Item Response Model with Lower Order Parameters for Peer Assessment. - Masaki Uto; Maomi Ueno (AIED, 2015)

Academic Writing Support System Using Bayesian Networks. - Masaki Uto; Maomi Ueno (ICALT, 2015)

Reliable Peer Assessment for Team-project-based Learning using Item Response Theory. - Nguyen Duc Thien; Masaki Uto; Yu Abe; Maomi Ueno (ICCE, 2015)

Constraint-Based Learning Bayesian Networks Using Bayes Factor. - Kazuki Natori; Masaki Uto; Yu Nishiyama; Shuichi Kawano; Maomi Ueno (AMBN@JSAI-isAI, 2015)

Item Response Theory for Peer Assessment. - Masaki Uto; Maomi Ueno (IEEE Trans. Learn. Technol., 2016)

Group Optimization to Maximize Peer Assessment Accuracy Using Item Response Theory. - Masaki Uto; Nguyen Duc Thien; Maomi Ueno (AIED, 2017)

Consistent Learning Bayesian Networks with Thousands of Variables. - Kazuki Natori; Masaki Uto; Maomi Ueno (AMBN, 2017)

Item Response Theory Without Restriction of Equal Interval Scale for Rater's Score. - Masaki Uto; Maomi Ueno (AIED (2), 2018)

Exact learning augmented naive Bayes classifier. - Shouta Sugahara; Masaki Uto; Maomi Ueno (PGM, 2018)

Rater-Effect IRT Model Integrating Supervised LDA for Accurate Measurement of Essay Writing Ability. - Masaki Uto (AIED (1), 2019)

Robust Neural Automated Essay Scoring Using Item Response Theory. - Masaki Uto; Masashi Okano (AIED (1), 2020)

Automated Short-Answer Grading Using Deep Neural Networks and Item Response Theory. - Masaki Uto; Yuto Uchida (AIED (2), 2020)

Neural Automated Essay Scoring Incorporating Handcrafted Features. - Masaki Uto; Yikuan Xie; Maomi Ueno (COLING, 2020)

Impact of the number of peers on a mutual assessment as learner's performance in a simulated MOOC environment using the IRT model. - Minoru Nakayama; Filippo Sciarrone; Masaki Uto; Marco Temperini (IV, 2020)

Time- and Learner-Dependent Hidden Markov Model for Writing Process Analysis Using Keystroke Log Data. - Masaki Uto; Yoshimitsu Miyazawa; Yoshihiro Kato; Koji Nakajima; Hajime Kuwata (Int. J. Artif. Intell. Educ., 2020)

Group Optimization to Maximize Peer Assessment Accuracy Using Item Response Theory and Integer Programming. - Masaki Uto; Nguyen Duc Thien; Maomi Ueno (IEEE Trans. Learn. Technol., 2020)

Integration of Automated Essay Scoring Models Using Item Response Theory. - Itsuki Aomi; Emiko Tsutsumi; Masaki Uto; Maomi Ueno (AIED (2), 2021)

A Multidimensional Item Response Theory Model for Rubric-Based Writing Assessment. - Masaki Uto (AIED (1), 2021)

Estimating Ability of Programming Skills using IRT based Peer Assessments. - Minoru Nakayama; Masaki Uto; Marco Temperini; Filippo Sciarrone (ITHET, 2021)

Learning Automated Essay Scoring Models Using Item-Response-Theory-Based Scores to Decrease Effects of Rater Biases. - Masaki Uto; Masashi Okano (IEEE Trans. Learn. Technol., 2021)

Analytic Automated Essay Scoring Based on Deep Neural Networks Integrating Multidimensional Item Response Theory. - Takumi Shibata; Masaki Uto (COLING, 2022)

Evaluation of Programming Skills via Peer Assessment and IRT Estimation Techniques. - Minoru Nakayama; Filippo Sciarrone; Marco Temperini; Masaki Uto (ITHET, 2022)

An Item Response Theory Approach to Enhance Peer Assessment Effectiveness in Massive Open Online Courses. - Minoru Nakayama; Filippo Sciarrone; Marco Temperini; Masaki Uto (Int. J. Distance Educ. Technol., 2022)

Neural Automated Essay Scoring Considering Logical Structure. - Misato Yamaura; Itsuki Fukuda; Masaki Uto (AIED, 2023)

Difficulty-Controllable Neural Question Generation for Reading Comprehension using Item Response Theory. - Masaki Uto; Yuto Tomikawa; Ayaka Suzuki (BEA@ACL, 2023)

Neural Automated Short-Answer Grading Considering Examinee-Specific Features. - Masaki Uto (ICALT, 2023)

Feasibility of Prediction of Student's Characteristics Using Texts of Essays Written During a Fully Online Course. - Minoru Nakayama; Masaki Uto; Satoru Kikuchi; Hiroh Yamamoto (IV, 2023)

Integration of Prediction Scores From Various Automated Essay Scoring Models Using Item Response Theory. - Masaki Uto; Itsuki Aomi; Emiko Tsutsumi; Maomi Ueno (IEEE Trans. Learn. Technol., 2023)

Collaborative Essay Evaluation with Human and Neural Graders Using Item Response Theory Under a Nonequivalent Groups Design. - Kota Aramaki; Masaki Uto (AIED Companion (2), 2024)

ChatGPT-Based Virtual Standardized Patient that Amends Overly Detailed Responses in Objective Structured Clinical Examinations. - Naoki Shindo; Masaki Uto (AIED Companion (1), 2024)

Difficulty-Controllable Multiple-Choice Question Generation for Reading Comprehension Using Item Response Theory. - Yuto Tomikawa; Masaki Uto (AIED Companion (1), 2024)

Neural Automated Essay Scoring for Improved Confidence Estimation and Score Prediction Through Integrated Classification and Regression. - Masaki Uto; Yuto Takahashi (AIED Companion (1), 2024)

Enhancing Cross-prompt Automated Essay Scoring by Selecting Training Data Based on Reinforcement Learning. - Takumi Shibata; Masaki Uto (EcalLAC@AIED, 2024)

Question Difficulty Prediction Based on Virtual Test-Takers and Item Response Theory. - Masaki Uto; Yuto Tomikawa; Ayaka Suzuki (EcalLAC@AIED, 2024)

Enhancing Diversity in Difficulty-Controllable Question Generation for Reading Comprehension via Extended T5. - Teruyoshi Goto; Yuto Tomikawa; Masaki Uto (ICCE, 2024)

Difficulty-Controllable Reading Comprehension Question Generation Considering the Difficulty of Reading Passages. - Yuto Tomikawa; Masaki Uto (ICCE, 2024)

Predicting Factor Scores of Critical Thinking Ability From Features of Essay Texts. - Minoru Nakayama; Masaki Uto; Satoru Kikuchi; Hiroh Yamamoto (ITHET, 2024)

Appropriate Number of Raters for IRT Based Peer Assessment Evaluation of Programming Skills. - Minoru Nakayama; Masaki Uto; Marco Temperini; Filippo Sciarrone (ITHET, 2024)

Estimating Scores of Critical Thinking Ability Using Essay Text Assessments. - Minoru Nakayama; Masaki Uto; Satoru Kikuchi; Hiroh Yamamoto (IV, 2024)

Adaptive Question-Answer Generation With Difficulty Control Using Item Response Theory and Pretrained Transformer Models. - Yuto Tomikawa; Ayaka Suzuki; Masaki Uto (IEEE Trans. Learn. Technol., 2024)

Tell Me Who Your Students Are: GPT Can Generate Valid Multiple-Choice Questions When Students' (Mis)Understanding Is Hinted. - Machi Shimmei; Masaki Uto; Yuichiroh Matsubayashi; Kentaro Inui; Aditi Mallavarapu; Noboru Matsuda (CoRR, 2025)

Leveraging AI Graders for Missing Score Imputation to Achieve Accurate Ability Estimation in Constructed-Response Tests. - Masaki Uto; Yuma Ito (CoRR, 2025)