{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T17:19:37Z","timestamp":1783012777152,"version":"3.54.6"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Hum.-Comput. Interact."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>\n                    The rise of large language models (LLMs) has given rise to a class of prompt-based interactive systems where users primarily express their input in natural language. However, composing a prompt as a linear text string becomes unwieldy when capturing users\u2019 multifaceted intents. We present\n                    <jats:bold>Object-Oriented Prompting (<\/jats:bold>\n                    <jats:sans-serif>\n                      <jats:bold>OOPrompt<\/jats:bold>\n                    <\/jats:sans-serif>\n                    <jats:bold>)<\/jats:bold>\n                    , an emergent interaction paradigm that enables users to create, edit, iterate, and reuse prompts as structured, manipulable artifacts, unifying and generalizing several existing point systems. We first outlined a design space from existing work and built an early prototype, which we deployed as a probe in a formative study with 20 participants. Their feedback informed an expanded\n                    <jats:sans-serif>OOPrompt<\/jats:sans-serif>\n                    design space. We then developed the full\n                    <jats:sans-serif>OOPrompt<\/jats:sans-serif>\n                    prototype and conducted a validation study to further understand\n                    <jats:sans-serif>OOPrompt<\/jats:sans-serif>\n                    \u2019s added values and trade-offs. We expect the\n                    <jats:sans-serif>OOPrompt<\/jats:sans-serif>\n                    design space to provide theoretical and empirical guidance to the design and engineering of prompt-based, LLM-enabled interactive systems.\n                  <\/jats:p>","DOI":"10.1145\/3816761","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T15:39:14Z","timestamp":1782747554000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["OOPrompt: Reifying Intents into Structured Artifacts for Modular and Iterative Prompting EICS009"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4373-1586","authenticated-orcid":false,"given":"Tengyou","family":"Xu","sequence":"first","affiliation":[{"name":"HCI Research","place":["Los Angeles, USA"]},{"name":"UCLA","place":["Los Angeles, USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8782-0777","authenticated-orcid":false,"given":"Detao","family":"Ma","sequence":"additional","affiliation":[{"name":"UCLA","place":["Los Angeles, USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8527-1744","authenticated-orcid":false,"given":"Xiang 'Anthony'","family":"Chen","sequence":"additional","affiliation":[{"name":"HCI Research","place":["Los Angeles, USA"]},{"name":"UCLA","place":["Los Angeles, USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,29]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia\u00a0Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et\u00a0al. 2023. Gpt-4 technical report. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2303.08774 (2023)."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3642016"},{"key":"e_1_3_1_4_2","unstructured":"James Betker Gabriel Goh Li Jing Tim Brooks Jianfeng Wang Linjie Li Long Ouyang Juntang Zhuang Joyce Lee Yufei Guo et\u00a0al. 2023. Improving image generation with better captions. Computer Science. https:\/\/cdn. openai. com\/papers\/dall-e-3. pdf 2 3 (2023) 8."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.5555\/1407387"},{"key":"e_1_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Virginia Braun and Victoria Clarke. 2021. Thematic analysis: A practical guide. (2021).","DOI":"10.4135\/9781036232078.n9"},{"key":"e_1_3_1_7_2","unstructured":"Yuzhe Cai Shaoguang Mao Wenshan Wu Zehua Wang Yaobo Liang Tao Ge Chenfei Wu Wang You Ting Song Yan Xia Jonathan Tien Nan Duan and Furu Wei. 2024. Low-code LLM: Graphical User Interface over Large Language Models. arxiv:https:\/\/arXiv.org\/abs\/2304.08103\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2304.08103"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3715336.3735780"},{"key":"e_1_3_1_9_2","unstructured":"Ian Drosos Jack Williams Advait Sarkar and Nicholas Wilson. 2024. Dynamic Prompt Middleware: Contextual Prompt Refinement Controls for Comprehension Tasks. arxiv:https:\/\/arXiv.org\/abs\/2412.02357\u00a0[cs.HC] https:\/\/arxiv.org\/abs\/2412.02357"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3746059.3747710"},{"key":"e_1_3_1_11_2","first-page":"17","volume-title":"Proceedings of the SIGCHI conference on Human factors in computing systems","author":"Hutchinson Hilary","year":"2003","unstructured":"Hilary Hutchinson, Wendy Mackay, Bo Westerlund, Benjamin\u00a0B Bederson, Allison Druin, Catherine Plaisant, Michel Beaudouin-Lafon, St\u00e9phane Conversy, Helen Evans, Heiko Hansen, et\u00a0al. 2003. Technology probes: inspiring design for and with families. In Proceedings of the SIGCHI conference on Human factors in computing systems. 17\u201324."},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3586183.3606737"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Zhengbao Jiang Frank\u00a0F Xu Jun Araki and Graham Neubig. 2020. How can we know what language models know? Transactions of the Association for Computational Linguistics 8 (2020) 423\u2013438.","DOI":"10.1162\/tacl_a_00324"},{"key":"e_1_3_1_14_2","unstructured":"Ishika Joshi Simra Shahid Shreeya Venneti Manushree Vasu Yantao Zheng Yunyao Li Balaji Krishnamurthy and Gromit Yeuk-Yin Chan. 2024. CoPrompter: User-Centric Evaluation of LLM Instruction Alignment for Improved Prompt Engineering. arxiv:https:\/\/arXiv.org\/abs\/2411.06099\u00a0[cs.HC] https:\/\/arxiv.org\/abs\/2411.06099"},{"key":"e_1_3_1_15_2","unstructured":"Tushar Khot Harsh Trivedi Matthew Finlayson Yao Fu Kyle Richardson Peter Clark and Ashish Sabharwal. 2023. Decomposed Prompting: A Modular Approach for Solving Complex Tasks. arxiv:https:\/\/arXiv.org\/abs\/2210.02406\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2210.02406"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","unstructured":"Soomin Kim Jinsu Eun Yoobin\u00a0Elyson Park Kwangwon Lee Gyuho Lee and Joonhwan Lee. 2025. PromptPilot: Exploring User Experience of Prompting with AI-Enhanced Initiative in LLMs. International Journal of Human\u2013Computer Interaction (2025) 1\u201323. arXiv:10.1080\/10447318.2025.2489030 doi:10.1080\/10447318.2025.2489030","DOI":"10.1080\/10447318.2025.2489030"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3586183.3606833"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3642216"},{"key":"e_1_3_1_19_2","unstructured":"Jiahui Li and Roman Klinger. 2025. iPrOp: Interactive Prompt Optimization for Large Language Models with a Human in the Loop. arxiv:https:\/\/arXiv.org\/abs\/2412.12644\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2412.12644"},{"key":"e_1_3_1_20_2","unstructured":"Xiaoqiang Lin Zhongxiang Dai Arun Verma See-Kiong Ng Patrick Jaillet and Bryan Kian\u00a0Hsiang Low. 2024. Prompt Optimization with Human Feedback. arxiv:https:\/\/arXiv.org\/abs\/2405.17346\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2405.17346"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613905.3650756"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.emnlp-main.133"},{"key":"e_1_3_1_23_2","unstructured":"Huan Ma Changqing Zhang Yatao Bian Lemao Liu Zhirui Zhang Peilin Zhao Shu Zhang Huazhu Fu Qinghua Hu and Bingzhe Wu. 2023. Fairness-guided Few-shot Prompting for Large Language Models. arxiv:https:\/\/arXiv.org\/abs\/2303.13217\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2303.13217"},{"key":"e_1_3_1_24_2","unstructured":"Qianou Ma Weirui Peng Hua Shen Kenneth Koedinger and Tongshuang Wu. 2024. What you say= what you want? Teaching humans to articulate requirements for LLMs."},{"key":"e_1_3_1_25_2","doi-asserted-by":"crossref","unstructured":"Qianou Ma Weirui Peng Chenyang Yang Hua Shen Ken Koedinger and Tongshuang Wu. 2025. What should we engineer in prompts? training humans in requirement-driven llm use. ACM Transactions on Computer-Human Interaction 32 4 (2025) 1\u201327.","DOI":"10.1145\/3731756"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/263552.263612"},{"key":"e_1_3_1_27_2","unstructured":"Yuetian Mao Junjie He and Chunyang Chen. 2025. From Prompts to Templates: A Systematic Prompt Template Analysis for Real-world LLMapps. arxiv:https:\/\/arXiv.org\/abs\/2504.02052\u00a0[cs.SE] https:\/\/arxiv.org\/abs\/2504.02052"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3642462"},{"key":"e_1_3_1_29_2","unstructured":"Aditi Mishra Utkarsh Soni Anjana Arunkumar Jinbin Huang Bum\u00a0Chul Kwon and Chris Bryan. 2025. PromptAid: Prompt Exploration Perturbation Testing and Iteration using Visual Analytics for Large Language Models. arxiv:https:\/\/arXiv.org\/abs\/2304.01964\u00a0[cs.HC] https:\/\/arxiv.org\/abs\/2304.01964"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-acl.50"},{"key":"e_1_3_1_31_2","volume-title":"The design of everyday things: Revised and expanded edition","author":"Norman Don","year":"2013","unstructured":"Don Norman. 2013. The design of everyday things: Revised and expanded edition. Basic books."},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544549.3585628"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3665318.3677159"},{"key":"e_1_3_1_34_2","unstructured":"Aditya Ramesh Prafulla Dhariwal Alex Nichol Casey Chu and Mark Chen. 2022. Hierarchical Text-Conditional Image Generation with CLIP Latents. arxiv:https:\/\/arXiv.org\/abs\/2204.06125\u00a0[cs.CV] https:\/\/arxiv.org\/abs\/2204.06125"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411763.3451760"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3714259"},{"key":"e_1_3_1_37_2","unstructured":"Rupak Sarkar Bahareh Sarrafzadeh Nirupama Chandrasekaran Nagu Rangan Philip Resnik Longqi Yang and Sujay\u00a0Kumar Jauhar. 2025. Conversational user-ai intervention: A study on prompt rewriting for improved llm response generation. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2503.16789 (2025)."},{"key":"e_1_3_1_38_2","unstructured":"Sander Schulhoff Michael Ilie Nishant Balepur Konstantine Kahadze Amanda Liu Chenglei Si Yinheng Li Aayush Gupta HyoJung Han Sevien Schulhoff Pranav\u00a0Sandeep Dulepet Saurav Vidyadhara Dayeon Ki Sweta Agrawal Chau Pham Gerson Kroiz Feileen Li Hudson Tao Ashay Srivastava Hevander\u00a0Da Costa Saloni Gupta Megan\u00a0L. Rogers Inna Goncearenco Giuseppe Sarli Igor Galynker Denis Peskoff Marine Carpuat Jules White Shyamal Anadkat Alexander Hoyle and Philip Resnik. 2025. The Prompt Report: A Systematic Survey of Prompt Engineering Techniques. arxiv:https:\/\/arXiv.org\/abs\/2406.06608\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2406.06608"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3706599.3720080"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","unstructured":"Shneiderman. 1983. Direct Manipulation: A Step Beyond Programming Languages. Computer 16 8 (1983) 57\u201369. doi:10.1109\/MC.1983.1654471","DOI":"10.1109\/MC.1983.1654471"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3642754"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3613904.3642400"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3586183.3606756"},{"key":"e_1_3_1_44_2","unstructured":"Gemini Team Rohan Anil Sebastian Borgeaud Jean-Baptiste Alayrac Jiahui Yu Radu Soricut Johan Schalkwyk Andrew\u00a0M Dai Anja Hauth Katie Millican et\u00a0al. 2023. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2312.11805 (2023)."},{"key":"e_1_3_1_45_2","unstructured":"Ming Wang Yuanzhong Liu Xiaoyu Liang Songlian Li Yijie Huang Xiaoming Zhang Sijia Shen Chaofeng Guan Daling Wang Shi Feng Huaiwen Zhang Yifei Zhang Minghui Zheng and Chi Zhang. 2024. LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language. arxiv:https:\/\/arXiv.org\/abs\/2402.16929\u00a0[cs.SE] https:\/\/arxiv.org\/abs\/2402.16929"},{"key":"e_1_3_1_46_2","unstructured":"Jason Wei Xuezhi Wang Dale Schuurmans Maarten Bosma Brian Ichter Fei Xia Ed Chi Quoc Le and Denny Zhou. 2023. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arxiv:https:\/\/arXiv.org\/abs\/2201.11903\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2201.11903"},{"key":"e_1_3_1_47_2","unstructured":"Qingyun Wu Gagan Bansal Jieyu Zhang Yiran Wu Beibin Li Erkang Zhu Li Jiang Xiaoyun Zhang Shaokun Zhang Jiale Liu Ahmed\u00a0Hassan Awadallah Ryen\u00a0W White Doug Burger and Chi Wang. 2023. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation. arxiv:https:\/\/arXiv.org\/abs\/2308.08155\u00a0[cs.AI] https:\/\/arxiv.org\/abs\/2308.08155"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3491101.3519729"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3491102.3517582"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/2858036.2858075"},{"key":"e_1_3_1_51_2","unstructured":"Jinyu Xiang Jiayi Zhang Zhaoyang Yu Xinbing Liang Fengwei Teng Jinhao Tu Fashen Ren Xiangru Tang Sirui Hong Chenglin Wu and Yuyu Luo. 2025. Self-Supervised Prompt Optimization. arxiv:https:\/\/arXiv.org\/abs\/2502.06855\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2502.06855"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3640543.3645196"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3544548.3581388"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","unstructured":"Zhongyi Zhou Jing Jin Vrushank Phadnis Xiuxiu Yuan Jun Jiang Xun Qian Kristen Wright Mark Sherwood Jason Mayes Jingtao Zhou Yiyi Huang Zheng Xu Yinda Zhang Johnny Lee Alex Olwal David Kim Ram Iyengar Na Li and Ruofei Du. 2025. InstructPipe: Generating Visual Blocks Pipelines with Human Instructions and LLMs. arxiv:https:\/\/arXiv.org\/abs\/2312.09672\u00a0[cs.HC] doi:10.1145\/3706598.3713905","DOI":"10.1145\/3706598.3713905"}],"container-title":["Proceedings of the ACM on Human-Computer Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3816761","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:37:31Z","timestamp":1783010251000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3816761"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,29]]},"references-count":54,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,6,30]]}},"alternative-id":["10.1145\/3816761"],"URL":"https:\/\/doi.org\/10.1145\/3816761","relation":{},"ISSN":["2573-0142"],"issn-type":[{"value":"2573-0142","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,29]]},"assertion":[{"value":"2026-06-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}