{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:16:29Z","timestamp":1759331789687,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031643149"},{"type":"electronic","value":"9783031643156"}],"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_26","type":"book-chapter","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T18:03:50Z","timestamp":1719857030000},"page":"304-311","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Large Language Models for\u00a0Career Readiness Prediction"],"prefix":"10.1007","author":[{"given":"Chenwei","family":"Cui","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amro","family":"Abdalla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Derry","family":"Wijaya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Scott","family":"Solberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah Adel","family":"Bargal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"26_CR1","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)"},{"key":"26_CR2","doi-asserted-by":"publisher","unstructured":"Bulathwela, S., Muse, H., Yilmaz, E.: Scalable educational question generation with pre-trained language models. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds.) Artificial Intelligence in Education, pp. 327\u2013339. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-36272-9_27","DOI":"10.1007\/978-3-031-36272-9_27"},{"key":"26_CR3","unstructured":"Chowdhery, A., et al.: Palm: scaling language modeling with pathways. J. Mach. Learn. Res. 24(240), 1\u2013113 (2023)"},{"key":"26_CR4","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: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds.) Artificial Intelligence in Education, pp. 217\u2013228. Springer, Cham (2023)","DOI":"10.1007\/978-3-031-36272-9_18"},{"key":"26_CR5","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Esbroeck, R.V., Tibos, K., Zaman, M.: A dynamic model of career choice development. Int. J. Educ. Vocat. Guid. 5, 5\u201318 (2005)","DOI":"10.1007\/s10775-005-2122-7"},{"key":"26_CR7","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/978-3-031-36272-9_7","volume-title":"Artificial Intelligence in Education","author":"H Funayama","year":"2023","unstructured":"Funayama, H., Asazuma, Y., Matsubayashi, Y., Mizumoto, T., Inui, K.: Reducing the cost: cross-prompt pre-finetuning for short answer scoring. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds.) Artificial Intelligence in Education, pp. 78\u201389. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-36272-9_7"},{"key":"26_CR8","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.jvb.2016.08.001","volume":"97","author":"J Guichard","year":"2016","unstructured":"Guichard, J.: Reflexivity in life design interventions: comments on life and career design dialogues. J. Vocat. Behav. 97, 78\u201383 (2016)","journal-title":"J. Vocat. Behav."},{"key":"26_CR9","unstructured":"Lin, X.V., et\u00a0al.: Few-shot learning with multilingual generative language models. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 9019\u20139052 (2022)"},{"key":"26_CR10","unstructured":"Marcia, J.E.: Citation classic-development and validation of ego identity status (1984)"},{"issue":"11","key":"26_CR11","first-page":"159","volume":"9","author":"JE Marcia","year":"1980","unstructured":"Marcia, J.E., et al.: Identity in adolescence. Handb. Adolesc. Psychol. 9(11), 159\u2013187 (1980)","journal-title":"Handb. Adolesc. Psychol."},{"key":"26_CR12","unstructured":"Neelakantan, A., et\u00a0al.: Text and code embeddings by contrastive pre-training. arXiv preprint arXiv:2201.10005 (2022)"},{"key":"26_CR13","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et\u00a0al.: Improving language understanding by generative pre-training (2018)"},{"key":"26_CR14","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.jvb.2016.09.001","volume":"97","author":"ML Savickas","year":"2016","unstructured":"Savickas, M.L.: Reflection and reflexivity during life-design interventions: Comments on career construction counseling. J. Vocat. Behav. 97, 84\u201389 (2016)","journal-title":"J. Vocat. Behav."},{"key":"26_CR15","unstructured":"Solberg, V.S., Wills, J., Redmon, K., Skaff, L.: Use of individualized learning plans: a promising practice for driving college and career efforts. Findings and recommendations from a multi-method, multi-study effort. In: National Collaborative on Workforce and Disability for Youth (2014)"},{"key":"26_CR16","unstructured":"Touvron, H., et\u00a0al.: Llama: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)"},{"key":"26_CR17","unstructured":"Workshop, B., et\u00a0al.: Bloom: a 176b-parameter open-access multilingual language model. arXiv preprint arXiv:2211.05100 (2022)"},{"key":"26_CR18","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/978-3-031-36272-9_23","volume-title":"Artificial Intelligence in Education","author":"B Yang","year":"2023","unstructured":"Yang, B., Nam, S., Huang, Y.: \u201cWhy my essay received a 4?\u2019\u2019: A natural language processing based argumentative essay structure analysis. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds.) Artificial Intelligence in Education, pp. 279\u2013290. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-36272-9_23"},{"key":"26_CR19","unstructured":"Zhang, S., et\u00a0al.: Opt: open pre-trained transformer language models. arXiv preprint arxiv:2205.01068 (2022)"}],"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_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T18:07:53Z","timestamp":1719857273000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-64315-6_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031643149","9783031643156"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-64315-6_26","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"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"}}]}}