{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T20:49:36Z","timestamp":1776286176297,"version":"3.50.1"},"reference-count":60,"publisher":"Informa UK Limited","issue":"4","content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Human\u2013Computer Interaction"],"published-print":{"date-parts":[[2026,2,16]]},"DOI":"10.1080\/10447318.2025.2530100","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T11:30:51Z","timestamp":1754307051000},"page":"2831-2859","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":1,"title":["Trapped in the Prompt Loop: Reprompt Behavior in Writing with ChatGPT"],"prefix":"10.1080","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5761-6904","authenticated-orcid":false,"given":"Hyelan","family":"Jung","sequence":"first","affiliation":[{"name":"Department of Intelligence and Information, Seoul National University","place":["Seoul, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6659-5252","authenticated-orcid":false,"given":"Youngchan","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Intelligence and Information, Seoul National University","place":["Seoul, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2376-3046","authenticated-orcid":false,"given":"Joongseek","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Intelligence and Information, Seoul National University","place":["Seoul, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"301","published-online":{"date-parts":[[2025,8,4]]},"reference":[{"key":"e_1_3_2_2_1","unstructured":"Adiwardana D. Luong M.-T. So D. R. Hall J. Fiedel N. Thoppilan R. Yang Z. Kulshreshtha A. Nemade G. & Lu Y. (2020). Towards a human-like open-domain chatbot. ArXiv Preprint ArXiv:2001.09977"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.37622\/IJAER\/18.2.2023.119-123"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1101\/2023.08.17.553688"},{"key":"e_1_3_2_5_1","volume-title":"Prompt engineering guidelines for LLMs in requirements engineering","author":"Arvidsson S.","year":"2023","unstructured":"Arvidsson, S., & Axell, J. (2023). Prompt engineering guidelines for LLMs in requirements engineering."},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.21541\/apjess.1293702"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asw.2023.100745"},{"key":"e_1_3_2_8_1","doi-asserted-by":"publisher","DOI":"10.17159\/sajs.2024\/17147"},{"key":"e_1_3_2_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.caeai.2023.100172"},{"key":"e_1_3_2_10_1","doi-asserted-by":"publisher","DOI":"10.21203\/rs.3.rs-2895792\/v1"},{"key":"e_1_3_2_11_1","doi-asserted-by":"publisher","DOI":"10.1191\/1478088706qp063oa"},{"key":"e_1_3_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11528-023-00896-0"},{"key":"e_1_3_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3641289"},{"key":"e_1_3_2_14_1","unstructured":"Chang K. Xu S. Wang C. Luo Y. Xiao T. & Zhu J. (2024). Efficient prompting methods for large language models: A survey. ArXiv Preprint ArXiv:2404.01077"},{"key":"e_1_3_2_15_1","unstructured":"Chen B. Zhang Z. Langren\u00e9 N. Zhu S. (2023). Unleashing the potential of prompt engineering in Large Language Models: A comprehensive review. http:\/\/arxiv.org\/abs\/2310.14735"},{"key":"e_1_3_2_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-024-01276-1"},{"key":"e_1_3_2_17_1","doi-asserted-by":"publisher","DOI":"10.51519\/journalisi.v6i1.639"},{"key":"e_1_3_2_18_1","unstructured":"Dang H. Mecke L. Lehmann F. Goller S. & Buschek D. (2022). How to prompt? Opportunities and challenges of zero-and few-shot learning for human-AI interaction in creative applications of generative models. ArXiv Preprint ArXiv:2209.01390"},{"key":"e_1_3_2_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2023.102642"},{"key":"e_1_3_2_20_1","unstructured":"Gattupalli S. Maloy R. W. & Edwards S. A. (2023). Prompt literacy: A pivotal educational skill in the age of AI."},{"key":"e_1_3_2_21_1","unstructured":"Haque M. U. Dharmadasa I. Sworna Z. T. Rajapakse R. N. & Ahmad H. (2022). I think this is the most disruptive technology\u201d: Exploring Sentiments of ChatGPT Early Adopters using Twitter Data. 2022."},{"key":"e_1_3_2_22_1","unstructured":"Ippolito D. Yuan A. Coenen A. & Burnam S. (2022). Creative writing with an ai-powered writing assistant: Perspectives from professional writers. ArXiv Preprint ArXiv:2211.05030"},{"key":"e_1_3_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3571730"},{"key":"e_1_3_2_24_1","unstructured":"Kaddour J. Harris J. Mozes M. Bradley H. Raileanu R. & McHardy R. (2023). Challenges and applications of large language models. ArXiv Preprint ArXiv:2307.10169"},{"key":"e_1_3_2_25_1","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2023.0254"},{"key":"e_1_3_2_26_1","doi-asserted-by":"publisher","DOI":"10.1080\/10447318.2024.2351717"},{"key":"e_1_3_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3640543.3645148"},{"key":"e_1_3_2_28_1","unstructured":"Kim Y. Son K. Kim S. & Kim J. (2024c). Beyond Prompts: Learning from Human Communication for Enhanced AI Intent Alignment. ArXiv Preprint ArXiv:2405.05678"},{"key":"e_1_3_2_29_1","doi-asserted-by":"crossref","unstructured":"Lu Q. Qiu B. Ding L. Zhang K. Kocmi T. Tao D. (2023). Error analysis prompting enables human-like translation evaluation in large language models. http:\/\/arxiv.org\/abs\/2303.13809","DOI":"10.20944\/preprints202303.0255.v1"},{"key":"e_1_3_2_30_1","unstructured":"Lu Z. Mysore S. Safavi T. Neville J. Yang L. & Wan M. (2024). Corporate communication companion (CCC): An LLM-empowered writing assistant for workplace social media. ArXiv Preprint ArXiv:2405.04656"},{"key":"e_1_3_2_31_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2303.17651"},{"key":"e_1_3_2_32_1","doi-asserted-by":"publisher","DOI":"10.59668\/371.14442"},{"key":"e_1_3_2_33_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13040-023-00339-9"},{"key":"e_1_3_2_34_1","unstructured":"Minaee S. Mikolov T. Nikzad N. Chenaghlu M. Socher R. Amatriain X. & Gao J. (2024). Large language models: A survey. ArXiv Preprint ArXiv:2402.06196"},{"key":"e_1_3_2_35_1","unstructured":"Mishra A. Soni U. Arunkumar A. Huang J. Kwon B. C. & Bryan C. (2023). Promptaid: Prompt exploration perturbation testing and iteration using visual analytics for large language models. ArXiv Preprint ArXiv:2304.01964"},{"key":"e_1_3_2_36_1","unstructured":"Naveed H. Khan A. U. Qiu S. Saqib M. Anwar S. Usman M. Akhtar N. Barnes N. & Mian A. (2023). A comprehensive overview of large language models. ArXiv Preprint ArXiv:2307.06435"},{"key":"e_1_3_2_37_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00783-6"},{"key":"e_1_3_2_38_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.373"},{"key":"e_1_3_2_39_1","doi-asserted-by":"publisher","DOI":"10.1080\/10447318.2023.2269006"},{"key":"e_1_3_2_40_1","doi-asserted-by":"publisher","DOI":"10.1561\/1900000078"},{"key":"e_1_3_2_41_1","doi-asserted-by":"publisher","DOI":"10.52631\/jemds.v3i1.175"},{"key":"e_1_3_2_42_1","unstructured":"Ren Z. Zhan Y. Yu B. Ding L. Tao D. (2024). Healthcare copilot: Eliciting the power of general LLMs for medical consultation. http:\/\/arxiv.org\/abs\/2402.13408"},{"key":"e_1_3_2_43_1","doi-asserted-by":"crossref","unstructured":"Sahoo P. Singh A. K. Saha S. Jain V. Mondal S. Chadha A. (2024). A systematic survey of prompt engineering in large language models: Techniques and applications. http:\/\/arxiv.org\/abs\/2402.07927","DOI":"10.1007\/979-8-8688-0569-1_4"},{"key":"e_1_3_2_44_1","unstructured":"Schulhoff S. Ilie M. Balepur N. Kahadze K. Liu A. Si C. Li Y. Gupta A. Han H. & Schulhoff S. (2024). The prompt report: A systematic survey of prompting techniques. ArXiv Preprint ArXiv:2406.06608"},{"key":"e_1_3_2_45_1","doi-asserted-by":"publisher","DOI":"10.30935\/cedtech\/13419"},{"key":"e_1_3_2_46_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1706.03762"},{"key":"e_1_3_2_47_1","doi-asserted-by":"publisher","DOI":"10.1080\/10447318.2024.2352919"},{"key":"e_1_3_2_48_1","unstructured":"Wang S. Ding L. Zhan Y. Luo Y. He Z. Tao D. (2025). Leveraging metamemory mechanisms for enhanced data-free code generation in LLMs. http:\/\/arxiv.org\/abs\/2501.07892"},{"key":"e_1_3_2_49_1","doi-asserted-by":"publisher","DOI":"10.1080\/10447318.2024.2400398"},{"key":"e_1_3_2_50_1","doi-asserted-by":"crossref","unstructured":"Washington J. (2023). The impact of generative artificial intelligence on writer\u2019s self-efficacy: A critical literature review. Available at SSRN 4538043.","DOI":"10.2139\/ssrn.4538043"},{"key":"e_1_3_2_51_1","unstructured":"Wei J. Tay Y. Bommasani R. Raffel C. Zoph B. Borgeaud S. Yogatama D. Bosma M. Zhou D. & Metzler D. (2022). Emergent abilities of large language models. ArXiv Preprint ArXiv:2206.07682"},{"key":"e_1_3_2_52_1","unstructured":"White J. Fu Q. Hays S. Sandborn M. Olea C. Gilbert H. Elnashar A. Spencer-Smith J. & Schmidt D. C. (2023). A prompt pattern catalog to enhance prompt engineering with chatgpt. ArXiv Preprint ArXiv:2302.11382"},{"key":"e_1_3_2_53_1","doi-asserted-by":"crossref","unstructured":"Xu Z. Peng K. Ding L. Tao D. Lu X. (2024). Take care of your prompt bias! Investigating and mitigating prompt bias in factual knowledge extraction. http:\/\/arxiv.org\/abs\/2403.09963","DOI":"10.63317\/3c82c6rhnq3c"},{"key":"e_1_3_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3649506"},{"key":"e_1_3_2_55_1","doi-asserted-by":"publisher","DOI":"10.1080\/10447318.2024.2316370"},{"key":"e_1_3_2_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3490099.3511105"},{"key":"e_1_3_2_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3544548.3581388"},{"key":"e_1_3_2_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3400824"},{"key":"e_1_3_2_59_1","unstructured":"Zhang Y. Li Y. Cui L. Cai D. Liu L. Fu T. Huang X. Zhao E. Zhang Y. & Chen Y. (2023). Siren\u2019s song in the AI ocean: A survey on hallucination in large language models. ArXiv Preprint ArXiv:2309.01219"},{"key":"e_1_3_2_60_1","unstructured":"Zhao W. X. Zhou K. Li J. Tang T. Wang X. Hou Y. Min Y. Zhang B. Zhang J. Dong Z. Du Y. Yang C. Chen Y. Chen Z. Jiang J. Ren R. Li Y. Tang X. Liu Z. \u2026 Wen J.-R. (2023). A survey of large language models. http:\/\/arxiv.org\/abs\/2303.18223"},{"key":"e_1_3_2_61_1","unstructured":"Zhong Q. Ding L. Liu J. Du B. Tao D. (2023). Can ChatGPT understand too? A comparative study on ChatGPT and fine-tuned BERT. http:\/\/arxiv.org\/abs\/2302.10198"}],"container-title":["International Journal of Human\u2013Computer Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/10447318.2025.2530100","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T05:39:23Z","timestamp":1772775563000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/10447318.2025.2530100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,4]]},"references-count":60,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,2,16]]}},"alternative-id":["10.1080\/10447318.2025.2530100"],"URL":"https:\/\/doi.org\/10.1080\/10447318.2025.2530100","relation":{},"ISSN":["1044-7318","1532-7590"],"issn-type":[{"value":"1044-7318","type":"print"},{"value":"1532-7590","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,4]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=hihc20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=hihc20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2024-10-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-30","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}