{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:37:14Z","timestamp":1757619434099,"version":"3.44.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031992636"},{"type":"electronic","value":"9783031992643"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-99264-3_21","type":"book-chapter","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T06:43:14Z","timestamp":1753252994000},"page":"169-176","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ScoreCLIQ: A Dynamic LLM-Based Framework for\u00a0Item Difficulty Estimation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7938-5534","authenticated-orcid":false,"given":"Soujatya","family":"Sarkar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2640-8528","authenticated-orcid":false,"given":"Manikandan","family":"Ravikiran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0773-3480","authenticated-orcid":false,"given":"Rohit","family":"Saluja","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"key":"21_CR1","unstructured":"Bahdanau, D., et al.: An actor-critic algorithm for sequence prediction. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"21_CR2","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1162\/tacl_a_00200","volume":"2","author":"L Beinborn","year":"2014","unstructured":"Beinborn, L., Zesch, T., Gurevych, I.: Predicting the difficulty of language proficiency tests. Trans. Assoc. Comput. Linguist. 2, 517\u2013530 (2014)","journal-title":"Trans. Assoc. Comput. Linguist."},{"issue":"4","key":"21_CR3","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1111\/j.1745-3992.1998.tb00631.x","volume":"18","author":"RE Bennett","year":"1999","unstructured":"Bennett, R.E.: Validity and automated scoring: it\u2019s not only the scoring. Educ. Meas. Issues Pract. 18(4), 9\u201316 (1999)","journal-title":"Educ. Meas. Issues Pract."},{"key":"21_CR4","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423. https:\/\/aclanthology.org\/N19-1423\/","DOI":"10.18653\/v1\/N19-1423"},{"key":"21_CR5","unstructured":"Due\u00f1as, G., Jimenez, S., Ferro, G.M.: Upn-icc at bea 2024 shared task: leveraging llms for multiple-choice questions difficulty prediction. In: Workshop on Innovative Use of NLP for Building Educational Applications (2024). https:\/\/api.semanticscholar.org\/CorpusID:270766389"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Fadaee, M., Bisazza, A., Monz, C.: Data augmentation for low-resource neural machine translation. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 567\u2013573 (2017)","DOI":"10.18653\/v1\/P17-2090"},{"key":"21_CR7","unstructured":"Felice, M., Duran\u00a0Karaoz, Z.: The British council submission to the BEA 2024 shared task. In: Kochmar, E., et al. (eds.) Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pp. 503\u2013511. Association for Computational Linguistics, Mexico City (2024). https:\/\/aclanthology.org\/2024.bea-1.42\/"},{"key":"21_CR8","unstructured":"Fulari, R., Rusert, J.: Utilizing machine learning to predict question difficulty and response time for enhanced test construction. In: Kochmar, E., et al. (eds.) Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pp. 528\u2013533. Association for Computational Linguistics, Mexico City (2024). https:\/\/aclanthology.org\/2024.bea-1.45\/"},{"key":"21_CR9","unstructured":"Gombert, S., Menzel, L., Di\u00a0Mitri, D., Drachsler, H.: Predicting item difficulty and item response time with scalar-mixed transformer encoder models and rational network regression heads. In: Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024) (2024)"},{"issue":"3","key":"21_CR10","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1111\/j.1745-3992.2007.00098.x","volume":"26","author":"JS Gorin","year":"2007","unstructured":"Gorin, J.S.: Cognition and assessment: a critical issue in the field of testing. Educ. Meas. Issues Pract. 26(3), 21\u201329 (2007)","journal-title":"Educ. Meas. Issues Pract."},{"key":"21_CR11","doi-asserted-by":"publisher","unstructured":"Grattafiori, A., et\u00a0al.: The llama 3 herd of models (2024). https:\/\/doi.org\/10.48550\/ARXIV.2407.21783. arxiv:2407.21783","DOI":"10.48550\/ARXIV.2407.21783"},{"key":"21_CR12","unstructured":"Grosse, R., et\u00a0al.: Can large language models be used as reward models? In: NeurIPS 2023 (2023)"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Huang, Y., Zhao, J., et\u00a0al.: Self-instruct: aligning language models with self-generated instructions. arXiv preprint arXiv:2212.10560 (2023)","DOI":"10.18653\/v1\/2023.acl-long.754"},{"key":"21_CR14","doi-asserted-by":"publisher","unstructured":"Jiang, A.Q., et al.: Mistral 7b (2023). https:\/\/doi.org\/10.48550\/ARXIV.2310.06825. arxiv:2310.06825","DOI":"10.48550\/ARXIV.2310.06825"},{"key":"21_CR15","unstructured":"Kumar, D., Ray, S., Chakrabarti, S.: Data augmentation using pre-trained transformer models. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2750\u20132764 (2020)"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Li, J., Monroe, W., Shi, T., Jean, S., Ritter, A., Jurafsky, D.: Deep reinforcement learning for dialogue generation. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1192\u20131202 (2016)","DOI":"10.18653\/v1\/D16-1127"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Lord, F.M.: A Theory of Test Scores. Psychometric Society (1952)","DOI":"10.1002\/j.2333-8504.1952.tb00926.x"},{"key":"21_CR18","unstructured":"Ram, G.V.R., Kesanam, A., M, A.K.: Leveraging physical and semantic features of text item for difficulty and response time prediction of usmle questions. In: Workshop on Innovative Use of NLP for Building Educational Applications (2024). https:\/\/api.semanticscholar.org\/CorpusID:270766188"},{"key":"21_CR19","unstructured":"Rogoz, A.C., Ionescu, R.T.: Unibucllm: harnessing llms for automated prediction of item difficulty and response time. In: Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (2024)"},{"key":"21_CR20","unstructured":"Tack, A., et al.: Itec at bea 2024 shared task: Predicting difficulty and response time of medical exam questions with statistical, machine learning, and language models. In: Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pp. 512\u2013521 (2024)"},{"key":"21_CR21","unstructured":"Veeramani, H., Thapa, S., Shankar, N.B., Alwan, A.: Large language model-based pipeline for item difficulty and response time estimation for educational assessments. In: Workshop on Innovative Use of NLP for Building Educational Applications (2024). https:\/\/api.semanticscholar.org\/CorpusID:270766172"},{"key":"21_CR22","doi-asserted-by":"publisher","unstructured":"Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. In: Machine Learning, vol.\u00a08, pp. 229\u2013256. Springer, Heidelberg (1992). https:\/\/doi.org\/10.1007\/bf00992696","DOI":"10.1007\/bf00992696"},{"key":"21_CR23","unstructured":"Yaneva, V., et al.: Findings from the first shared task on automated prediction of difficulty and response time for multiple-choice questions. In: Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pp. 470\u2013482. Association for Computational Linguistics, Mexico City (2024). https:\/\/aclanthology.org\/2024.bea-1.39\/"}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, Blue Sky, and WideAIED"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-99264-3_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T19:53:00Z","timestamp":1757274780000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-99264-3_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031992636","9783031992643"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-99264-3_21","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"21 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIED","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Education","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Palermo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aied2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aied2025.itd.cnr.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}