2026
1.
Dmitri A. Kalashnikov; John T. Abatzoglou; Daniel L. Swain
Climatology and trends of annual maximum subdaily precipitation in the Western United States Journal Article
In: Weather and Climate Extremes, vol. 53, pp. 100915, 2026, ISSN: 2212-0947.
Abstract | Links | BibTeX | Tags:
@article{kalashnikov_climatology_2026,
title = {Climatology and trends of annual maximum subdaily precipitation in the Western United States},
author = {Dmitri A. Kalashnikov and John T. Abatzoglou and Daniel L. Swain},
url = {https://www.sciencedirect.com/science/article/pii/S2212094726000666},
doi = {10.1016/j.wace.2026.100915},
issn = {2212-0947},
year = {2026},
date = {2026-09-01},
urldate = {2026-09-01},
journal = {Weather and Climate Extremes},
volume = {53},
pages = {100915},
abstract = {Short-duration precipitation extremes can threaten public safety and infrastructure by generating flash flooding and geophysical mass wasting events including mudslides and debris flows. Using two surface gauge-based precipitation datasets (1980-2024), we characterize the climatology of annual maximum subdaily precipitation and quantify trends across the western United States (WUS) \textendash a topographically complex region with widely varying precipitation regimes prone to flash flooding. We find that 60.7% of WUS stations, including the vast majority of those located in the continental interior, typically experience 1-hr annual maximum precipitation (AMP) during summer and during the afternoon and evening hours (12:00-23:00 local time). Although most stations do not show statistically significant trends in 1-hr AMP intensity over the full period of record (1980-2024), a significant 10.3% domain-median increase in 1-hr AMP intensity was observed during 2000-2024. These changes largely result from seasonal maximum precipitation (SMP) increases during summer over the continental interior coinciding with a trend toward more favorable summer thermodynamic environments for short-duration precipitation extremes. We also report widespread though statistically insignificant increases in SMP intensity during winter and spring in California since 2000 coinciding with increased column water vapor. These results are suggestive of potential recent intensification of subdaily precipitation extremes in a warming climate in the WUS despite a backdrop of considerable internal variability.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Short-duration precipitation extremes can threaten public safety and infrastructure by generating flash flooding and geophysical mass wasting events including mudslides and debris flows. Using two surface gauge-based precipitation datasets (1980-2024), we characterize the climatology of annual maximum subdaily precipitation and quantify trends across the western United States (WUS) – a topographically complex region with widely varying precipitation regimes prone to flash flooding. We find that 60.7% of WUS stations, including the vast majority of those located in the continental interior, typically experience 1-hr annual maximum precipitation (AMP) during summer and during the afternoon and evening hours (12:00-23:00 local time). Although most stations do not show statistically significant trends in 1-hr AMP intensity over the full period of record (1980-2024), a significant 10.3% domain-median increase in 1-hr AMP intensity was observed during 2000-2024. These changes largely result from seasonal maximum precipitation (SMP) increases during summer over the continental interior coinciding with a trend toward more favorable summer thermodynamic environments for short-duration precipitation extremes. We also report widespread though statistically insignificant increases in SMP intensity during winter and spring in California since 2000 coinciding with increased column water vapor. These results are suggestive of potential recent intensification of subdaily precipitation extremes in a warming climate in the WUS despite a backdrop of considerable internal variability.
2.
Miranda Cravetz; Alejandro Velasquez; Jared Northrop; Cindy Grimm; Joseph R. Davidson
Design and evaluation of two heuristic-based interactive controllers for selective apple harvest Journal Article
In: Computers and Electronics in Agriculture, vol. 250, pp. 111834, 2026, ISSN: 0168-1699.
Abstract | Links | BibTeX | Tags:
@article{cravetz_design_2026,
title = {Design and evaluation of two heuristic-based interactive controllers for selective apple harvest},
author = {Miranda Cravetz and Alejandro Velasquez and Jared Northrop and Cindy Grimm and Joseph R. Davidson},
url = {https://www.sciencedirect.com/science/article/pii/S0168169926004291},
doi = {10.1016/j.compag.2026.111834},
issn = {0168-1699},
year = {2026},
date = {2026-08-01},
urldate = {2026-08-01},
journal = {Computers and Electronics in Agriculture},
volume = {250},
pages = {111834},
abstract = {The picking action used by an apple harvesting robot has a critical impact on post-harvest fruit quality and long-term tree health. A poorly executed pick can lead to excessive fruit bruising, stem pulls, and limb breaks. The prior work has typically considered the picking phase an open loop motion control problem, using patterns such as linear pull, inclined pull, pull-twist, etc. In this paper, we consider picking an interaction control problem whereby force feedback is used to guide robot motion. We designed two picking controllers that utilize different strategies to detach the apple. The first controller keeps a constant tension in the stem while moving perpendicularly to the measured force in order to replicate a pendulum-like motion. The second uses a simplex search to seek out the stiffest direction, attempting to minimize plant damage by minimizing plant deflection. To evaluate performance, we conducted picking trials on a modular orchard testbed and performed a logistic regression to evaluate the effect of controller and plant properties on the rate of spur break events. We found that plant and fruit growth angles strongly influenced the observed rate of spur breaks. Compared to a benchmark controller, the stiffness seeking controller reduced the rate of spur breaks from an overall rate of 44.7% to 12.1%. The stiffness seeking controller also had the fastest separation rate with an average picking time of 1.00 s, compared to 2.34 s for the constant tension controller. During outdoor field experiments, preliminary results showed that controller performance in a commercial apple orchard was similar to what was observed in the lab on a physical proxy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The picking action used by an apple harvesting robot has a critical impact on post-harvest fruit quality and long-term tree health. A poorly executed pick can lead to excessive fruit bruising, stem pulls, and limb breaks. The prior work has typically considered the picking phase an open loop motion control problem, using patterns such as linear pull, inclined pull, pull-twist, etc. In this paper, we consider picking an interaction control problem whereby force feedback is used to guide robot motion. We designed two picking controllers that utilize different strategies to detach the apple. The first controller keeps a constant tension in the stem while moving perpendicularly to the measured force in order to replicate a pendulum-like motion. The second uses a simplex search to seek out the stiffest direction, attempting to minimize plant damage by minimizing plant deflection. To evaluate performance, we conducted picking trials on a modular orchard testbed and performed a logistic regression to evaluate the effect of controller and plant properties on the rate of spur break events. We found that plant and fruit growth angles strongly influenced the observed rate of spur breaks. Compared to a benchmark controller, the stiffness seeking controller reduced the rate of spur breaks from an overall rate of 44.7% to 12.1%. The stiffness seeking controller also had the fastest separation rate with an average picking time of 1.00 s, compared to 2.34 s for the constant tension controller. During outdoor field experiments, preliminary results showed that controller performance in a commercial apple orchard was similar to what was observed in the lab on a physical proxy.
3.
Abhijin Adiga; Ayush Chopra; Mandy L. Wilson; S. S. Ravi; Dawen Xie; Samarth Swarup; Bryan L. Lewis; Andrew Scott Warren; John Barnes; Ramesh Raskar; Madhav V. Marathe
A high-resolution, US-scale digital similar of interacting livestock, wild birds, and human ecosystems for multihost epidemic spread Journal Article
In: Proceedings of the National Academy of Sciences, vol. 123, no. 22, pp. e2507074123, 2026.
Abstract | Links | BibTeX | Tags:
@article{adiga_high-resolution_2026,
title = {A high-resolution, US-scale digital similar of interacting livestock, wild birds, and human ecosystems for multihost epidemic spread},
author = {Abhijin Adiga and Ayush Chopra and Mandy L. Wilson and S. S. Ravi and Dawen Xie and Samarth Swarup and Bryan L. Lewis and Andrew Scott Warren and John Barnes and Ramesh Raskar and Madhav V. Marathe},
url = {https://www.pnas.org/doi/abs/10.1073/pnas.2507074123},
doi = {10.1073/pnas.2507074123},
year = {2026},
date = {2026-06-01},
urldate = {2026-06-01},
journal = {Proceedings of the National Academy of Sciences},
volume = {123},
number = {22},
pages = {e2507074123},
publisher = {Proceedings of the National Academy of Sciences},
abstract = {One Health issues, such as the spread of highly pathogenic avian influenza, present unique challenges at the human\textendashanimal\textendashenvironmental interface. Ongoing H5N1 outbreaks underscore the urgent need for comprehensive modeling efforts that capture the complex interactions between various entities in these interconnected ecosystems. To support such efforts, we develop a methodology to construct a realistic spatiotemporal gridded digital similar of livestock production and processing, human population, and wild birds for the contiguous United States. It involves multiscale and multisource data fusion and synthesis using statistical and optimization techniques, followed by verification and validation. This framework, called FIELD, consists of multiple layers and sublayers. It includes farm-level representations of four major livestock types\textemdashcattle, poultry, swine, and sheep\textemdashwith further categorization into commodities such as dairy cows, beef cows, chickens, and turkeys. Abundance data for relevant wild bird species are included. Gridded distributions of the human population, with demographic and occupational features, capture the agricultural workers and the general population. We apply FIELD to evaluate the evolving incidence likelihood risk to dairy and poultry operations and validate these results using historical incidences and phylogenetic analysis. The resulting commodity-specific spatiotemporal risk maps identify high-risk hotspots, enabling prioritization of surveillance efforts. While regional infection presence significantly enhances outbreak risk, the models reveal substantial differences in spread dynamics across poultry and dairy cattle. Furthermore, the colocation of these high-risk agricultural areas with dense human populations suggests heightened potential for zoonotic spillover and underscores the need for targeted surveillance in these coupled socioecological systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
One Health issues, such as the spread of highly pathogenic avian influenza, present unique challenges at the human–animal–environmental interface. Ongoing H5N1 outbreaks underscore the urgent need for comprehensive modeling efforts that capture the complex interactions between various entities in these interconnected ecosystems. To support such efforts, we develop a methodology to construct a realistic spatiotemporal gridded digital similar of livestock production and processing, human population, and wild birds for the contiguous United States. It involves multiscale and multisource data fusion and synthesis using statistical and optimization techniques, followed by verification and validation. This framework, called FIELD, consists of multiple layers and sublayers. It includes farm-level representations of four major livestock types—cattle, poultry, swine, and sheep—with further categorization into commodities such as dairy cows, beef cows, chickens, and turkeys. Abundance data for relevant wild bird species are included. Gridded distributions of the human population, with demographic and occupational features, capture the agricultural workers and the general population. We apply FIELD to evaluate the evolving incidence likelihood risk to dairy and poultry operations and validate these results using historical incidences and phylogenetic analysis. The resulting commodity-specific spatiotemporal risk maps identify high-risk hotspots, enabling prioritization of surveillance efforts. While regional infection presence significantly enhances outbreak risk, the models reveal substantial differences in spread dynamics across poultry and dairy cattle. Furthermore, the colocation of these high-risk agricultural areas with dense human populations suggests heightened potential for zoonotic spillover and underscores the need for targeted surveillance in these coupled socioecological systems.
4.
Dmitri A. Kalashnikov; John T. Abatzoglou; Alyssa M. Stansfield
Contribution of Tropical Cyclones to Hourly Precipitation Extremes in the Contiguous United States Journal Article
In: 2026.
Abstract | Links | BibTeX | Tags:
@article{kalashnikov_contribution_2026,
title = {Contribution of Tropical Cyclones to Hourly Precipitation Extremes in the Contiguous United States},
author = {Dmitri A. Kalashnikov and John T. Abatzoglou and Alyssa M. Stansfield},
url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2026GL122815},
doi = {10.1029/2026GL122815},
year = {2026},
date = {2026-06-01},
urldate = {2026-06-01},
abstract = {Tropical cyclones (TCs) contributed to extreme hourly rainfall at 76% of stations in the contiguous United States since 1980
Local Atlantic TCs contribute to hourly extremes in eastern CONUS, whi...},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Tropical cyclones (TCs) contributed to extreme hourly rainfall at 76% of stations in the contiguous United States since 1980
Local Atlantic TCs contribute to hourly extremes in eastern CONUS, whi...
Local Atlantic TCs contribute to hourly extremes in eastern CONUS, whi...
5.
Dmitri A. Kalashnikov; John T. Abatzoglou; Emily L. Williams; Cong Yin; Madhulika Gurazada; Mukesh Kumar; Ashwin P. Thomas; Precious E. Ebiendele
Heatwaves enable wildfire activity in the western United States Journal Article
In: Science Advances, vol. 12, no. 25, pp. eaea1277, 2026.
Abstract | Links | BibTeX | Tags:
@article{kalashnikov_heatwaves_2026,
title = {Heatwaves enable wildfire activity in the western United States},
author = {Dmitri A. Kalashnikov and John T. Abatzoglou and Emily L. Williams and Cong Yin and Madhulika Gurazada and Mukesh Kumar and Ashwin P. Thomas and Precious E. Ebiendele},
url = {https://www.science.org/doi/10.1126/sciadv.aea1277},
doi = {10.1126/sciadv.aea1277},
year = {2026},
date = {2026-06-01},
urldate = {2026-06-01},
journal = {Science Advances},
volume = {12},
number = {25},
pages = {eaea1277},
publisher = {American Association for the Advancement of Science},
abstract = {While overall impacts of heatwaves have been extensively studied, the connection between heatwaves and wildfire activity remains relatively underexplored. We analyze links between heatwaves and both wildfire occurrence and growth across the western United States (WUS) and find that 42% of burned area during 2001\textendash2024 occurred during and immediately following heatwaves. Heatwaves facilitate significant increases in daily burned area through meteorological and fuel flammability conditions that promote new ignitions and exacerbate ongoing fire activity, with effects persisting post-heatwave in most regions. In addition, heatwaves co-occur with increased cloud-to-ground lightning that can potentially increase ignitions. Last, we observe a 2.5-fold increase in burned area in WUS forests since 2001, with textasciitilde64% of this increase coinciding with heatwaves, but without corresponding increases in nonforests. The growing influence of heatwaves in shaping burned area in WUS forests has important implications for fire management and public health and can improve predictions of wildfire risk.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
While overall impacts of heatwaves have been extensively studied, the connection between heatwaves and wildfire activity remains relatively underexplored. We analyze links between heatwaves and both wildfire occurrence and growth across the western United States (WUS) and find that 42% of burned area during 2001–2024 occurred during and immediately following heatwaves. Heatwaves facilitate significant increases in daily burned area through meteorological and fuel flammability conditions that promote new ignitions and exacerbate ongoing fire activity, with effects persisting post-heatwave in most regions. In addition, heatwaves co-occur with increased cloud-to-ground lightning that can potentially increase ignitions. Last, we observe a 2.5-fold increase in burned area in WUS forests since 2001, with textasciitilde64% of this increase coinciding with heatwaves, but without corresponding increases in nonforests. The growing influence of heatwaves in shaping burned area in WUS forests has important implications for fire management and public health and can improve predictions of wildfire risk.
6.
William Solow; Paola Pesantez-Cabrera; Markus Keller; Lav Khot; Sandhya Saisubramanian; Alan Fern
A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning Miscellaneous
2026, (arXiv:2603.15411 [cs.AI]).
Abstract | Links | BibTeX | Tags: Computer Science - Artificial Intelligence, Computer Science - Machine Learning
@misc{solow_hybrid_2026,
title = {A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning},
author = {William Solow and Paola Pesantez-Cabrera and Markus Keller and Lav Khot and Sandhya Saisubramanian and Alan Fern},
url = {http://arxiv.org/abs/2603.15411},
doi = {10.48550/arXiv.2603.15411},
year = {2026},
date = {2026-05-01},
urldate = {2026-05-01},
publisher = {arXiv},
abstract = {Accurate prediction of crop states (e.g., phenology stages and cold hardiness) is essential for timely farm management decisions such as irrigation, fertilization, and canopy management to optimize crop yield and quality. While traditional biophysical models can be used for season-long predictions, they lack the precision required for site-specific management. Deep learning methods are a compelling alternative, but can produce biologically unrealistic predictions and require large-scale data. We propose a textbackslashemphhybrid modeling approach that uses a neural network to parameterize a differentiable biophysical model and leverages multi-task learning for efficient data sharing across crop cultivars in data limited settings. By predicting the textbackslashemphparameters of the biophysical model, our approach improves the prediction accuracy while preserving biological realism. Empirical evaluation using real-world and synthetic datasets demonstrates that our method improves prediction accuracy by 60textbackslash% for phenology and 40textbackslash% for cold hardiness compared to deployed biophysical models.},
note = {arXiv:2603.15411 [cs.AI]},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
pubstate = {published},
tppubtype = {misc}
}
Accurate prediction of crop states (e.g., phenology stages and cold hardiness) is essential for timely farm management decisions such as irrigation, fertilization, and canopy management to optimize crop yield and quality. While traditional biophysical models can be used for season-long predictions, they lack the precision required for site-specific management. Deep learning methods are a compelling alternative, but can produce biologically unrealistic predictions and require large-scale data. We propose a textbackslashemphhybrid modeling approach that uses a neural network to parameterize a differentiable biophysical model and leverages multi-task learning for efficient data sharing across crop cultivars in data limited settings. By predicting the textbackslashemphparameters of the biophysical model, our approach improves the prediction accuracy while preserving biological realism. Empirical evaluation using real-world and synthetic datasets demonstrates that our method improves prediction accuracy by 60textbackslash% for phenology and 40textbackslash% for cold hardiness compared to deployed biophysical models.
7.
Rudrajit Choudhuri; Christopher A. Sanchez; Margaret Burnett; Anita Sarma
Thinking Less, Trusting More: GenAI’s Impacts on Students’ Cognitive Habits Miscellaneous
2026.
Abstract | Links | BibTeX | Tags:
@misc{choudhuri_thinking_2026,
title = {Thinking Less, Trusting More: GenAI’s Impacts on Students’ Cognitive Habits},
author = {Rudrajit Choudhuri and Christopher A. Sanchez and Margaret Burnett and Anita Sarma},
url = {https://papers.ssrn.com/abstract=6711809},
doi = {10.2139/ssrn.6711809},
year = {2026},
date = {2026-05-01},
urldate = {2026-05-01},
publisher = {Social Science Research Network},
address = {Rochester, NY},
abstract = {Context: Many students now use generative AI (genAI) in their coursework, yet its effects ontheir intellectual development remain poorly understood. While prior work has investigatedstudents’ cognitive offloading during episodic interactions, it remains unclear whether usinggenAI routinely is tied to more fundamental shifts in students’ thinking habits.Objectives: To explore this possibility, we investigate (RQ1-How): how students’ trust in androutine use of genAI affect their cognitive engagement\textemdashspecifically, reflection, the need forunderstanding, and critical thinking in STEM coursework. Further, we investigate (RQ2-Who):which students are particularly vulnerable to these cognitive disengagement effects.Methods: We drew on dual-process theory, cognitive offloading, and the automation biasliterature to develop a statistical model explaining how and to what extent students’ trust-driven routine use of genAI affected their cognitive engagement habits in STEM coursework, and how these effects differed across students’ diverse cognitive styles. We empirically evaluated this model using Partial Least Squares Structural Equation Modeling on survey data from 299 STEM students across five North American universities.Results: Students who trusted and routinely used genAI reported significantly lower cognitiveengagement. Unexpectedly, students with higher technophilic motivations, risk tolerance, andcomputer self-efficacy\textemdashtraits often celebrated in STEM\textemdashwere more prone to these effects.Interestingly, students’ prior experience with genAI or academia did not protect them fromcognitively disengaging.Conclusion: Our findings suggest a potential cognitive debt cycle in which routine genAIuse progressively weakens students’ intellectual habits, potentially driving over-reliance andescalating usage.This poses critical challenges for curricula and genAI system design, requiring interventions that actively support cognitive engagement.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Context: Many students now use generative AI (genAI) in their coursework, yet its effects ontheir intellectual development remain poorly understood. While prior work has investigatedstudents’ cognitive offloading during episodic interactions, it remains unclear whether usinggenAI routinely is tied to more fundamental shifts in students’ thinking habits.Objectives: To explore this possibility, we investigate (RQ1-How): how students’ trust in androutine use of genAI affect their cognitive engagement—specifically, reflection, the need forunderstanding, and critical thinking in STEM coursework. Further, we investigate (RQ2-Who):which students are particularly vulnerable to these cognitive disengagement effects.Methods: We drew on dual-process theory, cognitive offloading, and the automation biasliterature to develop a statistical model explaining how and to what extent students’ trust-driven routine use of genAI affected their cognitive engagement habits in STEM coursework, and how these effects differed across students’ diverse cognitive styles. We empirically evaluated this model using Partial Least Squares Structural Equation Modeling on survey data from 299 STEM students across five North American universities.Results: Students who trusted and routinely used genAI reported significantly lower cognitiveengagement. Unexpectedly, students with higher technophilic motivations, risk tolerance, andcomputer self-efficacy—traits often celebrated in STEM—were more prone to these effects.Interestingly, students’ prior experience with genAI or academia did not protect them fromcognitively disengaging.Conclusion: Our findings suggest a potential cognitive debt cycle in which routine genAIuse progressively weakens students’ intellectual habits, potentially driving over-reliance andescalating usage.This poses critical challenges for curricula and genAI system design, requiring interventions that actively support cognitive engagement.
8.
Nibir Chandra Mandal; Oishee Bintey Hoque; Mandy Wilson; Samarth Swarup; Sayjro Nouwakpo; Abhijin Adiga; Madhav Marathe
IRRISIGHT: A Large-Scale Multimodal Dataset and Scalable Pipeline to Address Irrigation and Water Management in Agriculture Journal Article
In: Advances in Neural Information Processing Systems, vol. 38, 2026.
@article{mandal_irrisight_2026,
title = {IRRISIGHT: A Large-Scale Multimodal Dataset and Scalable Pipeline to Address Irrigation and Water Management in Agriculture},
author = {Nibir Chandra Mandal and Oishee Bintey Hoque and Mandy Wilson and Samarth Swarup and Sayjro Nouwakpo and Abhijin Adiga and Madhav Marathe},
url = {https://proceedings.neurips.cc/paper_files/paper/2025/hash/0821a1413339bf79ba01876783d95c53-Abstract-Datasets_and_Benchmarks_Track.html},
year = {2026},
date = {2026-04-01},
urldate = {2026-04-01},
journal = {Advances in Neural Information Processing Systems},
volume = {38},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
9.
Deanna Flynn; Abhinav Jain; Heather Knight; Cristina G. Wilson; Cindy Grimm
Uncovering Implementable Dormant Pruning Decisions from Three Different Stakeholder Perspectives Journal Article
In: 2026.
Abstract | Links | BibTeX | Tags: AI, Labor
@article{flynn_uncovering_2026,
title = {Uncovering Implementable Dormant Pruning Decisions from Three Different Stakeholder Perspectives},
author = {Deanna Flynn and Abhinav Jain and Heather Knight and Cristina G. Wilson and Cindy Grimm},
url = {https://journals.ashs.org/view/journals/horttech/36/2/article-p325.xml},
doi = {10.21273/HORTTECH05817-25},
year = {2026},
date = {2026-04-01},
urldate = {2026-04-01},
chapter = {HortTechnology},
abstract = {Uncovering Implementable Dormant Pruning Decisions from Three Different Stakeholder Perspectives},
keywords = {AI, Labor},
pubstate = {published},
tppubtype = {article}
}
Uncovering Implementable Dormant Pruning Decisions from Three Different Stakeholder Perspectives
10.
Alec Busteed; Jimena Noa-Guevara; Lais Alexandra Castro; Dahana Moz Ruiz; Sadia Afroz; Iman Mokraoui; Prisha Velhal; Patricia Morreale; Anita Sarma; Margaret Burnett
“Fast, easy, simple”? SES-diverse transfer students' sociotechnical experiences registering for classes Proceedings Article
In: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, pp. 1–23, Association for Computing Machinery, New York, NY, USA, 2026, ISBN: 979-8-4007-2278-3.
Abstract | Links | BibTeX | Tags: AI, Humans
@inproceedings{busteed_fast_2026,
title = {“Fast, easy, simple”? SES-diverse transfer students\' sociotechnical experiences registering for classes},
author = {Alec Busteed and Jimena Noa-Guevara and Lais Alexandra Castro and Dahana Moz Ruiz and Sadia Afroz and Iman Mokraoui and Prisha Velhal and Patricia Morreale and Anita Sarma and Margaret Burnett},
url = {https://dl.acm.org/doi/10.1145/3772318.3791127},
doi = {10.1145/3772318.3791127},
isbn = {979-8-4007-2278-3},
year = {2026},
date = {2026-04-01},
urldate = {2026-04-01},
booktitle = {Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems},
pages = {1\textendash23},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {CHI \'26},
abstract = {Recruiting, retaining, and educating students in computing is a frequent research topic in CHI. However, students’ sociotechnical experiences of registering for classes are understudied\textemdashespecially those of socioeconomic-diverse students. These experiences matter: research shows that registration problems bring long-term consequences to student successes. We investigate students’ socioeconomic status (SES) impact on registration experiences through three studies: a case study with education professionals using an emerging analytic method, SocioeconomicMag (SESMag); interviews with faculty/staff/students from 8 universities; and observations of 14 SES-diverse students registering for classes. Results showed: (1) 5 SES-inclusivity bugs which arose 30 times, 72% more often by lower-SES students than by higher-SES students. (2) 6/7 lower-SES students (but only 2/7 higher-SES students) expected downstream problems from the registration issues. (3) The risk-to-negative-outcomes rate was 3 times higher for lower-SES students. (4) The issues generalized across 8 universities and potentially to \>700 other universities who use the same registration portal.},
keywords = {AI, Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Recruiting, retaining, and educating students in computing is a frequent research topic in CHI. However, students’ sociotechnical experiences of registering for classes are understudied—especially those of socioeconomic-diverse students. These experiences matter: research shows that registration problems bring long-term consequences to student successes. We investigate students’ socioeconomic status (SES) impact on registration experiences through three studies: a case study with education professionals using an emerging analytic method, SocioeconomicMag (SESMag); interviews with faculty/staff/students from 8 universities; and observations of 14 SES-diverse students registering for classes. Results showed: (1) 5 SES-inclusivity bugs which arose 30 times, 72% more often by lower-SES students than by higher-SES students. (2) 6/7 lower-SES students (but only 2/7 higher-SES students) expected downstream problems from the registration issues. (3) The risk-to-negative-outcomes rate was 3 times higher for lower-SES students. (4) The issues generalized across 8 universities and potentially to >700 other universities who use the same registration portal.
