{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:35:25Z","timestamp":1757619325118,"version":"3.44.0"},"publisher-location":"Cham","reference-count":32,"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_18","type":"book-chapter","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T06:43:20Z","timestamp":1753253000000},"page":"143-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Knowledge Profiles and\u00a0Generative AI for\u00a0Realistic Student Response Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2985-5336","authenticated-orcid":false,"given":"Eyl\u00fcl","family":"Ip\u00e7i","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1343-0627","authenticated-orcid":false,"given":"Tanya","family":"Nazaretsky","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0672-0415","authenticated-orcid":false,"given":"Tanja","family":"K\u00e4ser","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"issue":"2","key":"18_CR1","doi-asserted-by":"publisher","first-page":"101","DOI":"10.3233\/IRG-2006-16(2)02","volume":"16","author":"V Aleven","year":"2006","unstructured":"Aleven, V., Mclaren, B., Roll, I., Koedinger, K.: Toward meta-cognitive tutoring: a model of help seeking with a cognitive tutor. Int. J. Artif. Intell. Educ. 16(2), 101\u2013128 (2006)","journal-title":"Int. J. Artif. Intell. Educ."},{"issue":"3","key":"18_CR2","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1187\/cbe.06-06-0168","volume":"5","author":"D Allen","year":"2006","unstructured":"Allen, D., Tanner, K.: Rubrics: tools for making learning goals and evaluation criteria explicit for both teachers and learners. CBE-Life Sci. Educ. 5(3), 197\u2013203 (2006)","journal-title":"CBE-Life Sci. Educ."},{"issue":"1","key":"18_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40593-021-00283-x","volume":"33","author":"M Ariely","year":"2023","unstructured":"Ariely, M., Nazaretsky, T., Alexandron, G.: Machine learning and Hebrew NLP for automated assessment of open-ended questions in biology. Int. J. Artif. Intell. Educ. 33(1), 1\u201334 (2023)","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Ariely, M., Nazaretsky, T., Alexandron, G.: Causal-mechanical explanations in biology: applying automated assessment for personalized learning in the science classroom. J. Res. Sci. Teach. (2024)","DOI":"10.1002\/tea.21929"},{"key":"18_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.caeai.2023.100143","volume":"4","author":"A Bewersdorff","year":"2023","unstructured":"Bewersdorff, A., Zhai, X., Roberts, J., Nerdel, C.: Myths, mis- and preconceptions of artificial intelligence: a review of the literature. Comput. Educ. Artif. Intell. 4, 100143 (2023)","journal-title":"Comput. Educ. Artif. Intell."},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Boud, D., Molloy, E.: Rethinking models of feedback for learning: the challenge of design. Assess. Eval. High. Educ. 38, 698\u2013712 (2013)","DOI":"10.1080\/02602938.2012.691462"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Chen, H., Saha, A., Hoi, S., Joty, S.: Personalised distillation: Empowering open-sourced LLMs with adaptive learning for code generation. arXiv preprint arXiv:2310.18628 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.417"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Cochran, K., Cohn, C., Hutchins, N., Biswas, G., Hastings, P.: Improving automated evaluation of formative assessments with text data augmentation. In: International Conference on Artificial Intelligence in Education, pp. 390\u2013401 (2022)","DOI":"10.1007\/978-3-031-11644-5_32"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Cochran, K., Cohn, C., Rouet, J.F., Hastings, P.: Improving automated evaluation of student text responses using GPT-3.5 for text data augmentation. In: International Conference on Artificial Intelligence in Education, pp. 217\u2013228 (2023)","DOI":"10.1007\/978-3-031-36272-9_18"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Dai, W., et al.: Can large language models provide feedback to students? A case study on ChatGPT. In: 2023 IEEE International Conference on Advanced Learning Technologies (ICALT), pp. 323\u2013325. IEEE (2023)","DOI":"10.1109\/ICALT58122.2023.00100"},{"key":"18_CR11","unstructured":"Fang, L., Lee, G.G., Zhai, X.: Using GPT-4 to augment unbalanced data for automatic scoring. arXiv preprint arXiv:2310.18365 (2023)"},{"issue":"1","key":"18_CR12","first-page":"7","volume":"24","author":"LN Jescovitch","year":"2019","unstructured":"Jescovitch, L.N., et al.: Deconstruction of holistic rubrics into analytic rubrics for large-scale assessments of students\u2019 reasoning of complex science concepts. Pract. Assess. Res. Eval. 24(1), 7 (2019)","journal-title":"Pract. Assess. Res. Eval."},{"issue":"2","key":"18_CR13","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1007\/s10956-020-09858-0","volume":"30","author":"LN Jescovitch","year":"2021","unstructured":"Jescovitch, L.N., et al.: Comparison of machine learning performance using analytic and holistic coding approaches across constructed response assessments aligned to a science learning progression. J. Sci. Educ. Technol. 30(2), 150\u2013167 (2021)","journal-title":"J. Sci. Educ. Technol."},{"issue":"2","key":"18_CR14","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.edurev.2007.05.002","volume":"2","author":"A Jonsson","year":"2007","unstructured":"Jonsson, A., Svingby, G.: The use of scoring rubrics: reliability, validity and educational consequences. Educ. Res. Rev. 2(2), 130\u2013144 (2007)","journal-title":"Educ. Res. Rev."},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Kasneci, E., et al.: ChatGPT for good? On opportunities and challenges of large language models for education. Learn. Individ. Differ. 103, 102274 (2023)","DOI":"10.1016\/j.lindif.2023.102274"},{"issue":"2","key":"18_CR16","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevPhysEducRes.19.020150","volume":"19","author":"F Kieser","year":"2023","unstructured":"Kieser, F., Wulff, P., Kuhn, J., K\u00fcchemann, S.: Educational data augmentation in physics education research using ChatGPT. Phys. Rev. Phys. Educ. Res. 19(2), 020150 (2023)","journal-title":"Phys. Rev. Phys. Educ. Res."},{"key":"18_CR17","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.aiopen.2022.03.001","volume":"3","author":"B Li","year":"2022","unstructured":"Li, B., Hou, Y., Che, W.: Data augmentation approaches in natural language processing: a survey. Ai Open 3, 71\u201390 (2022)","journal-title":"Ai Open"},{"issue":"2","key":"18_CR18","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1111\/emip.12028","volume":"33","author":"OL Liu","year":"2014","unstructured":"Liu, O.L., Brew, C., Blackmore, J., Gerard, L., Madhok, J., Linn, M.C.: Automated scoring of constructed-response science items: prospects and obstacles. Educ. Meas. Issues Pract. 33(2), 19\u201328 (2014)","journal-title":"Educ. Meas. Issues Pract."},{"issue":"1","key":"18_CR19","first-page":"10","volume":"7","author":"BM Moskal","year":"2019","unstructured":"Moskal, B.M., Leydens, J.A.: Scoring rubric development: validity and reliability. Pract. Assess. Res. Eval. 7(1), 10 (2019)","journal-title":"Pract. Assess. Res. Eval."},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Nazaretsky, T., Yolcu, H., Ariely, M., Alexandron, G.: Towards automated assessment of scientific explanations in Turkish using language transfer. In: Proceedings of the 16th International Conference on Educational Data Mining, pp. 453\u2013457 (2023)","DOI":"10.31219\/osf.io\/wuzy9"},{"key":"18_CR21","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s10956-011-9300-9","volume":"21","author":"RH Nehm","year":"2012","unstructured":"Nehm, R.H., Ha, M., Mayfield, E.: Transforming biology assessment with machine learning: automated scoring of written evolutionary explanations. J. Sci. Educ. Technol. 21, 183\u2013196 (2012)","journal-title":"J. Sci. Educ. Technol."},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Pankiewicz, M., Baker, R.S.: Large language models (GPT) for automating feedback on programming assignments. arXiv preprint arXiv:2307.00150 (2023)","DOI":"10.58459\/icce.2023.950"},{"key":"18_CR23","first-page":"72","volume":"55","author":"WJ Popham","year":"1997","unstructured":"Popham, W.J.: What\u2019s wrong-and what\u2019s right-with rubrics. Educ. Leadersh. 55, 72\u201375 (1997)","journal-title":"Educ. Leadersh."},{"issue":"4","key":"18_CR24","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/s40593-017-0143-2","volume":"27","author":"Z Rahimi","year":"2017","unstructured":"Rahimi, Z., Litman, D., Correnti, R., Wang, E., Matsumura, L.C.: Assessing students\u2019 use of evidence and organization in response-to-text writing: using natural language processing for rubric-based automated scoring. Int. J. Artif. Intell. Educ. 27(4), 694\u2013728 (2017)","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Shute, V.J.: Focus on formative feedback. Rev. Educ. Res. 78, 153\u2013189 (2008)","DOI":"10.3102\/0034654307313795"},{"key":"18_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.learninstruc.2024.101894","volume":"91","author":"J Steiss","year":"2024","unstructured":"Steiss, J., et al.: Comparing the quality of human and ChatGPT feedback of students\u2019 writing. Learn. Instr. 91, 101894 (2024)","journal-title":"Learn. Instr."},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"Uhl, J.D., Sripathi, K.N., Meir, E., Merrill, J., Urban-Lurain, M., Haudek, K.C.: Automated writing assessments measure undergraduate learning after completion of a computer-based cellular respiration tutorial. CBE\u2013Life Sci. Educ. 20(3), ar33 (2021)","DOI":"10.1187\/cbe.20-06-0122"},{"issue":"1","key":"18_CR28","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1002\/tea.21864","volume":"61","author":"CD Wilson","year":"2024","unstructured":"Wilson, C.D., et al.: Using automated analysis to assess middle school students\u2019 competence with scientific argumentation. J. Res. Sci. Teach. 61(1), 38\u201369 (2024)","journal-title":"J. Res. Sci. Teach."},{"issue":"1","key":"18_CR29","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1111\/bjet.13370","volume":"55","author":"L Yan","year":"2024","unstructured":"Yan, L., et al.: Practical and ethical challenges of large language models in education: a systematic scoping review. Br. J. Edu. Technol. 55(1), 90\u2013112 (2024)","journal-title":"Br. J. Edu. Technol."},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Yune, S.J., Lee, S.Y., Im, S.J., Kam, B.S., Baek, S.Y.: Holistic rubric vs. analytic rubric for measuring clinical performance levels in medical students. BMC Med. Educ. 18, 1\u20136 (2018)","DOI":"10.1186\/s12909-018-1228-9"},{"key":"18_CR31","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s10956-020-09875-z","volume":"30","author":"X Zhai","year":"2021","unstructured":"Zhai, X., Shi, L., Nehm, R.H.: A meta-analysis of machine learning-based science assessments: factors impacting machine-human score agreements. J. Sci. Educ. Technol. 30, 361\u2013379 (2021)","journal-title":"J. Sci. Educ. Technol."},{"key":"18_CR32","unstructured":"Neshaei, S.P., Davis, R.L., Mejia-Domenzain, P., Nazaretsky, T., K\u00e4ser, T.: Bridging the data gap: using LLMs to augment datasets for text classification. In: Proceedings of the 18th International Conference on Educational Data Mining (2025)"}],"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_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T19:04:07Z","timestamp":1757271847000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-99264-3_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031992636","9783031992643"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-99264-3_18","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"}}]}}