{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T07:47:10Z","timestamp":1782546430538,"version":"3.54.5"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032297594","type":"print"},{"value":"9783032297600","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T00:00:00Z","timestamp":1782604800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T00:00:00Z","timestamp":1782604800000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-29760-0_41","type":"book-chapter","created":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T07:11:20Z","timestamp":1782544280000},"page":"370-379","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["When Can We Trust AI Coding of\u00a0Student-Generated Text? A Committee-Based Approach to\u00a0Diagnosing Agreement and\u00a0Uncertainty at\u00a0Scale"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7016-6354","authenticated-orcid":false,"given":"Fanjie","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6395-0976","authenticated-orcid":false,"given":"Madison Lee","family":"Mason","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2652-0472","authenticated-orcid":false,"given":"Daniel T.","family":"Levin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4043-6808","authenticated-orcid":false,"given":"Alyssa Friend","family":"Wise","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,28]]},"reference":[{"key":"41_CR1","unstructured":"Anthropic: Let Claude think (CoT). https:\/\/platform.claude.com\/docs\/en\/build-with-claude\/prompt-engineering\/chain-of-thought"},{"key":"41_CR2","doi-asserted-by":"crossref","unstructured":"Cohn, C., Hutchins, N., Le, T., Biswas, G.: A chain-of-thought prompting approach with LLMs for evaluating students\u2019 formative assessment responses in science. In: Proceedings of AAAI Conference on Artificial Intelligence, pp. 23182\u201323190 (2024)","DOI":"10.1609\/aaai.v38i21.30364"},{"issue":"6","key":"41_CR3","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.ecns.2013.04.008","volume":"9","author":"S Decker","year":"2013","unstructured":"Decker, S., et al.: Standards of best practice: simulation standard VI: The debriefing process. Clin. Simul. Nurs. 9(6), 26\u201329 (2013)","journal-title":"Clin. Simul. Nurs."},{"key":"41_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/frai.2025.1609097","volume":"8","author":"D Herrera-Poyatos","year":"2025","unstructured":"Herrera-Poyatos, D., et al.: An overview of model uncertainty and variability in LLM-based sentiment analysis: challenges, mitigation strategies, and the role of explainability. Front. Artif. Intell. 8, 1\u201324 (2025)","journal-title":"Front. Artif. Intell."},{"key":"41_CR5","doi-asserted-by":"crossref","unstructured":"Jamison, E., Gurevych, I.: Noise or additional information? Leveraging crowdsource annotation item agreement for natural language tasks. In: Proceedings of 2015 Conference on Empirical Methods in Natural Language Processing, pp. 291\u2013297 (2015)","DOI":"10.18653\/v1\/D15-1035"},{"key":"41_CR6","doi-asserted-by":"crossref","unstructured":"Krippendorff, K.: Content Analysis: An Introduction to its Methodology (2019)","DOI":"10.4135\/9781071878781"},{"key":"41_CR7","doi-asserted-by":"crossref","unstructured":"Lajoie, S.P., Gube, M.: Adaptive expertise in medical education: accelerating learning trajectories by fostering self-regulated learning. Med. Teach. 40(8) (2018)","DOI":"10.1080\/0142159X.2018.1485886"},{"key":"41_CR8","unstructured":"Li, F., Mason, M.L., Levin, D., Wise, A.: Using RE-LLM coding uncertainty to resolve codebook ambiguities: an example of the CLARIFY toolset and workflow in action. In: Joint Proceedings of LAK 2026 Workshops, pp. 1\u201310 (2026)"},{"key":"41_CR9","unstructured":"Mason, M.L., Jessee, M.A., Levin, D.T.: Transforming experiences into expertise: leveraging event cognition to support self-regulation in a practical learning system (2026), in review"},{"key":"41_CR10","doi-asserted-by":"crossref","unstructured":"Ramanathan, S., Lim, L.A., Mottaghi, N.R., Buckingham Shum, S.: When the prompt becomes the codebook: grounded prompt engineering (GROPROE) and its application to belonging analytics. In: LAK\u201925 Proceedings, pp. 713\u2013725. ACM (2025)","DOI":"10.1145\/3706468.3706564"},{"key":"41_CR11","doi-asserted-by":"crossref","unstructured":"Scarlatos, A., Baker, R.S., Lan, A.: Exploring knowledge tracing in tutor-student dialogues using LLMs. In: LAK\u201925 Proceedings, pp. 249\u2013259. ACM (2025)","DOI":"10.1145\/3706468.3706501"},{"key":"41_CR12","unstructured":"Settles, B.: Active learning literature survey, University of Wisconsin-Madison Department of Computer Sciences (2009). TR1648 Tech. Rep"},{"issue":"4","key":"41_CR13","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1080\/10584609.2020.1723752","volume":"37","author":"H Song","year":"2020","unstructured":"Song, H., et al.: In validations we trust? the impact of imperfect human annotations as a gold standard on the quality of validation of automated content analysis. Polit. Commun. 37(4), 550\u2013572 (2020)","journal-title":"Polit. Commun."},{"key":"41_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/16094069241231168","volume":"23","author":"RH Tai","year":"2024","unstructured":"Tai, R.H., et al.: An examination of the use of large language models to aid analysis of textual data. Int J Qual Methods 23, 1\u201314 (2024)","journal-title":"Int J Qual Methods"},{"issue":"6","key":"41_CR15","doi-asserted-by":"publisher","first-page":"204","DOI":"10.3928\/01484834-20060601-04","volume":"45","author":"CA Tanner","year":"2006","unstructured":"Tanner, C.A.: Thinking like a nurse: a research-based model of clinical judgment in nursing. J. Nurs. Educ. 45(6), 204\u2013211 (2006)","journal-title":"J. Nurs. Educ."},{"key":"41_CR16","unstructured":"Wang, X.: Self-consistency improves chain of thought reasoning in language models (2023). arXiv:2203.11171 cs.CL"},{"key":"41_CR17","unstructured":"Zhang, Y.: Consensus entropy: Harnessing multi-VLM agreement for self-verifying and self-improving OCR (2025). arXiv:2504.11101 cs.CV"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-29760-0_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T07:11:31Z","timestamp":1782544291000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-29760-0_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,28]]},"ISBN":["9783032297594","9783032297600"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-29760-0_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,28]]},"assertion":[{"value":"28 June 2026","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":"Seoul","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aied2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.aied-conference.org\/2026","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}