Papers
8
Total Citations
458
H-Index
5
About
Matt Beane is a leading scholar at the intersection of technology, work, and learning, with a primary focus on how robotics and artificial intelligence reshape professional expertise. His most influential work, "Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail" (253 citations), reveals how surgical trainees develop competence through informal, often hidden practices when formal training pathways prove inadequate. This ethnographic study has become foundational for understanding skill acquisition in high-stakes, technology-mediated environments. Beane's research consistently examines the material and social consequences of robotic systems, as demonstrated in "What Difference Does a Robot Make?" (140 citations), which analyzes how telepresence robots alter coordination in intensive care units. His work extends to organizational strategy, notably in "Resourcing a Technological Portfolio" (2023), which theorizes how hospitals manage aging robotic systems while maintaining clinical outcomes. Beane has also published influential commentary in *Science Robotics* and *Harvard Business Review*, warning that robotic surgery risks turning trainees into passive spectators. With over 450 total citations, his research bridges organizational behavior, medical education, and human-robot interaction, offering critical insights for designing systems that support rather than undermine human skill development.
Research Focus
Key Achievements
Top Papers
- 1Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail253 citations · 2018
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- 4In Storage, Yet on Display16 citations · 2020
- 5Robots in Group Context6 citations · 2017
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- 8Knowledge Management for Rapidly Extensible Collaborative Robots2 citations · 2019