{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T07:28:00Z","timestamp":1774250880912,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":13,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819571437","type":"print"},{"value":"9789819571444","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-7144-4_12","type":"book-chapter","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T06:33:28Z","timestamp":1774247608000},"page":"145-153","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring Exercises Selection Methods for\u00a0Computerized Adaptive Testing Based on\u00a0Large Language Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3014-0522","authenticated-orcid":false,"given":"Tianle","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3009-4721","authenticated-orcid":false,"given":"Hai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3115-6855","authenticated-orcid":false,"given":"Haiping","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Ma, H., Xia, A., Wang, C., Wang, H., Zhang, X.: Diffusion-inspired cold start with sufficient prior in computerized adaptive testing. arXiv preprint arXiv:2411.12182 (2024)","DOI":"10.1145\/3690624.3709317"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Embretson, S.\u00a0E., Reise, S.\u00a0P.: Item response theory. Psychology Press (2013)","DOI":"10.4324\/9781410605269"},{"key":"12_CR3","unstructured":"Wei, J., et\u00a0al.: Chain-of-thought prompting elicits reasoning in large language models. Adv. Neural Inf. Process. Syst.,35, 24\u00a0824\u201324\u00a0837 (2022)"},{"issue":"2","key":"12_CR4","first-page":"3","volume":"1","author":"EJ Hu","year":"2022","unstructured":"Hu, E.J., et al.: Lora: Low-rank adaptation of large language models. ICLR 1(2), 3 (2022)","journal-title":"ICLR"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Bi, H., et al.: Quality meets diversity: a model-agnostic framework for computerized adaptive testing. In: 2020 IEEE International Conference on Data Mining (ICDM). IEEE, pp. 42\u201351 (2020)","DOI":"10.1109\/ICDM50108.2020.00013"},{"key":"12_CR6","first-page":"2381","volume":"36","author":"Y Zhuang","year":"2023","unstructured":"Zhuang, Y., et al.: A bounded ability estimation for computerized adaptive testing. Adv. Neural. Inf. Process. Syst. 36, 2381\u20132402 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"4","key":"12_CR7","first-page":"4734","volume":"36","author":"Y Zhuang","year":"2022","unstructured":"Zhuang, Y., et al.: Fully adaptive framework: neural computerized adaptive testing for online education. Proceed. AAAI conf. artifi. intell. 36(4), 4734\u20134742 (2022)","journal-title":"Proceed. AAAI conf. artifi. intell."},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Hong, Y., et al.: Search-efficient computerized adaptive testing. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 773\u2013782 (2023)","DOI":"10.1145\/3583780.3615049"},{"key":"12_CR9","unstructured":"Liu, Z., et al.: Computerized adaptive testing via collaborative ranking. Adv. Neural Inf. Process. Syst., 37, 95\u00a0488\u201395\u00a0514 (2024)"},{"key":"12_CR10","unstructured":"Yu, J., et al.: A unified adaptive testing system enabled by hierarchical structure search. In: Forty-first International Conference on Machine Learning (2024)"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Yu, J., et\u00a0al.: Moocradar: a fine-grained and multi-aspect knowledge repository for improving cognitive student modeling in moocs, In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2924\u20132934 (2023)","DOI":"10.1145\/3539618.3591898"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Van\u00a0der Linden, W.\u00a0J., Pashley, P.\u00a0J.: Item selection and ability estimation in adaptive testing. In: Elements of adaptive testing. Springer, pp. 3\u201330 (2009)","DOI":"10.1007\/978-0-387-85461-8_1"},{"issue":"3","key":"12_CR13","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1177\/014662169602000303","volume":"20","author":"H-H Chang","year":"1996","unstructured":"Chang, H.-H., Ying, Z.: A global information approach to computerized adaptive testing. Appl. Psychol. Meas. 20(3), 213\u2013229 (1996)","journal-title":"Appl. Psychol. Meas."}],"container-title":["Lecture Notes in Computer Science","Behavioural and Social Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-7144-4_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T06:33:34Z","timestamp":1774247614000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-7144-4_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819571437","9789819571444"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-7144-4_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BESC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Behavioural and Social Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong SAR","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"16 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"besc2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/besc-conf.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}