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

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

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)