{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T02:49:49Z","timestamp":1786416589382,"version":"3.56.0"},"publisher-location":"Cham","reference-count":57,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031643149","type":"print"},{"value":"9783031643156","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-64315-6_3","type":"book-chapter","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T18:03:50Z","timestamp":1719857030000},"page":"32-43","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["Enhancing LLM-Based Feedback: Insights from\u00a0Intelligent Tutoring Systems and\u00a0the\u00a0Learning Sciences"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2291-1468","authenticated-orcid":false,"given":"John","family":"Stamper","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6461-7611","authenticated-orcid":false,"given":"Ruiwei","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1182-5839","authenticated-orcid":false,"given":"Xinying","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"issue":"2","key":"3_CR1","first-page":"101","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."},{"key":"3_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/11774303_7","volume-title":"Intelligent Tutoring Systems","author":"V Aleven","year":"2006","unstructured":"Aleven, V., McLaren, B.M., Sewall, J., Koedinger, K.R.: The cognitive tutor authoring tools (CTAT): preliminary evaluation of efficiency gains. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 61\u201370. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11774303_7"},{"issue":"2","key":"3_CR3","first-page":"105","volume":"19","author":"V Aleven","year":"2009","unstructured":"Aleven, V., Mclaren, B.M., Sewall, J., Koedinger, K.R.: A new paradigm for intelligent tutoring systems: example-tracing tutors. Int. J. Artif. Intell. Educ. 19(2), 105\u2013154 (2009)","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"3_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1007\/978-3-030-52240-7_2","volume-title":"Artificial Intelligence in Education","author":"S Alhazmi","year":"2020","unstructured":"Alhazmi, S., Thevathayan, C., Hamilton, M.: Interactive pedagogical agents for learning sequence diagrams. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Mill\u00e1n, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 10\u201314. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-52240-7_2"},{"issue":"4","key":"3_CR5","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1037\/0003-066X.51.4.355","volume":"51","author":"JR Anderson","year":"1996","unstructured":"Anderson, J.R.: Act: a simple theory of complex cognition. Am. Psychol. 51(4), 355 (1996)","journal-title":"Am. Psychol."},{"issue":"1","key":"3_CR6","doi-asserted-by":"publisher","first-page":"52","DOI":"10.61969\/jai.1337500","volume":"7","author":"D Baidoo-Anu","year":"2023","unstructured":"Baidoo-Anu, D., Ansah, L.O.: Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. J. AI 7(1), 52\u201362 (2023)","journal-title":"J. AI"},{"key":"3_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1007\/11774303_17","volume-title":"Intelligent Tutoring Systems","author":"H Cen","year":"2006","unstructured":"Cen, H., Koedinger, K., Junker, B.: Learning factors analysis \u2013 a general method for cognitive model evaluation and improvement. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 164\u2013175. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11774303_17"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Hallucination detection: robustly discerning reliable answers in large language models. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 245\u2013255 (2023)","DOI":"10.1145\/3583780.3614905"},{"issue":"11","key":"3_CR9","doi-asserted-by":"publisher","first-page":"4715","DOI":"10.1016\/j.eswa.2013.02.007","volume":"40","author":"K Chrysafiadi","year":"2013","unstructured":"Chrysafiadi, K., Virvou, M.: Student modeling approaches: a literature review for the last decade. Expert Syst. Appl. 40(11), 4715\u20134729 (2013)","journal-title":"Expert Syst. Appl."},{"key":"3_CR10","unstructured":"Clark, R.C., Mayer, R.E.: E-learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning. Wiley, Hoboken (2023)"},{"key":"3_CR11","series-title":"International Centre for Mechanical Sciences","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-3-7091-2670-7_24","volume-title":"User Modeling","author":"C Conati","year":"1997","unstructured":"Conati, C., Gertner, A.S., VanLehn, K., Druzdzel, M.J.: On-line student modeling for coached problem solving using Bayesian networks. In: Jameson, A., Paris, C., Tasso, C. (eds.) User Modeling. ICMS, vol. 383, pp. 231\u2013242. Springer, Vienna (1997). https:\/\/doi.org\/10.1007\/978-3-7091-2670-7_24"},{"key":"3_CR12","unstructured":"Di\u00a0Eugenio, B., Fossati, D., Yu, D., Haller, S.M., Glass, M.: Natural language generation for intelligent tutoring systems: a case study. In: AIED, pp. 217\u2013224 (2005)"},{"issue":"1","key":"3_CR13","first-page":"5","volume":"10","author":"D Fossati","year":"2015","unstructured":"Fossati, D., Di Eugenio, B., Ohlsson, S., Brown, C., Chen, L.: Data driven automatic feedback generation in the ilist intelligent tutoring system. Technol. Instr. Cogn. Learn. 10(1), 5\u201326 (2015)","journal-title":"Technol. Instr. Cogn. Learn."},{"key":"3_CR14","unstructured":"Fournier-Viger, P., Nkambou, R., Nguifo, E.M.: Learning procedural knowledge from user solutions to ill-defined tasks in a simulated robotic manipulator. In: Romero, et al. (eds.) Handbook of Educational Data Mining, pp. 451\u2013465 (2010)"},{"key":"3_CR15","first-page":"417","volume":"158","author":"RG Hausmann","year":"2007","unstructured":"Hausmann, R.G., VanLehn, K.: Explaining self-explaining: a contrast between content and generation. Front. Artif. Intell. Appl. 158, 417 (2007)","journal-title":"Front. Artif. Intell. Appl."},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Hellas, A., Leinonen, J., Sarsa, S., Koutcheme, C., Kujanp\u00e4\u00e4, L., Sorva, J.: Exploring the responses of large language models to beginner programmers\u2019 help requests. In: Proceedings of the 2023 ACM Conference on International Computing Education Research-Volume 1, pp. 93\u2013105 (2023)","DOI":"10.1145\/3568813.3600139"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Hou, X., Ericson, B.J., Wang, X.: Using adaptive parsons problems to scaffold write-code problems. In: Proceedings of the 2022 ACM Conference on International Computing Education Research-Volume 1, pp. 15\u201326 (2022)","DOI":"10.1145\/3501385.3543977"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Hou, X., Ericson, B.J., Wang, X.: Understanding the effects of using parsons problems to scaffold code writing for students with varying CS self-efficacy levels. In: Proceedings of the 23rd Koli Calling International Conference on Computing Education Research, pp. 1\u201312 (2023)","DOI":"10.1145\/3631802.3631832"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Kazemitabaar, M., Hou, X., Henley, A., Ericson, B.J., Weintrop, D., Grossman, T.: How novices use LLM-based code generators to solve cs1 coding tasks in a self-paced learning environment. In: Proceedings of the 23rd Koli Calling International Conference on Computing Education Research, pp. 1\u201312 (2023)","DOI":"10.1145\/3631802.3631806"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Kazemitabaar, M., et al.: Codeaid: evaluating a classroom deployment of an llm-based programming assistant that balances student and educator needs. arXiv preprint arXiv:2401.11314 (2024)","DOI":"10.1145\/3613904.3642773"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Koedinger, K., Brunskill, E., Baker, R., Mclaughlin, E., Stamper, J.C.: New potentials for data-driven intelligent tutoring system development and optimization. AI Mag. 34, 27\u201341 (2013). https:\/\/api.semanticscholar.org\/CorpusID:13189100","DOI":"10.1609\/aimag.v34i3.2484"},{"issue":"5","key":"3_CR22","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1111\/j.1551-6709.2012.01245.x","volume":"36","author":"KR Koedinger","year":"2012","unstructured":"Koedinger, K.R., Corbett, A.T., Perfetti, C.: The knowledge-learning-instruction framework: bridging the science-practice chasm to enhance robust student learning. Cogn. Sci. 36(5), 757\u2013798 (2012)","journal-title":"Cogn. Sci."},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Krathwohl, D.R.: A revision of bloom\u2019s taxonomy: an overview. Theory Pract. 41(4), 212\u2013218 (2002)","DOI":"10.1207\/s15430421tip4104_2"},{"key":"3_CR24","unstructured":"Kumar, H., Musabirov, I., Williams, J.J., Liut, M.: Quickta: exploring the design space of using large language models to provide support to students. In: Learning Analytics and Knowledge Conference. Learning Analytics and Knowledge Conference 2023 (LAK\u201923). ACM, Arlington, Texas (2023)"},{"key":"3_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/978-3-031-36272-9_2","volume-title":"Artificial Intelligence in Education - AIED 2023","author":"J Lee","year":"2023","unstructured":"Lee, J., Lan, A.: Smartphone: exploring keyword mnemonic with auto-generated verbal and visual cues. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds.) AIED 2023. LNCS, vol. 13916, pp. 16\u201327. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-36272-9_2"},{"issue":"4","key":"3_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.acalib.2023.102720","volume":"49","author":"LS Lo","year":"2023","unstructured":"Lo, L.S.: The clear path: a framework for enhancing information literacy through prompt engineering. J. Acad. Librariansh. 49(4), 102720 (2023)","journal-title":"J. Acad. Librariansh."},{"key":"3_CR27","unstructured":"Lu, C., Cutumisu, M.: Integrating deep learning into an automated feedback generation system for automated essay scoring. International Educational Data Mining Society (2021)"},{"issue":"1","key":"3_CR28","doi-asserted-by":"publisher","first-page":"1331533","DOI":"10.1080\/2331186X.2017.1331533","volume":"4","author":"SA Malik","year":"2017","unstructured":"Malik, S.A.: Revisiting and re-representing scaffolding: the two gradient model. Cogent Educ. 4(1), 1331533 (2017)","journal-title":"Cogent Educ."},{"key":"3_CR29","unstructured":"Martin, B., Koedinger, K.R., Mitrovic, A., Mathan, S.: On using learning curves to evaluate its. In: AIED, pp. 419\u2013426 (2005)"},{"key":"3_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2021.104366","volume":"178","author":"BM McLaren","year":"2022","unstructured":"McLaren, B.M., Richey, J.E., Nguyen, H., Hou, X.: How instructional context can impact learning with educational technology: lessons from a study with a digital learning game. Comput. Educ. 178, 104366 (2022)","journal-title":"Comput. Educ."},{"key":"3_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/978-3-031-36272-9_30","volume-title":"Artificial Intelligence in Education - AIED 2023","author":"H McNichols","year":"2023","unstructured":"McNichols, H., Zhang, M., Lan, A.: Algebra error classification with large language models. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds.) AIED 2023. LNCS, vol. 13916, pp. 365\u2013376. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-36272-9_30"},{"key":"3_CR32","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1007\/3-540-45517-5_103","volume-title":"Engineering of Intelligent Systems","author":"A Mitrovic","year":"2001","unstructured":"Mitrovic, A., Mayo, M., Suraweera, P., Martin, B.: Constraint-based tutors: a success story. In: Monostori, L., V\u00e1ncza, J., Ali, M. (eds.) IEA\/AIE 2001. LNCS (LNAI), vol. 2070, pp. 931\u2013940. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-45517-5_103"},{"issue":"1","key":"3_CR33","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/j.compedu.2012.07.002","volume":"60","author":"A Mitrovic","year":"2013","unstructured":"Mitrovic, A., Ohlsson, S., Barrow, D.K.: The effect of positive feedback in a constraint-based intelligent tutoring system. Comput. Educ. 60(1), 264\u2013272 (2013)","journal-title":"Comput. Educ."},{"key":"3_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1007\/978-3-031-42682-7_19","volume-title":"Responsive and Sustainable Educational Futures - EC-TEL 2023","author":"HA Nguyen","year":"2023","unstructured":"Nguyen, H.A., Stec, H., Hou, X., Di, S., McLaren, B.M.: Evaluating ChatGPT\u2019s decimal skills and feedback generation in a digital learning game. In: Viberg, O., Jivet, I., Mu\u00f1oz-Merino, P., Perifanou, M., Papathoma, T. (eds.) EC-TEL 2023. LNCS, vol. 14200, pp. 278\u2013293. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-42682-7_19"},{"issue":"2","key":"3_CR35","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1037\/0033-295X.103.2.241","volume":"103","author":"S Ohlsson","year":"1996","unstructured":"Ohlsson, S.: Learning from performance errors. Psychol. Rev. 103(2), 241 (1996)","journal-title":"Psychol. Rev."},{"key":"3_CR36","unstructured":"OpenAI: Dall.e 2 (2023). https:\/\/openai.com\/dall-e-2\/. Accessed 12 Mar 2024"},{"key":"3_CR37","unstructured":"OpenAI: Sora (2024). https:\/\/openai.com. Accessed 12 Mar 2024"},{"key":"3_CR38","doi-asserted-by":"crossref","unstructured":"Pankiewicz, M., Baker, R.S.: Navigating compiler errors with AI assistance\u2013a study of GPT hints in an introductory programming course. arXiv preprint arXiv:2403.12737 (2024)","DOI":"10.1145\/3649217.3653608"},{"key":"3_CR39","unstructured":"Phung, T., et al.: Generating high-precision feedback for programming syntax errors using large language models. arXiv preprint arXiv:2302.04662 (2023)"},{"key":"3_CR40","doi-asserted-by":"crossref","unstructured":"Phung, T., et al.: Automating human tutor-style programming feedback: leveraging GPT-4 tutor model for hint generation and GPT-3.5 student model for hint validation. In: Proceedings of the 14th Learning Analytics and Knowledge Conference, pp. 12\u201323 (2024)","DOI":"10.1145\/3636555.3636846"},{"key":"3_CR41","unstructured":"Price, T.W., Dong, Y., Barnes, T.: Generating data-driven hints for open-ended programming. International Educational Data Mining Society (2016)"},{"key":"3_CR42","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/978-3-319-61425-0_26","volume-title":"Artificial Intelligence in Education","author":"TW Price","year":"2017","unstructured":"Price, T.W., Zhi, R., Barnes, T.: Hint generation under uncertainty: the effect of hint quality on help-seeking behavior. In: Andr\u00e9, E., Baker, R., Hu, X., Rodrigo, M.M.T., du Boulay, B. (eds.) AIED 2017. LNCS (LNAI), vol. 10331, pp. 311\u2013322. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-61425-0_26"},{"key":"3_CR43","unstructured":"Qi, J.Z.P.L., Hartmann, B., Norouzi, J.D.N.: Conversational programming with LLM-powered interactive support in an introductory computer science course. In: NeurIPS\u201923 Workshop on Generative AI for Education (GAIED) (2023)"},{"key":"3_CR44","unstructured":"Rau, M.A., Aleven, V., Rummel, N.: Intelligent tutoring systems with multiple representations and self-explanation prompts support learning of fractions. In: AIED, pp. 441\u2013448 (2009)"},{"key":"3_CR45","doi-asserted-by":"crossref","unstructured":"Rivers, K., Harpstead, E., Koedinger, K.R.: Learning curve analysis for programming: which concepts do students struggle with? In: ICER, vol.\u00a016, pp. 143\u2013151. ACM (2016)","DOI":"10.1145\/2960310.2960333"},{"key":"3_CR46","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s40593-015-0070-z","volume":"27","author":"K Rivers","year":"2017","unstructured":"Rivers, K., Koedinger, K.R.: Data-driven hint generation in vast solution spaces: a self-improving python programming tutor. Int. J. Artif. Intell. Educ. 27, 37\u201364 (2017)","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"3_CR47","doi-asserted-by":"crossref","unstructured":"Roest, L., Keuning, H., Jeuring, J.: Next-step hint generation for introductory programming using large language models. In: Proceedings of the 26th Australasian Computing Education Conference, pp. 144\u2013153 (2024)","DOI":"10.1145\/3636243.3636259"},{"key":"3_CR48","unstructured":"Schmucker, R., Xia, M., Azaria, A., Mitchell, T.: Ruffle &Riley: towards the automated induction of conversational tutoring systems. arXiv preprint arXiv:2310.01420 (2023)"},{"key":"3_CR49","unstructured":"Schwonke, R., Wittwer, J., Aleven, V., Salden, R., Krieg, C., Renkl, A.: Can tutored problem solving benefit from faded worked-out examples. In: European Cognitive Science Conference, pp. 23\u201327 (2007)"},{"key":"3_CR50","unstructured":"Stamper, J.: Automating the generation of production rules for intelligent tutoring systems. In: Proceedings of 9th International Conference on Interactive Computer Aided Learning (2006)"},{"key":"3_CR51","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/978-3-642-13437-1_4","volume-title":"Intelligent Tutoring Systems","author":"J Stamper","year":"2010","unstructured":"Stamper, J., Barnes, T., Croy, M.: Enhancing the automatic generation of hints with expert seeding. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6095, pp. 31\u201340. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-13437-1_4"},{"key":"3_CR52","unstructured":"Stamper, J., Barnes, T., Lehmann, L., Croy, M.: The hint factory: automatic generation of contextualized help for existing computer aided instruction. In: Proceedings of the 9th International Conference on Intelligent Tutoring Systems Young Researchers Track, pp. 71\u201378 (2008)"},{"key":"3_CR53","unstructured":"Tack, A., Piech, C.: The AI teacher test: measuring the pedagogical ability of blender and GPT-3 in educational dialogues. arXiv preprint arXiv:2205.07540 (2022)"},{"key":"3_CR54","unstructured":"VanLehn, K.: Student modeling. In: Foundations of Intelligent Tutoring Systems, pp. 55\u201378 (2013)"},{"key":"3_CR55","unstructured":"VanLehn, K., et al.: The Andes physics tutoring system: five years of evaluations. In: AIED, pp. 678\u2013685 (2005)"},{"key":"3_CR56","unstructured":"Wei, Y., Carvalho, P.F., Stamper, J.: Uncovering name-based biases in large language models through simulated trust game. arXiv preprint arXiv:2404.14682 (2024)"},{"key":"3_CR57","doi-asserted-by":"crossref","unstructured":"Xiao, R., Hou, X., Stamper, J.: Exploring how multiple levels of GPT-generated programming hints support or disappoint novices. arXiv preprint arXiv:2404.02213 (2024)","DOI":"10.1145\/3613905.3650937"}],"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 and Blue Sky"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-64315-6_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T06:19:57Z","timestamp":1732342797000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-64315-6_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031643149","9783031643156"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-64315-6_3","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2 July 2024","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":"Recife","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aied2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aied2024.cesar.school\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}