Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems
Abstract
Few categories of people are thought to have gained more from remote work than parents of pre-adolescent children. We put this notion to the test by performing naturalistic monitoring for four days of a hybrid health professional with children and an onsite health professional with no children (control). The monitoring results, which are also visualized as comic strips, suggest that simultaneous performance of remote work and parenting tasks leads to increased stress and reduced productivity. These negative outcomes appear to be driven by the discordance between the parent’s work tasks and children’s activities at home. We theorize that the parent’s work tasks and children’s activities can become concordant in the mold of family farming practices of old, where children were incorporated into the parents’ activities in the field. In the context of modern white-collar work, such concordance can be achieved by designing imaginative extensions to the parent’s professional work software.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Empirical work shows varying levels of AI assistance can have U-shaped effects on quality and productivity, highlighting the need to personalize scaffolding to user needs.
“Empirical work shows varying levels of AI assistance can have Ushaped effects on quality and productivity, highlighting the need to personalize scaffolding to user needs [15].”
From How Do Human Creators Embrace Human-AI Co-Creation? A Perspective on Human Agency of Screenwriters (2026) · cites this paper as [15]
In the Creative context, support for planning takes the form of generative ideation, usually presented as suggestions, although some systems have a more equal and collaborative storytelling focus that weaves the AI ideas into the story text.
“Support for planning takes the form of generative ideation, usually presented as suggestions [74,135], although some systems have a more equal and collaborative storytelling focus that weaves the AI ideas into the story text [70,92].”
From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [135]
Cited by
Sorted by citations
The rise and potential of large language model based agents: a survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4222-0 · Cited by 970 · see citing works
Hadi Mogavi, R., Deng, C., Juho Kim, J., Zhou, P., D. Kwon, Y., Hosny Saleh Metwally, A., Tlili, A., Bassanelli, S., Bucchiarone, A., Gujar, S., et al. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100027 · Cited by 369 · see citing works
The digital workplace and its dark side: An integrative review
Marsh, E., Vallejos, E. P., & Spence, A. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107118 · Cited by 348 · see citing works
Human-centred learning analytics and AI in education: A systematic literature review
Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100215 · Cited by 281 · see citing works
He, J., Treude, C., & Lo, D. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3712003 · Cited by 246 · see citing works
Ning, Y., Teixayavong, S., Shang, Y., Savulescu, J., Nagaraj, V., Miao, D., Mertens, M., Ting, D. S. W., Ong, J. C. L., Liu, M., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00143-2 · Cited by 212 · see citing works
Lai, V., Chen, C., Smith-Renner, A., Liao, Q. V., & Tan, C. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594087 · Cited by 204 · see citing works
Kwon, H., Tong, C., Haresamudram, H., Gao, Y., Abowd, G. D., Lane, N. D., & Plötz, T. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3411841 · Cited by 152 · see citing works
Integrating generative AI in knowledge building
Chen, B., Zhu, X., & Díaz del Castillo H., F. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100184 · Cited by 159 · see citing works
Social Media Use and adolescents' mental health and well-being: An umbrella review
Sala, A., Porcaro, L., & Gómez, E. (2024) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2024.100404 · Cited by 120 · see citing works
Exiting the Cleanroom: On Ecological Validity and Ubiquitous Computing
Carter, S., Mankoff, J., Klemmer, S. R., & Matthews, T. (2008) · Human–Computer Interaction · doi:10.1080/07370020701851086 · Cited by 107 · see citing works
Generative AI for corpus approaches to discourse studies: A critical evaluation of ChatGPT
Curry, N., Baker, P., & Brookes, G. (2024) · Applied Corpus Linguistics · doi:10.1016/j.acorp.2023.100082 · Cited by 102 · see citing works
When and How to Use AI in the Design Process? Implications for Human-AI Design Collaboration
Lee, S.-Y., Law, M., & Hoffman, G. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2353451 · Cited by 97 · see citing works
Retrieval-Augmented Generation (RAG)
Klesel, M., & Wittmann, H. F. (2025) · Business & Information Systems Engineering · doi:10.1007/s12599-025-00945-3 · Cited by 92 · see citing works
The Value, Benefits, and Concerns of Generative AI-Powered Assistance in Writing
Li, Z., Liang, C., Peng, J., & Yin, M. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642625 · Cited by 81 · see citing works
Saihi, A., Ben-Daya, M., Hariga, M., & As'ad, R. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100274 · Cited by 75 · see citing works
Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers
Diel, A., Lalgi, T., Schröter, I. C., MacDorman, K. F., Teufel, M., & Bäuerle, A. (2024) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2024.100538 · Cited by 64 · see citing works
Hou, C., Zhu, G., & Sudarshan, V. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13613 · Cited by 72 · see citing works
Stone, B. W. (2025) · Teaching of Psychology · doi:10.1177/00986283241305398 · Cited by 54 · see citing works
Psychological Traits and Appropriate Reliance: Factors Shaping Trust in AI
Küper, A., & Krämer, N. (2024) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2348216 · Cited by 49 · see citing works
Garg, A., Nisumba Soodhani, K., & Rajendran, R. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100380 · Cited by 49 · see citing works
Xing, W., Song, Y., Li, C., Liu, Z., Zhu, W., & Oh, H. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13586 · Cited by 50 · see citing works
Liu, M., Zhang, L. J., & Zhang, D. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108569 · Cited by 43 · see citing works
Large language models meet user interfaces: The case of provisioning feedback
Pozdniakov, S., Brazil, J., Abdi, S., Bakharia, A., Sadiq, S., Gašević, D., Denny, P., & Khosravi, H. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100289 · Cited by 43 · see citing works
Dangol, A., Newman, M., Wolfe, R., Lee, J. H., Kientz, J. A., Yip, J., & Pitt, C. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3661515 · Cited by 42 · see citing works
Generative AI and the future of connectivist learning in higher education
Liang, E. S., & Bai, S. (2025) · Journal of Asian Public Policy · doi:10.1080/17516234.2024.2386085 · Cited by 34 · see citing works
Temporal Presence Variation in Immersive Computer Games
Chung, J., & Gardner, H. J. (2012) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2011.627298 · Cited by 32 · see citing works
Zhang, S., Wang, H., & Yi, X. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757594 · Cited by 36 · see citing works
Song, Y., Cui, M., Wan, F., Yu, Z., & Jiang, J. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2025.2483858 · Cited by 30 · see citing works
Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process
Reza, M., Thomas-Mitchell, J., Dushniku, P., Laundry, N., Williams, J. J., & Kuzminykh, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757566 · Cited by 37 · see citing works
Fostering children’s creativity through LLM-driven storytelling with a social robot
Elgarf, M., Salam, H., & Peters, C. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1457429 · Cited by 28 · see citing works
Eagle, T., Baltaxe-Admony, L. B., & Ringland, K. E. (2024) · ACM Transactions on Accessible Computing · doi:10.1145/3689648 · Cited by 20 · see citing works
Hartmuth, D., Amin, R. M., Schlichting, D., Schött, S., Haudenschild, J., Küchenhoff, H., & Neuhaus, B. J. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2470563 · Cited by 7 · see citing works
Qorich, M., & El Ouazzani, R. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128405 · Cited by 6 · see citing works
Man, S. S., Su, P., Xiao, C., Yun, H., & Chan, A. H. S. (2026) · Ergonomics · doi:10.1080/00140139.2025.2554243 · Cited by 7 · see citing works
Mastrianni, A., Kim, M. S., Sullivan, T. M., Sippel, G. J., Burd, R. S., Gajos, K. Z., & Sarcevic, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757512 · Cited by 6 · see citing works
How Do Human Creators Embrace Human-AI Co-Creation? A Perspective on Human Agency of Screenwriters
Tang, Y., Zhou, J., Li, H., Xie, X., Ma, X., & Qu, H. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790300 · Cited by 5 · see citing works
“It’s Just a Wild, Wild West”: Harnessing Public Procurement as an AI Governance Mechanism
Hudig, A. I., Kallina, E., & Singh, J. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791968 · Cited by 3 · see citing works
How Do Human Creators Embrace Human-AI Co-Creation? A Perspective on Human Agency of Screenwriters
Tang, Y., Zhou, J., Li, H., Xie, X., Ma, X., & Qu, H. (2026) · arXiv · Cited by 5
Performance and Metacognition Disconnect when Reasoning in Human-AI Interaction
Fernandes, D., Villa, S., Nicholls, S., Haavisto, O., Buschek, D., Schmidt, A., Kosch, T., Shen, C., & Welsch, R. (2024) · arXiv · doi:10.48550/arxiv.2409.16708 · Cited by 71
- Authors
- Rott, C., Fettah, K., Pavlidis, I., & Akleman, E.
- Publisher
- ACM
- Published
- 2024-05-11
- OpenAlex
- W2769533150
- Cited by
- 896
- References
- 0
- Citations by year
- 2026: 2 · 2025: 410 · 2024: 313 · 2023: 55 · 2022: 24 · 2021: 13 · 2020: 26 · 2019: 3 · 2018: 3 · 2017: 4 · 2016: 3 · 2014: 1 · 2013: 1 · 2012: 2
- Type
- paratext
- Language
- en
- Open access
- closed
- Locations
- Research Publications (Maastricht University) (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Work-Family Balance Challenges
- Social Sciences › Decision Sciences › Information Systems and Management › Personal Information Management and User Behavior
- Social Sciences › Psychology › Social Psychology › Technostress in Professional Settings
- Keywords
- remote work, parenting, work-family conflict, hybrid work, stress, productivity, comic strips, concordance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review