{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T16:48:14Z","timestamp":1782146894507,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","funder":[{"name":"Learning Engineering Virtual Institute","award":["G-23-2137070"],"award-info":[{"award-number":["G-23-2137070"]}]},{"name":"Jaffe Foundation","award":["AGR00026932"],"award-info":[{"award-number":["AGR00026932"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,29]]},"DOI":"10.1145\/3774398.3811566","type":"proceedings-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T15:50:37Z","timestamp":1782143437000},"page":"448-452","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Generate-Filter-Edit: A Human-AI Collaborative Pipeline for Developing and Automatically Evaluating Middle School Mathematics Questions"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7299-2042","authenticated-orcid":false,"given":"Hai","family":"Li","sequence":"first","affiliation":[{"name":"University of Florida, Gainesville, Florida, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1446-889X","authenticated-orcid":false,"given":"Wanli","family":"Xing","sequence":"additional","affiliation":[{"name":"University of Miami, Miami, Florida, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1782-0457","authenticated-orcid":false,"given":"Chenglu","family":"Li","sequence":"additional","affiliation":[{"name":"University of Utah, Salt Lak City, Utah, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2567-0473","authenticated-orcid":false,"given":"Ran","family":"Gao","sequence":"additional","affiliation":[{"name":"University of Florida, Gainesville, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/2043132.2043139"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1186\/s12909-025-06881-w","article-title":"Quality assurance and validity of AI-generated single best answer questions","volume":"25","author":"Ahmed Ayla","year":"2025","unstructured":"Ayla Ahmed, Ellen Kerr, and Andrew O'Malley. 2025. Quality assurance and validity of AI-generated single best answer questions. BMC Medical Education, Vol. 25, 1 (2025), 300.","journal-title":"BMC Medical Education"},{"key":"e_1_3_2_1_3_1","volume-title":"Munazza Zaib, and Ahoud Alhazmi.","author":"Alhazmi Elaf","year":"2024","unstructured":"Elaf Alhazmi, Quan Z Sheng, Wei Emma Zhang, Munazza Zaib, and Ahoud Alhazmi. 2024. Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation. arXiv preprint arXiv:2402.01512 (2024)."},{"key":"e_1_3_2_1_4_1","volume-title":"ICERI2024 Proceedings. IATED, 1312-1321","author":"Attard A","year":"2024","unstructured":"A Attard and A Dingli. 2024. Empowering educators: Leveraging large language models to streamline content creation in education. In ICERI2024 Proceedings. IATED, 1312-1321."},{"key":"e_1_3_2_1_5_1","volume-title":"Evaluation of text generation: A survey. arXiv preprint arXiv:2006.14799","author":"Celikyilmaz Asli","year":"2020","unstructured":"Asli Celikyilmaz, Elizabeth Clark, and Jianfeng Gao. 2020. Evaluation of text generation: A survey. arXiv preprint arXiv:2006.14799 (2020)."},{"key":"e_1_3_2_1_6_1","volume-title":"Can AI writing be salvaged? Mitigating Idiosyncrasies and Improving Human-AI Alignment in the Writing Process through Edits. arXiv preprint arXiv:2409.14509","author":"Chakrabarty Tuhin","year":"2024","unstructured":"Tuhin Chakrabarty, Philippe Laban, and Chien-Sheng Wu. 2024. Can AI writing be salvaged? Mitigating Idiosyncrasies and Improving Human-AI Alignment in the Writing Process through Edits. arXiv preprint arXiv:2409.14509 (2024)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3027385.3027399"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","first-page":"251","DOI":"10.17239\/jowr-2019.11.02.01","article-title":"Using human judgments to examine the validity of automated grammar, syntax, and mechanical errors in writing","volume":"11","author":"Crossley Scott A","year":"2019","unstructured":"Scott A Crossley, Franklin Bradfield, and Analynn Bustamante. 2019. Using human judgments to examine the validity of automated grammar, syntax, and mechanical errors in writing. Journal of Writing Research, Vol. 11, 2 (2019), 251-270.","journal-title":"Journal of Writing Research"},{"key":"e_1_3_2_1_9_1","volume-title":"The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion. Behavior research methods","author":"Crossley Scott A","year":"2016","unstructured":"Scott A Crossley, Kristopher Kyle, and Danielle S McNamara. 2016. The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion. Behavior research methods, Vol. 48, 4 (2016), 1227-1237."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-023-07840-7"},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the Third Workshop on Intelligent and Interactive Writing Assistants. 16-22","author":"Huang Chieh-Yang","year":"2024","unstructured":"Chieh-Yang Huang, Jing Wei, and Ting-Hao Kenneth Huang. 2024. Generating educational materials with different levels of readability using LLMs. In Proceedings of the Third Workshop on Intelligent and Interactive Writing Assistants. 16-22."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","first-page":"100087","DOI":"10.1016\/j.rmal.2023.100087","article-title":"Modelling the use of the tool for the automatic analysis of syntactic sophistication and complexity (TAASSC)","volume":"3","author":"Kim Sangeun","year":"2024","unstructured":"Sangeun Kim, Phoenix Williams, and Lee McCallum. 2024. Modelling the use of the tool for the automatic analysis of syntactic sophistication and complexity (TAASSC). Research Methods in Applied Linguistics, Vol. 3, 1 (2024), 100087.","journal-title":"Research Methods in Applied Linguistics"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40593-023-00333-6"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40593-019-00186-y"},{"key":"e_1_3_2_1_15_1","volume-title":"The tool for the automatic analysis of lexical sophistication (TAALES): Version 2.0. Behavior research methods","author":"Kyle Kristopher","year":"2018","unstructured":"Kristopher Kyle, Scott Crossley, and Cynthia Berger. 2018. The tool for the automatic analysis of lexical sophistication (TAALES): Version 2.0. Behavior research methods, Vol. 50, 3 (2018), 1030-1046."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1080\/15434303.2020.1844205"},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the Eleventh ACM Conference on Learning@ Scale. 110-121","author":"Li Hai","year":"2024","unstructured":"Hai Li, Rui Guo, Chenglu Li, and Wanli Xing. 2024a. Automated quality assessment of multimodal mathematical stories generated by generative artificial intelligence. In Proceedings of the Eleventh ACM Conference on Learning@ Scale. 110-121."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3636555.3636860"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3706468.3706537"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjet.13554"},{"key":"e_1_3_2_1_21_1","volume-title":"Leveraging Multi-modality and Collaborative Filtering for Supporting Automatic Scoring in Mathematics Education. In International Conference on Artificial Intelligence in Education. Springer, 313-320","author":"Li Hai","year":"2025","unstructured":"Hai Li, Wanli Xing, Wangda Zhu, Chenglu Li, Bailing Lyu, Zifeng Liu, and Neil Heffernan. 2025c. Leveraging Multi-modality and Collaborative Filtering for Supporting Automatic Scoring in Mathematics Education. In International Conference on Artificial Intelligence in Education. Springer, 313-320."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.in2writing-1.7"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1162\/coli_a_00398"},{"key":"e_1_3_2_1_24_1","unstructured":"Danielle S McNamara Max M Louwerse and Arthur C Graesser. 2002. Coh-Metrix: Automated cohesion and coherence scores to predict text readability and facilitate comprehension. Technical Report. Technical report Institute for Intelligent Systems University of Memphis \u2026."},{"key":"e_1_3_2_1_25_1","volume-title":"Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers college record","author":"Mishra Punya","year":"2006","unstructured":"Punya Mishra and Matthew J Koehler. 2006. Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers college record, Vol. 108, 6 (2006), 1017-1054."},{"key":"e_1_3_2_1_26_1","first-page":"147","article-title":"Automatic generation of cloze items for repeated testing to improve reading comprehension","volume":"24","author":"Yang Albert CM","year":"2021","unstructured":"Albert CM Yang, Irene YL Chen, Brendan Flanagan, and Hiroaki Ogata. 2021. Automatic generation of cloze items for repeated testing to improve reading comprehension. Educational Technology & Society, Vol. 24, 3 (2021), 147-158.","journal-title":"Educational Technology & Society"},{"key":"e_1_3_2_1_27_1","first-page":"17","article-title":"Automated Extraction of Values of Quantitative Indicators to a Quality Evaluation System Using Natural Language Analysis Tools","author":"Zhekova Mariya","year":"2021","unstructured":"Mariya Zhekova, George Pashev, George Totkov, and Silvia Gaftandzhieva. 2021. Automated Extraction of Values of Quantitative Indicators to a Quality Evaluation System Using Natural Language Analysis Tools.. In ERIS. 17-28.","journal-title":"ERIS."}],"event":{"name":"L@S '26: Thirteenth ACM Conference on Learning @ Scale","location":"Seoul Republic of Korea"},"container-title":["Proceedings of the Thirteenth ACM Conference on Learning @ Scale"],"original-title":[],"deposited":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T15:57:20Z","timestamp":1782143840000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774398.3811566"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,28]]},"references-count":27,"alternative-id":["10.1145\/3774398.3811566","10.1145\/3774398"],"URL":"https:\/\/doi.org\/10.1145\/3774398.3811566","relation":{},"subject":[],"published":{"date-parts":[[2026,6,28]]},"assertion":[{"value":"2026-06-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}