{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T07:47:07Z","timestamp":1782546427122,"version":"3.54.5"},"publisher-location":"Cham","reference-count":23,"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_61","type":"book-chapter","created":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T07:09:23Z","timestamp":1782544163000},"page":"639-653","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Examples to\u00a0Rules? Exploring Inductive Reverse Engineering and\u00a0Deductive Few-Shot Coding via\u00a0LLMs for\u00a0Qualitative Data Analysis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5833-2141","authenticated-orcid":false,"given":"Zifeng","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9956-433X","authenticated-orcid":false,"given":"Anupom","family":"Mondol","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9496-609X","authenticated-orcid":false,"given":"Xinyue","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9153-3552","authenticated-orcid":false,"given":"Jie","family":"Chao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0119-5367","authenticated-orcid":false,"given":"Linlin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1446-889X","authenticated-orcid":false,"given":"Wanli","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,28]]},"reference":[{"key":"61_CR1","unstructured":"Preface. In: From data to discovery: LLMs for qualitative analysis in education (LLM-QUAL). ACM (2025). workshop preface, LAK 2025"},{"key":"61_CR2","unstructured":"Bakharia, A., Shibani, A., Lim, L.A., McCluskey, T., Shum, S.B.: From transcripts to themes: a trustworthy workflow for qualitative analysis using large language models. In: Proceedings of the 15th International Conference on Learning Analytics and Knowledge (LAK 2025). ACM, New York, NY, USA (2025)"},{"key":"61_CR3","doi-asserted-by":"publisher","unstructured":"Barany, A., et al.: ChatGPT for education research: exploring the potential of large language models for qualitative codebook development. In: Artificial Intelligence in Education, vol. 14830, pp. 99\u2013107. Springer (2024). https:\/\/doi.org\/10.1007\/978-3-031-64299-9_10","DOI":"10.1007\/978-3-031-64299-9_10"},{"key":"61_CR4","doi-asserted-by":"crossref","unstructured":"Barros, C.F., et al.: Large language model for qualitative research: a systematic mapping study. In: IEEE\/ACM International Workshop on Methodological Issues with Empirical Studies in Software Engineering (WSESE), pp. 48\u201355. IEEE (2025)","DOI":"10.1109\/WSESE66602.2025.00015"},{"issue":"1","key":"61_CR5","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1109\/52.43044","volume":"7","author":"EJ Chikofsky","year":"1990","unstructured":"Chikofsky, E.J., Cross, J.H.: Reverse engineering and design recovery: a taxonomy. IEEE Softw. 7(1), 13\u201317 (1990). https:\/\/doi.org\/10.1109\/52.43044","journal-title":"IEEE Softw."},{"key":"61_CR6","doi-asserted-by":"publisher","unstructured":"Davison, R.M., et al.: The ethics of using generative AI for qualitative data analysis. Inf. Syst. J. 34, 1433\u20131439 (2024). https:\/\/doi.org\/10.1111\/isj.12504","DOI":"10.1111\/isj.12504"},{"issue":"4","key":"61_CR7","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1177\/08944393231220483","volume":"42","author":"S De Paoli","year":"2024","unstructured":"De Paoli, S.: Performing an inductive thematic analysis of semi-structured interviews with a large language model: an exploration and provocation on the limits of the approach. Soc. Sci. Comput. Rev. 42(4), 997\u20131019 (2024). https:\/\/doi.org\/10.1177\/08944393231220483","journal-title":"Soc. Sci. Comput. Rev."},{"key":"61_CR8","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1140\/epjds\/s13688-025-00548-8","volume":"14","author":"ZO Dunivin","year":"2025","unstructured":"Dunivin, Z.O.: Scaling hermeneutics: a guide to qualitative coding with LLMS for reflexive content analysis. EPJ Data Sci. 14, 28 (2025). https:\/\/doi.org\/10.1140\/epjds\/s13688-025-00548-8","journal-title":"EPJ Data Sci."},{"key":"61_CR9","unstructured":"Gu, M., et al.: Toward automated qualitative analysis: leveraging large language models for tutoring dialogue evaluation. arXiv preprint arXiv:2504.13882 (2025)"},{"key":"61_CR10","unstructured":"Jiang, H., Corrigan, S., Peppler, K., Erana, T.I.: Bridging human and machine perspectives: integrating large language models into collaborative coding and peer debriefing for qualitative inquiry. In: Joint Proceedings of LAK 2025 Workshops (2025)"},{"key":"61_CR11","unstructured":"Kapoor, S., Gil, A., Bhaduri, S., Mittal, A., Mulkar, R.: Qualitative insights tool (qualit): LLM enhanced topic modeling. arXiv preprint arXiv:2409.15626 (2024)"},{"key":"61_CR12","doi-asserted-by":"crossref","unstructured":"Lacy, J.W., Nnoka, C., Jock, Z., Morreale, C.: LLM sentiment quantification reveals selective alignment with human course-evaluation raters. Comput. Educ. Artif. Intell., 100545 (2026)","DOI":"10.1016\/j.caeai.2026.100545"},{"key":"61_CR13","unstructured":"Lin, G.C., et al.: Collaborative AI for qualitative analysis: bridging AI and human expertise for scalable analysis. In: Proceedings of the 15th International Conference on Learning Analytics and Knowledge, ACM, LAK (2025)"},{"key":"61_CR14","doi-asserted-by":"publisher","unstructured":"Liu, X., et al.: Qualitative coding with GPT-4: where it works better. J. Learn. Anal. 12(1), 169\u2013185 (2025). https:\/\/doi.org\/10.18608\/jla.2025.8575","DOI":"10.18608\/jla.2025.8575"},{"key":"61_CR15","volume-title":"Qualitative Data Analysis: A Methods Sourcebook","author":"MB Miles","year":"2014","unstructured":"Miles, M.B., Huberman, A.M., Salda\u00f1a, J.: Qualitative Data Analysis: A Methods Sourcebook, 3rd edn. SAGE Publications, Thousand Oaks, CA (2014)","edition":"3"},{"key":"61_CR16","doi-asserted-by":"crossref","unstructured":"Min, S., Shi, W., Yao, M., Zhou, Y., Zettlemoyer, L., Hajishirzi, H.: Rethinking the role of demonstrations: what makes in-context learning work? In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 11048\u201311064 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.759"},{"key":"61_CR17","doi-asserted-by":"crossref","unstructured":"Parfenova, A., Marfurt, A., Pfeffer, J., Denzler, J.: Text annotation via inductive coding: comparing human experts to LLMs in qualitative data analysis. In: Findings of the Association for Computational Linguistics: NAACL 2025, pp. 6456\u20136469. Association for Computational Linguistics (2025)","DOI":"10.18653\/v1\/2025.findings-naacl.361"},{"issue":"4","key":"61_CR18","first-page":"889","volume":"23","author":"R Rogers","year":"2018","unstructured":"Rogers, R.: Coding and writing analytic memos on qualitative data: a review of johnny salda\u00f1a\u2019s the coding manual for qualitative researchers. Qual. Rep. 23(4), 889\u2013892 (2018)","journal-title":"Qual. Rep."},{"key":"61_CR19","doi-asserted-by":"crossref","unstructured":"Sankaranarayanan, S., et al.: Automating thematic analysis with multi-agent LLM systems. In: Joint Proceedings of LAK 2025 Workshops (2025)","DOI":"10.35542\/osf.io\/kq8zh_v1"},{"key":"61_CR20","unstructured":"Shah, S.T.U., Hussein, M., Barcomb, A., Moshirpour, M.: From inductive to deductive: LLMs-based qualitative data analysis in requirements engineering. arXiv preprint arXiv:2504.19384 (2025)"},{"issue":"13s","key":"61_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3582688","volume":"55","author":"Y Song","year":"2023","unstructured":"Song, Y., Wang, T., Cai, P., Mondal, S.K., Sahoo, J.P.: A comprehensive survey of few-shot learning: evolution, applications, challenges, and opportunities. ACM Comput. Surv. 55(13s), 1\u201340 (2023)","journal-title":"ACM Comput. Surv."},{"key":"61_CR22","doi-asserted-by":"publisher","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\u201316 (2024). https:\/\/doi.org\/10.1177\/16094069241231168","DOI":"10.1177\/16094069241231168"},{"key":"61_CR23","doi-asserted-by":"publisher","unstructured":"Yan, L., et al.: Human-AI collaboration in thematic analysis using ChatGPT: a user study and design recommendations. In: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, ACM (2024). https:\/\/doi.org\/10.1145\/3613905.3650732","DOI":"10.1145\/3613905.3650732"}],"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_61","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T07:09:32Z","timestamp":1782544172000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-29760-0_61"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,28]]},"ISBN":["9783032297594","9783032297600"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-29760-0_61","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"}}]}}