{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T14:01:43Z","timestamp":1779976903621,"version":"3.53.1"},"reference-count":61,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"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":[[2026,5,31]]},"abstract":"<jats:p>Gaze event detection is fundamental to vision science, human-computer interaction, and applied analytics. However, current workflows often require specialized programming knowledge and careful handling of heterogeneous raw data formats. Classical detectors such as I-VT and I-DT are effective but highly sensitive to preprocessing and parameterization, limiting their usability outside specialized laboratories. This work introduces a code-free, large language model (LLM)-driven pipeline that converts natural language instructions into an end-to-end analysis. The system (1) inspects raw eye-tracking files to infer structure and metadata, (2) generates executable routines for data cleaning and detector implementation from concise user prompts, (3) applies the generated detector to label fixations and saccades, and (4) returns results and explanatory reports, and allows users to iteratively optimize their code by editing the prompt. Evaluated on public benchmarks, the approach achieves accuracy comparable to traditional methods while substantially reducing technical overhead. The framework lowers barriers to entry for eye-tracking research, providing a flexible and accessible alternative to code-intensive workflows.<\/jats:p>","DOI":"10.1145\/3806038","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T12:44:33Z","timestamp":1779972273000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Lazy or Efficient? Towards Accessible Eye-Tracking Event Detection Using LLMs ETRA024"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2088-9330","authenticated-orcid":false,"given":"Dongyang","family":"Guo","sequence":"first","affiliation":[{"name":"Technical University of Munich","place":["Munich, Germany"]},{"name":"Munich Center for Machine Learning (MCML)","place":["Munich, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8895-4997","authenticated-orcid":false,"given":"Yasmeen","family":"Abdrabou","sequence":"additional","affiliation":[{"name":"Technical University of Munich","place":["M\u00fcnchen, Germany"]},{"name":"Munich Center for Machine Learning (MCML)","place":["M\u00fcnchen, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3146-4484","authenticated-orcid":false,"given":"Enkelejda","family":"Kasneci","sequence":"additional","affiliation":[{"name":"Technical University of Munich","place":["Munich, Germany"]},{"name":"Munich Center for Machine Learning (MCML)","place":["Munich, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3715669.3726788"},{"key":"e_1_3_2_3_1","unstructured":"Nikhil Abhyankar Parshin Shojaee and Chandan\u00a0K Reddy. 2025. LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2503.14434 (2025)."},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2857491.2857521"},{"key":"e_1_3_2_5_1","doi-asserted-by":"crossref","unstructured":"Ioannis Agtzidis Mikhail Startsev and Michael Dorr. 2020. Two hours in Hollywood: A manually annotated ground truth data set of eye movements during movie clip watching. Journal of Eye Movement Research 13 4 (2020) 10\u201316910.","DOI":"10.16910\/jemr.13.4.5"},{"key":"e_1_3_2_6_1","doi-asserted-by":"crossref","unstructured":"Richard Andersson Linnea Larsson Kenneth Holmqvist Martin Stridh and Marcus Nystr\u00f6m. 2017. One algorithm to rule them all? An evaluation and discussion of ten eye movement event-detection algorithms. Behavior research methods 49 2 (2017) 616\u2013637.","DOI":"10.3758\/s13428-016-0738-9"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-0716-2391-6_10"},{"key":"e_1_3_2_8_1","doi-asserted-by":"crossref","unstructured":"David\u00a0J Berg Susan\u00a0E Boehnke Robert\u00a0A Marino Douglas\u00a0P Munoz and Laurent Itti. 2009. Free viewing of dynamic stimuli by humans and monkeys. Journal of vision 9 5 (2009) 19\u201319.","DOI":"10.1167\/9.5.19"},{"key":"e_1_3_2_9_1","doi-asserted-by":"crossref","unstructured":"Birtukan Birawo and Pawel Kasprowski. 2022. Review and evaluation of eye movement event detection algorithms. Sensors 22 22 (2022) 8810.","DOI":"10.3390\/s22228810"},{"key":"e_1_3_2_10_1","doi-asserted-by":"crossref","unstructured":"Pieter Blignaut. 2009. Fixation identification: The optimum threshold for a dispersion algorithm. Attention Perception & Psychophysics 71 4 (2009) 881\u2013895.","DOI":"10.3758\/APP.71.4.881"},{"key":"e_1_3_2_11_1","unstructured":"Sebastian Bordt Ben Lengerich Harsha Nori and Rich Caruana. 2024. Data science with LLMs and interpretable models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2402.14474 (2024)."},{"key":"e_1_3_2_12_1","doi-asserted-by":"crossref","unstructured":"Efe Bozkir Onur G\u00fcnl\u00fc Wolfgang Fuhl Rafael\u00a0F Schaefer and Enkelejda Kasneci. 2021. Differential privacy for eye tracking with temporal correlations. Plos one 16 8 (2021) e0255979.","DOI":"10.1371\/journal.pone.0255979"},{"key":"e_1_3_2_13_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.naacl-demo.2"},{"key":"e_1_3_2_14_1","unstructured":"Xinyun Chen Maxwell Lin Nathanael Sch\u00e4rli and Denny Zhou. 2023. Teaching large language models to self-debug. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2304.05128 (2023)."},{"key":"e_1_3_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR56361.2022.9956687"},{"key":"e_1_3_2_16_1","doi-asserted-by":"crossref","unstructured":"Asim\u00a0H Dar Adina\u00a0S Wagner and Michael Hanke. 2021. REMoDNaV: robust eye-movement classification for dynamic stimulation. Behavior research methods 53 1 (2021) 399\u2013414.","DOI":"10.3758\/s13428-020-01428-x"},{"key":"e_1_3_2_17_1","doi-asserted-by":"crossref","unstructured":"Michael Dorr Thomas Martinetz Karl\u00a0R Gegenfurtner and Erhardt Barth. 2010. Variability of eye movements when viewing dynamic natural scenes. Journal of vision 10 10 (2010) 28\u201328.","DOI":"10.1167\/10.10.28"},{"key":"e_1_3_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3674805.3686672"},{"key":"e_1_3_2_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-84628-609-4_12"},{"key":"e_1_3_2_20_1","doi-asserted-by":"crossref","unstructured":"Claudia Ehmke and Stephanie Wilson. 2007. Identifying web usability problems from eyetracking data. (2007).","DOI":"10.14236\/ewic\/HCI2007.12"},{"key":"e_1_3_2_21_1","doi-asserted-by":"crossref","unstructured":"Carlos Elmadjian Candy Gonzales Rodrigo Lima\u00a0da Costa and Carlos\u00a0H Morimoto. 2023. Online eye-movement classification with temporal convolutional networks. Behavior Research Methods 55 7 (2023) 3602\u20133620.","DOI":"10.3758\/s13428-022-01978-2"},{"key":"e_1_3_2_22_1","doi-asserted-by":"crossref","unstructured":"Ralf Engbert and Reinhold Kliegl. 2003. Microsaccades uncover the orientation of covert attention. Vision research 43 9 (2003) 1035\u20131045.","DOI":"10.1016\/S0042-6989(03)00084-1"},{"key":"e_1_3_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3279810.3279843"},{"key":"e_1_3_2_24_1","doi-asserted-by":"crossref","unstructured":"Henry Griffith Dillon Lohr Evgeny Abdulin and Oleg Komogortsev. 2021. GazeBase a large-scale multi-stimulus longitudinal eye movement dataset. Scientific Data 8 1 (2021) 184.","DOI":"10.1038\/s41597-021-00959-y"},{"key":"e_1_3_2_25_1","unstructured":"Yang Gu Hengyu You Jian Cao Muran Yu Haoran Fan and Shiyou Qian. 2024. Large language models for constructing and optimizing machine learning workflows: A survey. ACM Transactions on Software Engineering and Methodology (2024)."},{"key":"e_1_3_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3716553.3750787"},{"key":"e_1_3_2_27_1","volume-title":"Eye tracking: A comprehensive guide to methods and measures","author":"Holmqvist Kenneth","year":"2011","unstructured":"Kenneth Holmqvist, Marcus Nystr\u00f6m, Richard Andersson, Richard Dewhurst, Halszka Jarodzka, and Joost Van\u00a0de Weijer. 2011. Eye tracking: A comprehensive guide to methods and measures. oup Oxford."},{"key":"e_1_3_2_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-044451020-4\/50031-1"},{"key":"e_1_3_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3649902.3653942"},{"key":"e_1_3_2_30_1","doi-asserted-by":"crossref","unstructured":"Yingying Jiao and Fei Jiang. 2022. Detecting slow eye movements with bimodal-LSTM for recognizing drivers\u2019 sleep onset period. Biomedical Signal Processing and Control 75 (2022) 103608.","DOI":"10.1016\/j.bspc.2022.103608"},{"key":"e_1_3_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3701716.3717812"},{"key":"e_1_3_2_32_1","unstructured":"Enkelejda Kasneci Hong Gao Suleyman Ozdel Virmarie Maquiling Enkeleda Thaqi Carrie Lau Yao Rong Gjergji Kasneci and Efe Bozkir. 2024. Introduction to eye tracking: A hands-on tutorial for students and practitioners. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2404.15435 (2024)."},{"key":"e_1_3_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2578153.2578213"},{"key":"e_1_3_2_34_1","unstructured":"Moe Kayali Fabian Wenz Nesime Tatbul and \u00c7a\u011fatay Demiralp. 2024. Mind the data gap: Bridging llms to enterprise data integration. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2412.20331 (2024)."},{"key":"e_1_3_2_35_1","doi-asserted-by":"crossref","unstructured":"Oleg\u00a0V Komogortsev and Alex Karpov. 2013. Automated classification and scoring of smooth pursuit eye movements in the presence of fixations and saccades. Behavior research methods 45 1 (2013) 203\u2013215.","DOI":"10.3758\/s13428-012-0234-9"},{"key":"e_1_3_2_36_1","doi-asserted-by":"crossref","unstructured":"Seth\u00a0D K\u00f6nig and Elizabeth\u00a0A Buffalo. 2014. A nonparametric method for detecting fixations and saccades using cluster analysis: Removing the need for arbitrary thresholds. Journal of neuroscience methods 227 (2014) 121\u2013131.","DOI":"10.1016\/j.jneumeth.2014.01.032"},{"key":"e_1_3_2_37_1","doi-asserted-by":"crossref","unstructured":"Linn\u00e9a Larsson Marcus Nystr\u00f6m Richard Andersson and Martin Stridh. 2015. Detection of fixations and smooth pursuit movements in high-speed eye-tracking data. Biomedical Signal Processing and Control 18 (2015) 145\u2013152.","DOI":"10.1016\/j.bspc.2014.12.008"},{"key":"e_1_3_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3650203.3663334"},{"key":"e_1_3_2_39_1","doi-asserted-by":"crossref","unstructured":"Jia\u00a0Zheng Lim James Mountstephens and Jason Teo. 2020. Emotion recognition using eye-tracking: taxonomy review and current challenges. Sensors 20 8 (2020) 2384.","DOI":"10.3390\/s20082384"},{"key":"e_1_3_2_40_1","doi-asserted-by":"crossref","unstructured":"Jia\u00a0Zheng Lim James Mountstephens and Jason Teo. 2022. Eye-tracking feature extraction for biometric machine learning. Frontiers in neurorobotics 15 (2022) 796895.","DOI":"10.3389\/fnbot.2021.796895"},{"key":"e_1_3_2_41_1","doi-asserted-by":"crossref","unstructured":"Xiaoxiao Liu and Ying Cui. 2025. Eye tracking technology for examining cognitive processes in education: A systematic review. Computers & Education (2025) 105263.","DOI":"10.1016\/j.compedu.2025.105263"},{"key":"e_1_3_2_42_1","doi-asserted-by":"crossref","unstructured":"Malte L\u00fcken \u0160imon Kucharsk\u1ef3 and Ingmar Visser. 2022. Characterising eye movement events with an Unsupervised hidden Markov model. Journal of Eye Movement Research 15 1 (2022) 10\u201316910.","DOI":"10.16910\/jemr.15.1.4"},{"key":"e_1_3_2_43_1","doi-asserted-by":"crossref","unstructured":"Marcus Nystr\u00f6m and Kenneth Holmqvist. 2010. An adaptive algorithm for fixation saccade and glissade detection in eyetracking data. Behavior research methods 42 1 (2010) 188\u2013204.","DOI":"10.3758\/BRM.42.1.188"},{"key":"e_1_3_2_44_1","unstructured":"Danrui Qi Zhengjie Miao and Jiannan Wang. 2024. Cleanagent: Automating data standardization with llm-based agents. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2403.08291 (2024)."},{"key":"e_1_3_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/355017.355028"},{"key":"e_1_3_2_46_1","unstructured":"Javier San\u00a0Agustin. 2010. Off-the-shelf gaze interaction. (2010)."},{"key":"e_1_3_2_47_1","doi-asserted-by":"crossref","unstructured":"D Sauter BJ Martin N Di\u00a0Renzo and C Vomscheid. 1991. Analysis of eye tracking movements using innovations generated by a Kalman filter. Medical and biological Engineering and Computing 29 1 (1991) 63\u201369.","DOI":"10.1007\/BF02446297"},{"key":"e_1_3_2_48_1","doi-asserted-by":"crossref","unstructured":"Timo Schick Jane Dwivedi-Yu Roberto Dess\u00ec Roberta Raileanu Maria Lomeli Eric Hambro Luke Zettlemoyer Nicola Cancedda and Thomas Scialom. 2023. Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems 36 (2023) 68539\u201368551.","DOI":"10.52202\/075280-2997"},{"key":"e_1_3_2_49_1","unstructured":"Segev Shlomov Avi Yaeli Sami Marreed Sivan Schwartz Netanel Eder Offer Akrabi and Sergey Zeltyn. 2024. Ida: Breaking barriers in no-code ui automation through large language models and human-centric design. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2407.15673 (2024)."},{"key":"e_1_3_2_50_1","volume-title":"20th International Conference on Wirtschaftsinformatik (WI)","author":"Sonnabend David","year":"2025","unstructured":"David Sonnabend, Mahei Li, and Christoph Peters. 2025. LLMs for Intelligent Automation-Insights from a Systematic Literature Review. In 20th International Conference on Wirtschaftsinformatik (WI)."},{"key":"e_1_3_2_51_1","doi-asserted-by":"crossref","unstructured":"Mikhail Startsev Ioannis Agtzidis and Michael Dorr. 2019. 1D CNN with BLSTM for automated classification of fixations saccades and smooth pursuits. Behavior Research Methods 51 2 (2019) 556\u2013572.","DOI":"10.3758\/s13428-018-1144-2"},{"key":"e_1_3_2_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40728-4_56"},{"key":"e_1_3_2_53_1","unstructured":"Immanuel Trummer. 2023. From BERT to GPT-3 codex: harnessing the potential of very large language models for data management. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2306.09339 (2023)."},{"key":"e_1_3_2_54_1","unstructured":"Aleksei Turobov Diane Coyle and Verity Harding. 2024. Using ChatGPT for thematic analysis. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2405.08828 (2024)."},{"key":"e_1_3_2_55_1","doi-asserted-by":"crossref","unstructured":"Peiran Wang Yaoning Yu Ke Chen Xianyang Zhan and Haohan Wang. 2025. Large language model-based data science agent: A survey. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2508.02744 (2025).","DOI":"10.36227\/techrxiv.175519201.10732484\/v1"},{"key":"e_1_3_2_56_1","doi-asserted-by":"crossref","unstructured":"Sam\u00a0V Wass Tim\u00a0J Smith and Mark\u00a0H Johnson. 2013. Parsing eye-tracking data of variable quality to provide accurate fixation duration estimates in infants and adults. Behavior research methods 45 1 (2013) 229\u2013250.","DOI":"10.3758\/s13428-012-0245-6"},{"key":"e_1_3_2_57_1","doi-asserted-by":"crossref","unstructured":"Michel Wedel. 2013. Attention research in marketing: A review of eye tracking studies. Robert H. Smith School Research Paper No. RHS 2460289 (2013).","DOI":"10.2139\/ssrn.2460289"},{"key":"e_1_3_2_58_1","doi-asserted-by":"crossref","unstructured":"Michel Wedel Rik Pieters et\u00a0al. 2008. Eye tracking for visual marketing. Foundations and Trends\u00ae in Marketing 1 4 (2008) 231\u2013320.","DOI":"10.1561\/1700000011"},{"key":"e_1_3_2_59_1","unstructured":"Jinglue Xu Jialong Li Zhen Liu Nagar Anthel\u00a0Venkatesh Suryanarayanan Guoyuan Zhou Jia Guo Hitoshi Iba and Kenji Tei. 2024. Large language models synergize with automated machine learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2405.03727 (2024)."},{"key":"e_1_3_2_60_1","volume-title":"The eleventh international conference on learning representations","author":"Yao Shunyu","year":"2022","unstructured":"Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik\u00a0R Narasimhan, and Yuan Cao. 2022. React: Synergizing reasoning and acting in language models. In The eleventh international conference on learning representations."},{"key":"e_1_3_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2993901.2993908"},{"key":"e_1_3_2_62_1","doi-asserted-by":"crossref","unstructured":"Raimondas Zemblys Diederick\u00a0C Niehorster Oleg Komogortsev and Kenneth Holmqvist. 2018. Using machine learning to detect events in eye-tracking data. Behavior research methods 50 1 (2018) 160\u2013181.","DOI":"10.3758\/s13428-017-0860-3"}],"container-title":["Proceedings of the ACM on Human-Computer Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3806038","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T13:02:15Z","timestamp":1779973335000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3806038"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,28]]},"references-count":61,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5,31]]}},"alternative-id":["10.1145\/3806038"],"URL":"https:\/\/doi.org\/10.1145\/3806038","relation":{},"ISSN":["2573-0142"],"issn-type":[{"value":"2573-0142","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,28]]},"assertion":[{"value":"2026-05-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}