Human Factors Daily is a curated guide to recently published work across human–AI collaboration, intelligent mobility, accessibility, adaptive interfaces, and embodied AI.
We regularly scan the literature and review this collection for updates at least once a week.
Research from outside BAT LabThese papers are not BAT Lab publications. They were written by researchers outside the lab and are shared as a free resource for learning and study.
Updated July 16, 2026·8 selected papers
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New work, oriented for human factors readers.
Each entry orients human factors readers to a recent paper from the wider research community, with a source-based summary and a note on why it may matter.
Showing 8 papers
Embodied AIAdaptive Interfaces
Human-Centric Composite Field Motion Planning for Ergonomics-Aware and Demonstration-Informed Human-Robot Collaboration
Chenzui Li, Yiming Chen, Xi Wu, & Fei Chen · International Journal of Social Robotics, 18, Article 81 · Open access
External publication · not a BAT Lab paper
The authors propose a composite-field motion planner that combines task geometry learned from human demonstrations with a continuous representation of ergonomic comfort. The planner generates smooth robot motions through gradient flow and executes them with compliant control. In simulations and physical collaboration experiments, it preserved demonstrated movement patterns while reducing ergonomic strain and muscle activation, showing how embodied systems can balance task fidelity with human comfort.
Why it matters
It connects embodied intelligence with real-time, ergonomics-aware collaboration in shared work.
This study develops a transferable mental-workload model for human–machine collaboration that uses eye-tracking data to help calibrate noisy EEG signals. Dynamic graph learning, cross-modal attention, and self-supervised pretraining identify features that generalize across people. Tests on two benchmark datasets showed strong cross-subject performance, including under noisy conditions, suggesting a path toward adaptive systems that can estimate operator workload without retraining for every user.
Why it matters
Transferable workload sensing can help adaptive interfaces respond to cognitive state without retraining for every user.
An IMOI model for human-AI teams in critical care settings
Jenna Korentsides, Zander N. Miller, Elizabeth R. Merwin, Brooklyn Costinett, & Joseph R. Keebler · Human Factors in Healthcare, 9, 100137 · Open access
External publication · not a BAT Lab paper
The paper adapts the Input–Mediator–Output–Input model to describe recurring human–AI teamwork in intensive care and emergency settings. It links system and team conditions to processes such as trust and coordination, then connects those processes to team performance and patient outcomes through a feedback loop. The model is conceptual and has not been empirically validated, but it offers a structured foundation for training, interface design, and future simulation studies.
Why it matters
It offers a systems view of roles and responsibility in high-stakes human–AI teams.
Exploring the Role of Individual Characteristics in Shaping User Experience during Human–Robot Collaboration in Manufacturing Contexts
Riccardo Gervasi, Matteo Capponi, Luca Mastrogiacomo, & Fiorenzo Franceschini · Journal of Intelligent & Robotic Systems · Open access
External publication · not a BAT Lab paper
This study examines how personality and attitudes toward robots shape experiences with a collaborative robot during manufacturing assembly tasks. The researchers combined self-reported workload and affect with physiological indicators while participants completed tasks with and without robot assistance. Results suggest that individual characteristics meaningfully moderate the experience of collaboration, supporting adaptive cobot designs that account for differences among operators instead of assuming one interaction style will suit everyone.
Why it matters
The results point toward embodied systems that adapt to meaningful differences among users.
Human–AI collaboration: trade-offs between performance and preferences
Lukas W. Mayer, Sheer Karny, Jackie Ayoub, Miao Song, Danyang Tian, Ehsan Moradi-Pari, & Mark Steyvers · Cognitive Research: Principles and Implications, 11, Article 18 · Open access
External publication · not a BAT Lab paper
In two behavioral experiments, participants worked with AI agents that varied in how strongly they adapted to human actions during a shared decision task. People preferred agents that respected their intentions and allowed them to contribute meaningfully, while objective performance alone did not drive preference. The findings show that human-centered collaboration strategies can improve how AI teammates are perceived without necessarily reducing team performance.
Why it matters
The findings sharpen how role allocation can protect human agency without sacrificing team performance.
A Shared-Control Framework for A Human-Robot Front-Following Behaviour in Unknown Dynamic Environments
George Moustris & Costas Tzafestas · International Journal of Social Robotics, 18, Article 31 · Open access
External publication · not a BAT Lab paper
The authors present a mobile robot that follows a person from the front while navigating unfamiliar, obstacle-filled environments. Its shared-control framework combines local planning with intention recognition, shifting control between the person and robot when route choices become uncertain. Field trials showed highly accurate intent recognition and walking patterns closer to participants’ natural gait, while also revealing that leader and follower roles can change fluidly during assistive navigation.
Why it matters
It links assistive mobility with intent inference and responsibility handoff.
Bridging the gap: generating a design space model of socially assistive robots for older adults using participatory design methods
Adi Bulgaro, Ela Liberman-Pincu, & Tal Oron-Gilad · Universal Access in the Information Society, 25, Article 43 · Open access
External publication · not a BAT Lab paper
Using participatory design, the researchers worked with older adults and other stakeholders to identify expectations and concerns surrounding socially assistive robots. The work progressed from interviews and focus groups to direct interactions with a commercial robot, informing a model that connects robot characteristics with users’ emotional responses. The resulting process helps translate lived experience into design requirements and can be adapted to different applications and cultural settings.
Why it matters
The work demonstrates how participatory design can translate lived experience into accessible robot requirements.
Partner or burden? The dual pathways linking perceived attributes of intelligent cockpits to human–machine collaboration willingness via cognitive load
Silian Li · BMC Psychology, 14, Article 313 · Open access
External publication · not a BAT Lab paper
Using survey data from 503 licensed drivers, this study examines how perceived cockpit usefulness, entertainment value, and anthropomorphic design relate to willingness to collaborate with an intelligent vehicle system. Usefulness and entertainment were associated with lower cognitive load, which predicted stronger collaboration willingness. Anthropomorphism increased willingness through an affective pathway but did not reduce cognitive load, indicating that a socially appealing interface is not automatically easier to use in demanding driving contexts.
Why it matters
It argues for intelligent cockpit design grounded in cognitive resources rather than surface-level anthropomorphism.
These summaries are concise research orientations, not substitutes for the original papers. Follow the source links for methods, evidence, limitations, and complete author information.
