathy; Richard G. Baraniuk (ASONAM, 2019)

Grade Prediction with Neural Collaborative Filtering. - Zhiyun Ren; Xia Ning; Andrew S. Lan; Huzefa Rangwala (DSAA, 2019)

Grade Prediction Based on Cumulative Knowledge and Co-taken Courses. - Zhiyun Ren; Xia Ning; Andrew S. Lan; Huzefa Rangwala (EDM, 2019)

A Meta-Learning Augmented Bidirectional Transformer Model for Automatic Short Answer Grading. - Zichao Wang 001; Andrew S. Lan; Andrew E. Waters; Phillip Grimaldi; Richard G. Baraniuk (EDM, 2019)

Active Learning for Student Affect Detection. - Tsung-Yen Yang; Ryan S. Baker 001; Christoph Studer; Neil T. Heffernan; Andrew S. Lan (EDM, 2019)

Adaptive Remediation with Multi-modal Content. - Yuwei Tu; Christopher G. Brinton; Andrew S. Lan; Mung Chiang (HCI (32), 2019)

Predicting the Timing and Quality of Responses in Online Discussion Forums. - Patrick Hansen; Richard Junior Bustamante; Tsung-Yen Yang; Elizabeth Tenorio; Christopher G. Brinton; Mung Chiang; Andrew S. Lan (ICDCS, 2019)

Hurts to Be Too Early: Benefits and Drawbacks of Communication in Multi-Agent Learning. - Parinaz Naghizadeh; Maria Gorlatova; Andrew S. Lan; Mung Chiang (INFOCOM, 2019)

Exploring Automated Question Answering Methods for Teaching Assistance. - Brian Zylich; Adam Viola; Brokk Toggerson; Lara Al-Hariri; Andrew S. Lan (AIED (1), 2020)

Skill-based Career Path Modeling and Recommendation. - Aritra Ghosh 001; Beverly P. Woolf; Shlomo Zilberstein; Andrew S. Lan (IEEE BigData, 2020)

MSE-Optimal Neural Network Initialization via Layer Fusion. - Ramina Ghods; Andrew S. Lan; Tom Goldstein; Christoph Studer (CISS, 2020)

VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics. - Zichao Wang 001; Yi Gu; Andrew S. Lan; Richard G. Baraniuk (EDM, 2020)

qDKT: Question-centric Deep Knowledge Tracing. - Shashank Sonkar; Andrew S. Lan; Andrew E. Waters; Phillip Grimaldi; Richard G. Baraniuk (EDM, 2020)

Robust and Interpretable Grounding of Spatial References with Relation Networks. - Tsung-Yen Yang; Andrew S. Lan; Karthik Narasimhan (EMNLP (Findings), 2020)

Learning Student Interest Trajectory for MOOC Thread Recommendation. - Shalini Pandey; Andrew S. Lan; George Karypis; Jaideep Srivastava (ICDM (Workshops), 2020)

Context-Aware Attentive Knowledge Tracing. - Aritra Ghosh 001; Neil T. Heffernan; Andrew S. Lan (KDD, 2020)

Highlights as an Early Predictor of Student Comprehension and Interests. - Adam Winchell; Andrew S. Lan; Michael Mozer (Cogn. Sci., 2020)

Meta-knowledge dictionary learning on 1-bit response data for student knowledge diagnosis. - Yupei Zhang; Huan Dai; Yue Yun; Shuhui Liu; Andrew S. Lan; Xuequn Shang 001 (Knowl. Based Syst., 2020)

Mathematical Formula Representation via Tree Embeddings. - Zichao Wang 001; Andrew S. Lan; Richard G. Baraniuk (iTextbooks@AIED, 2021)

Option Tracing: Beyond Correctness Analysis in Knowledge Tracing. - Aritra Ghosh 001; Jay Raspat; Andrew S. Lan (AIED (1), 2021)

Scientific Formula Retrieval via Tree Embeddings. - Zichao Wang 001; Mengxue Zhang; Richard G. Baraniuk; Andrew S. Lan (IEEE BigData, 2021)

Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach. - Yun-Wei Chu; Elizabeth Tenorio; Laura M. Cruz Castro; Kerrie A. Douglas; Andrew S. Lan; Christopher G. Brinton (IEEE BigData, 2021)

Contrastive Learning Improves Model Robustness Under Label Noise. - Aritra Ghosh 001; Andrew S. Lan (CVPR Workshops, 2021)

Math Operation Embeddings for Open-ended Solution Analysis and Feedback. - Mengxue Zhang; Zichao Wang 001; Richard G. Baraniuk; Andrew S. Lan (EDM, 2021)

Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints. - Zichao Wang 001; Andrew S. Lan; Richard G. Baraniuk (EMNLP (1), 2021)

BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing. - Aritra Ghosh 001; Andrew S. Lan (IJCAI, 2021)

Using Past Data to Warm Start Active Machine Learning: Does Context Matter? - Shamya Karumbaiah; Andrew S. Lan; Sachit Nagpal; Ryan S. Baker 001; Anthony Botelho; Neil T. Heffernan (LAK, 2021)

Linguistic Skill Modeling for Second Language Acquisition. - Brian Zylich; Andrew S. Lan (LAK, 2021)

Do We Really Need Gold Samples for Sample Weighting under Label Noise? - Aritra Ghosh 001; Andrew S. Lan (WACV, 2021)

Learning Student Interest Trajectory for MOOCThread Recommendation. - Shalini Pandey; Andrew S. Lan; George Karypis; Jaideep Srivastava (CoRR, 2021)

Personalized Education in the AI Era: What to Expect Next? - Setareh Maghsudi; Andrew S. Lan; Jie Xu 001; Mihaela van der Schaar (CoRR, 2021)

MetaSense: Boosting RF Sensing Accuracy Using Dynamic Metasurface Antenna. - Guohao Lan; Mohammadreza F. Imani; Zida Liu; José Manjarrés; Wenjun Hu; Andrew S. Lan; David R. Smith; Maria Gorlatova (IEEE Internet Things J., 2021)

Personalized Education in the Artificial Intelligence Era: What to Expect Next. - Setareh Maghsudi; Andrew S. Lan; Jie Xu 001; Mihaela van der Schaar (IEEE Signal Process. Mag., 2021)

DiPS: Differentiable Policy for Sketching in Recommender Systems. - Aritra Ghosh 001; Saayan Mitra; Andrew S. Lan (AAAI, 2022)

Automated Scoring for Reading Comprehension via In-context BERT Tuning. - Nigel Fernandez; Aritra Ghosh 001; Naiming Liu; Zichao Wang 001; Benoît Choffin; Richard G. Baraniuk; Andrew S. Lan (AIED (1), 2022)

Intelligent Textbooks: Themes and Topics. - Sergey A. Sosnovsky; Peter Brusilovsky; Andrew S. Lan (AIED (2), 2022)

Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning. - Yun-Wei Chu; Seyyedali Hosseinalipour; Elizabeth Tenorio; Laura M. Cruz Castro; Kerrie A. Douglas; Andrew S. Lan; Christopher G. Brinton (CIKM, 2022)

Process-BERT: A Framework for Representation Learning on Educational Process Data. - Alexander Scarlatos; Christopher Brinton 001; Andrew S. Lan (EDM, 2022)

Automatic Short Math Answer Grading via In-context Meta-learning. - Mengxue Zhang; Sami Baral; Neil T. Heffernan; Andrew S. Lan (EDM, 2022)

Open-ended Knowledge Tracing for Computer Science Education. - Naiming Liu; Zichao Wang 001; Richard G. Baraniuk; Andrew S. Lan (EMNLP, 2022)

Open-Ended Knowledge Tracing. - Naiming Liu; Zichao Wang 001; Richard G. Baraniuk; Andrew S. Lan (CoRR, 2022)

DiFA: Differentiable Feature Acquisition. - Aritra Ghosh 001; Andrew S. Lan (AAAI, 2023)

Active Learning for a Classroom Observer who Can't Time Travel. - Andres Felipe Zambrano; Ryan S. Baker 001; Andrew S. Lan (ACIIW, 2023)

Tree-Based Representation and Generation of Natural and Mathematical Language. - Alexander Scarlatos; Andrew S. Lan (ACL (1), 2023)

Interpretable Math Word Problem Solution Generation via Step-by-step Planning. - Mengxue Zhang; Zichao Wang 001; Zhichao Yang 001; Weiqi Feng; Andrew S. Lan (ACL (1), 2023)

SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues. - Jaewook Lee 006; Andrew S. Lan (AIED, 2023)

Balancing Test Accuracy and Security in Computerized Adaptive Testing. - Wanyong Feng; Aritra Ghosh 001; Stephen Sireci; Andrew S. Lan (AIED, 2023)

Algebra Error Classification with Large Language Models. - Hunter McNichols; Mengxue Zhang; Andrew S. Lan (AIED, 2023)

Intelligent Textbooks: The Fifth International Workshop. - Sergey A. Sosnovsky; Peter Brusilovsky; Andrew S. Lan (AIED (Posters/Late Breaking Results/...), 2023)

Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank. - Nischal Ashok Kumar; Nigel Fernandez; Zichao Wang 001; Andrew S. Lan (BEA@ACL, 2023)

A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing. - Nischal Ashok Kumar; Wanyong Feng; Jaewook Lee 006; Hunter McNichols; Aritra Ghosh 001; Andrew S. Lan (EDM, 2023)

Modeling and Analyzing Scorer Preferences in Short-Answer Math Questions. - Mengxue Zhang; Neil T. Heffernan; Andrew S. Lan (EDM, 2023)

RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning. - Alexander Scarlatos; Andrew S. Lan (CoRR, 2023)

Exploring Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning. - Hunter McNichols; Wanyong Feng; Jaewook Lee 006; Alexander Scarlatos; Digory Smith; Simon Woodhead 002; Andrew S. Lan (CoRR, 2023)

SyllabusQA: A Course Logistics Question Answering Dataset. - Nigel Fernandez; Alexander Scarlatos; Andrew S. Lan (ACL (1), 2024)

Improving the Validity of Automatically Generated Feedback via Reinforcement Learning. - Alexander Scarlatos; Digory Smith; Simon Woodhead 002; Andrew S. Lan (AIED (1), 2024)

Automatic Short Answer Grading in College Mathematics Using In-Context Meta-learning: An Evaluation of the Transferability of Findings. - Michael Smalenberger; Elham Sohrabi; Mengxue Zhang; Sami Baral; Kelly Smalenberger; Andrew S. Lan; Neil T. Heffernan (AIED Companion (1), 2024)

Improving Socratic Question Generation using Data Augmentation and Preference Optimization. - Nischal Ashok Kumar; Andrew S. Lan (BEA, 2024)

Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank. - Alexander Scarlatos; Wanyong Feng; Andrew S. Lan; Simon Woodhead 002; Digory Smith (BEA, 2024)

8th Educational Data Mining in Computer Science Education (CSEDM) Workshop. - Yang Shi 004; Peter Brusilovsky; Bita Akram; Thomas W. Price; Juho Leinonen 001; Kenneth R. Koedinger; Andrew S. Lan (EDM, 2024)

Math Multiple Choice Question Generation via Human-Large Language Model Collaboration. - Jaewook Lee 006; Digory Smith; Simon Woodhead 002; Andrew S. Lan (EDM, 2024)

Interpreting Latent Student Knowledge Representations in Programming Assignments. - Nigel Fernandez; Andrew S. Lan (EDM, 2024)

Leveraging Large Language Models for Next-Generation Educational Technologies. - Neil T. Heffernan; Rose E. Wang; Christopher MacLellan; Arto Hellas; Chenglu Li; Candace A. Walkington; Joshua Littenberg-Tobias; David Joyner; Steven Moore; Adish Singla; Zach A. Pardos; Maciej Pankiewicz; et al. (EDM, 2024)

Generating Feedback-Ladders for Logical Errors in Programming using Large Language Models. - Hasnain Heickal; Andrew S. Lan (EDM, 2024)

Can Large Language Models Replicate ITS Feedback on Open-Ended Math Questions? - Hunter McNichols; Jaewook Lee 006; Stephen Fancsali; Steven Ritter 001; Andrew S. Lan (EDM, 2024)

From Reaction to Anticipation: Predicting Future Affect. - Andres Felipe Zambrano; Ryan S. Baker 001; Sami Baral; Neil T. Heffernan; Andrew S. Lan (EDM, 2024)

Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank. - Jaewook Lee 006; Hunter McNichols; Andrew S. Lan (EMNLP (Findings), 2024)

DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions. - Nigel Fernandez; Alexander Scarlatos; Wanyong Feng; Simon Woodhead 002; Andrew S. Lan (EMNLP, 2024)

Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models. - Wanyong Feng; Jaewook Lee 006; Hunter McNichols; Alexander Scarlatos; Digory Smith; Simon Woodhead 002; Nancy Otero Ornelas; Andrew S. Lan (NAACL-HLT (Findings), 2024)

Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education. - Nischal Ashok Kumar; Andrew S. Lan (CoRR, 2024)

DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions. - Nigel Fernandez; Alexander Scarlatos; Simon Woodhead 002; Andrew S. Lan (CoRR, 2024)

Exploring Knowledge Tracing in Tutor-Student Dialogues. - Alexander Scarlatos; Andrew S. Lan (CoRR, 2024)

Evaluating GPT-4 at Grading Handwritten Solutions in Math Exams. - Adriana Caraeni; Alexander Scarlatos; Andrew S. Lan (CoRR, 2024)

A Benchmark for Math Misconceptions: Bridging Gaps in Middle School Algebra with AI-Supported Instruction. - Nancy Otero; Stefania Druga; Andrew S. Lan (CoRR, 2024)

Reasoning and Sampling-Augmented MCQ Difficulty Prediction via LLMs. - Wanyong Feng; Peter Tran; Stephen Sireci; Andrew S. Lan (AIED (4), 2025)

Learning Code-Edit Embeddings to Model Student Debugging Behavior. - Hasnain Heickal; Andrew S. Lan (AIED (6), 2025)

Training LLM-Based Tutors to Improve Student Learning Outcomes in Dialogues. - Alexander Scarlatos; Naiming Liu; Jaewook Lee 006; Richard G. Baraniuk; Andrew S. Lan (AIED (1), 2025)

The Efficiency of Teacher-Driven Context Personalization in Mathematics with Large Language Models. - Candace A. Walkington; Theodora Beauchamp; Andrew S. Lan; Tiffini Pruitt-Britton (AIED (4), 2025)

9th Educational Data Mining in Computer Science Education (CSEDM) Workshop. - Bita Akram; Yang Shi 004; Peter Brusilovsky; Thomas W. Price; Ken Koedinger; Paulo Carvalho 004; Shan Zhang 003; Andrew S. Lan; Juho Leinonen 001 (EDM, 2025)

Test Case-Informed Knowledge Tracing for Open-ended Coding Tasks. - Zhangqi Duan; Nigel Fernandez; Alexander Hicks; Andrew S. Lan (LAK, 2025)

Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs. - Alexander Scarlatos; Ryan Shaun Baker; Andrew S. Lan (LAK, 2025)

Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics. - Yun-Wei Chu; Seyyedali Hosseinalipour; Elizabeth Tenorio; Laura Melissa Cruz Castro; Kerrie A. Douglas; Andrew S. Lan; Christopher G. Brinton (IEEE Trans. Emerg. Top. Comput., 2025)

Whose story is it? Personalizing story generation by inferring author styles. - Nischal Ashok Kumar; Chau Minh Pham; Mohit Iyyer; Andrew S. Lan (CoRR, 2025)

Automated Knowledge Component Generation and Knowledge Tracing for Coding Problems. - Zhangqi Duan; Nigel Fernandez; Sri Kanakadandi; Bita Akram; Andrew S. Lan (CoRR, 2025)

Learning Code-Edit Embedding to Model Student Debugging Behavior. - Hasnain Heickal; Andrew S. Lan (CoRR, 2025)

From Text to Visuals: Using LLMs to Generate Math Diagrams with Vector Graphics. - Jaewook Lee; Jeongah Lee; Wanyong Feng; Andrew S. Lan (CoRR, 2025)

The StudyChat Dataset: Student Dialogues With ChatGPT in an Artificial Intelligence Course. - Hunter McNichols; Andrew S. Lan (CoRR, 2025)

LookAlike: Consistent Distractor Generation in Math MCQs. - Nisarg Parikh; Nigel Fernandez; Alexander Scarlatos; Simon Woodhead 002; Andrew S. Lan (CoRR, 2025)

The Use of Large Language Models in Education. - Wanli Xing; Nia Nixon; Scott A. Crossley; Paul Denny 001; Andrew S. Lan; John C. Stamper; Zhou Yu (Int. J. Artif. Intell. Educ., 2025)

SMART: Simulated Students Aligned with Item Response Theory for Question Difficulty Prediction. - Alexander Scarlatos; Nigel Fernandez; Christopher Ormerod; Susan Lottridge; Andrew S. Lan (CoRR, 2025)

Interpretable Mnemonic Generation for Kanji Learning via Expectation-Maximization. - Jaewook Lee; Alexander Scarlatos; Andrew S. Lan (CoRR, 2025)

Exploring LLMs for Predicting Tutor Strategy and Student Outcomes in Dialogues. - Fareya Ikram; Alexander Scarlatos; Andrew S. Lan (CoRR, 2025)

Pattern-based Knowledge Component Extraction from Student Code Using Representation Learning. - Muntasir Hoq; Griffin Pitts; Andrew S. Lan; Peter Brusilovsky; Bita Akram (CoRR, 2025)