| 2025 | AIED | 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 |
| 2025 | EMNLP | Identification of Multiple Logical Interpretations in Counter-Arguments. | Wenzhi Wang, Paul Reisert, Shoichi Naito, Naoya Inoue, Machi Shimmei, Surawat Pothong, Jungmin Choi, Kentaro Inui |
| 2025 | IJCNLP | FOCUS: A Benchmark for Targeted Socratic Question Generation via Source-Span Grounding. | Surawat Pothong, Machi Shimmei, Naoya Inoue, Paul Reisert, Ana Brassard, Wenzhi Wang, Shoichi Naito, Jungmin Choi, Kentaro Inui |
| 2023 | AIED | Machine-Generated Questions Attract Instructors When Acquainted with Learning Objectives. | Machi Shimmei, Norman L. Bier, Noboru Matsuda |
| 2023 | EDM | Can't Inflate Data? Let the Models Unite and Vote: Data-agnostic Method to Avoid Overfit with Small Data. | Machi Shimmei, Noboru Matsuda |
| 2022 | AIED | Automatic Question Generation for Evidence-based Online Courseware Engineering. | Machi Shimmei, Noboru Matsuda |
| 2021 | AIED | Learning Association Between Learning Objectives and Key Concepts to Generate Pedagogically Valuable Questions. | Machi Shimmei, Noboru Matsuda |
| 2020 | EDM | Learning a Policy Primes Quality Control: Towards Evidence-Based Automation of Learning Engineering. | Machi Shimmei, Noboru Matsuda |
| 2019 | AIED | PASTEL: Evidence-based Learning Engineering Method to Create Intelligent Online Textbook at Scale. | Noboru Matsuda, Machi Shimmei |
| 2019 | AIED | Evidence-Based Recommendation for Content Improvement Using Reinforcement Learning. | Machi Shimmei, Noboru Matsuda |