{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T12:52:08Z","timestamp":1782478328581,"version":"3.54.5"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032297433","type":"print"},{"value":"9783032297440","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"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\/978-3-032-29744-0_8","type":"book-chapter","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T12:43:48Z","timestamp":1782477828000},"page":"110-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Can MLLMs Read Students\u2019 Minds? Unpacking Multimodal Error Analysis in\u00a0Handwritten Math"],"prefix":"10.1007","author":[{"given":"Dingjie","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianlong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Fan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiling","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lichao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingsong","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"key":"8_CR1","unstructured":"Abouelenin, A., et al.: Phi-4-Mini technical report: compact yet powerful multimodal language models via mixture-of-LoRas (2025). arXiv:2503.01743 arXiv preprint"},{"key":"8_CR2","unstructured":"Bai, J., et al.: Qwen-VL: a versatile vision-language model for understanding, localization, text reading, and beyond. arXiv preprint arXiv:2308.12966 (2023)"},{"key":"8_CR3","unstructured":"Bai, S., et\u00a0al.: Qwen2. 5-VL technical report. arXiv preprint arXiv:2502.13923 (2025)"},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Botelho, A.F., Baral, S., Erickson, J.A., Benachamardi, P., Heffernan, N.T.: Leveraging natural language processing to support automated assessment and feedback for student open responses in mathematics. J. Comput. Assist. Learn. (2023)","DOI":"10.1111\/jcal.12793"},{"key":"8_CR5","unstructured":"Caraeni, A., Scarlatos, A., Lan, A.: Evaluating GPT-4 at grading handwritten solutions in math exams (2024). arXiv preprint arXiv:2411.05231"},{"key":"8_CR6","unstructured":"Chen, S., Li, B., Niu, D.: Boosting of thoughts: trial-and-error problem solving with large language models (2024). arXiv preprint arXiv:2402.11140"},{"key":"8_CR7","unstructured":"Chen, Z., et\u00a0al.: Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling. arXiv preprint arXiv:2412.05271 (2024)"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Davalos, E., et al.: LLMs as educational analysts: transforming multimodal data traces into actionable reading assessment reports. arXiv preprint arXiv:2503.02099 (2025)","DOI":"10.1007\/978-3-031-98417-4_14"},{"key":"8_CR9","unstructured":"Grattafiori, A., et al.: The llama 3 herd of models (2024). arXiv preprint arXiv:2407.21783"},{"key":"8_CR10","unstructured":"Hurst, A., et al.: GPT-4o system card (2024). arXiv preprint arXiv:2410.21276"},{"key":"8_CR11","unstructured":"Jiang, B.: LLMs can find mathematical reasoning mistakes by pedagogical chain-of-thought. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) (2024)"},{"key":"8_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.caeai.2024.100349","volume":"8","author":"A Kinder","year":"2025","unstructured":"Kinder, A., et al.: Effects of adaptive feedback generated by a large language model: a case study in teacher education. Comput. Educ. Artif. Intell. 8, 100349 (2025)","journal-title":"Comput. Educ. Artif. Intell."},{"key":"8_CR13","unstructured":"Lee, J., Baraniuk, R., Lan, A.S.: Training LLM-based tutors to improve student learning outcomes in dialogues, (2025). arXiv preprint arXiv:2503.06424"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning (2023). arXiv preprint arXiv:2304.08485","DOI":"10.52202\/075280-1516"},{"key":"8_CR15","unstructured":"Liu, T., Chatain, J., Ruzika, S., Kuhn, J., K\u00fcchemann, S.: AI-assisted automated short answer grading of handwritten university level mathematics exams (2024). arXiv preprint arXiv:2408.11728"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Mahdavi, M., Zanibbi, R., Mouch\u00e8re, H., Viard-Gaudin, C., Garain, U.: ICDAR 2019 CROHME + TFD: Competition on recognition of handwritten mathematical expressions and typeset formula detection. In: Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), pp. 1533\u20131538 (2019)","DOI":"10.1109\/ICDAR.2019.00247"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"McNichols, H., Zhang, M., Lan, A.: Algebra error classification with large language models. In: Proceedings of the 24th International Conference on Artificial Intelligence in Education (AIED) (2023)","DOI":"10.1007\/978-3-031-36272-9_30"},{"key":"8_CR18","unstructured":"Peng, Y., et al.: Skywork R1V: pioneering multimodal reasoning with chain-of-thought (2025). arXiv preprint arXiv:2504.05599"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: Math-LLaVA: bootstrapping mathematical reasoning for multimodal large language models. In: Findings of EMNLP (2024)","DOI":"10.18653\/v1\/2024.findings-emnlp.268"},{"key":"8_CR20","unstructured":"Song, D., Chen, S., Chen, G.H., Yu, F., Wan, X., Wang, B.: MileBench: benchmarking MLLMs in long context. In: COLM (2024)"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Song, D., Lai, S., Chen, S., Sun, L., Wang, B.: Both text and images leaked! a systematic analysis of data contamination in multimodal LLMs. In: Findings of EMNLP (2025)","DOI":"10.18653\/v1\/2025.findings-emnlp.556"},{"key":"8_CR22","unstructured":"Team, G., et al.: Gemini: a family of highly capable multimodal models (2023). arXiv preprint arXiv:2312.11805"},{"key":"8_CR23","unstructured":"Team, G., et al.: Gemma 3 technical report (2025). arXiv preprint arXiv:2503.19786"},{"key":"8_CR24","unstructured":"Team, Q.: QVQ: to see the world with wisdom (2024). https:\/\/qwenlm.github.io\/blog\/qvq-72b-preview\/"},{"key":"8_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110531","volume":"153","author":"TN Truong","year":"2024","unstructured":"Truong, T.N., Nguyen, C.T., Zanibbi, R., Mouch\u00e8re, H., Nakagawa, M.: A survey on handwritten mathematical expression recognition: the rise of encoder-decoder and GNN models. Pattern Recogn. 153, 110531 (2024)","journal-title":"Pattern Recogn."},{"key":"8_CR26","doi-asserted-by":"crossref","unstructured":"Wang, K., et al.: Measuring multimodal mathematical reasoning with the math-vision dataset. In: NeurIPS Datasets and Benchmarks (2024)","DOI":"10.52202\/079017-3014"},{"issue":"6","key":"8_CR27","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/MSP.2025.3594309","volume":"42","author":"S Wang","year":"2026","unstructured":"Wang, S., et al.: Large language models for education: a survey and outlook. IEEE Signal Process. Mag. 42(6), 51\u201363 (2026)","journal-title":"IEEE Signal Process. Mag."},{"key":"8_CR28","unstructured":"Wu, Z., et\u00a0al.: DeepSeek-VL2: mixture-of-experts vision-language models for advanced multimodal understanding. arXiv preprint arXiv:2412.10302 (2024)"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Yan, Y., et al.: A survey of mathematical reasoning in the era of multimodal large language model: benchmark, method & challenges. In: Findings of the Association for Computational Linguistics: ACL 2025, pp. 11798\u201311827 (2025)","DOI":"10.18653\/v1\/2025.findings-acl.614"},{"key":"8_CR30","unstructured":"Yan, Y., et\u00a0al.: ErrorRadar: benchmarking complex mathematical reasoning of multimodal large language models via error detection. arXiv preprint arXiv:2410.04509 (2024)"},{"key":"8_CR31","doi-asserted-by":"crossref","unstructured":"Yan, Y., Wang, S., Huo, J., Yu, P.S., Hu, X., Wen, Q.: MathAgent: leveraging a mixture-of-math-agent framework for real-world multimodal mathematical error detection. In: ACL (2025)","DOI":"10.18653\/v1\/2025.acl-industry.7"},{"key":"8_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, R., et al.: MathVerse: does your multi-modal LLM truly see the diagrams in visual math problems? In: ECCV (2024)","DOI":"10.1007\/978-3-031-73242-3_10"},{"key":"8_CR33","unstructured":"Zhang, Y.F., Li, H., Song, D., Sun, L., Xu, T., Wen, Q.: From correctness to comprehension: AI agents for personalized error diagnosis in education (2025). arXiv preprint arXiv:2502.13789"},{"key":"8_CR34","unstructured":"Zhou, J.: MathScape: evaluating MLLMs in multimodal math scenarios through a hierarchical benchmark (2024). arXiv preprint arXiv:2408.07543"}],"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\/978-3-032-29744-0_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T12:44:27Z","timestamp":1782477867000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-29744-0_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,27]]},"ISBN":["9783032297433","9783032297440"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-29744-0_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,27]]},"assertion":[{"value":"27 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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"}}]}}