Research
Three areas, one question.
Everything we study is a version of the same question: what does it mean for an AI system to act with care — with empathy, competence, and appropriateness?
Knowledge Management and AI
What does an organization know, and what does it lose in the act of deciding? We study the knowledge that coordination produces and then discards — the concerns raised and set aside, the expert judgment that never gets written down.
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Knowledge management; human–AI innovation
Preserving Disagreement in Human–AI Innovation: An LLM Multi-Agent Replay Study
Minority concerns that consensus discards validate at nearly the same rate as the ones it keeps.
Multi-agent systems are converging on consensus rules to decide what to act on, and everything outside the majority is thrown away. If dismissed concerns turn out to carry as much downstream-validated knowledge as adopted ones, then consensus is not a filter for quality — it is a lossy compression of what the system knew.
Accepted · OUI 2026
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Knowledge management; organizational learning
Disagreement as Signal: Externalizing Tacit Knowledge through Ambient LLM Probing
An ambient LLM that probes experts the moment they disagree, turning tacit judgment into reusable knowledge.
Organizations lose expert judgment because the moments that contain it are never written down, and asking experts to state their general rules after the fact produces platitudes. Catching the reasoning while a concrete decision is still live is the difference between a knowledge base of maxims and one of usable, scoped judgments.
Proof of concept · gold labeling and evaluation pending
Agent to Agent
How do agents choose, evaluate, and address one another — and the people they work alongside? We study partner selection, the biases hiding inside agent evaluation, and when an artificial interlocutor should respond with empathy at all.
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Agentic AI; multi-agent collaboration
Not Birds of a Feather: Personality-Based Partner Selection in LLM Agents
With capability held constant, LLM hosts choose complementary personalities — not partners like themselves.
As multi-agent systems become composable, agents will increasingly pick their own collaborators. If those choices track persona rather than capability, endogenous selection quietly produces systematic exclusion — some agent profiles are never chosen for any task, and no one designed that outcome.
Analysis complete · drafting
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Agentic AI; agent evaluation and partner selection
Order Effects When Agents Choose Collaborators
Counterbalancing yields an unbiased average, yet each individual agent choice still moves with interview order.
Agent evaluation borrows its methods from human experiments, where counterbalancing is treated as a solved problem. If a balanced design produces a clean aggregate while every individual decision remains position-dependent, then deployments that make one choice at a time inherit a bias the study design was supposed to have removed.
Two studies complete · drafting
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Human–AI communication; empathic AI
Should Machines Be Empathic? An Integrative Review of Empathy Appropriateness in Large Language Models
Empathy is appropriate when it preserves the user's primary goal, and costly when it displaces it.
The debate over empathic LLMs has been stuck on capability — whether a model can produce empathy that reads as genuine. That question is now largely settled and the wrong one to keep asking. What designers actually need is a principle for when empathy helps and when it quietly redirects a user away from what they came to do.
Conceptual paper · propositions developed
AI and Education
How is AI actually governed where students meet it? Not in central policy documents, but in one syllabus, for one course, written by one instructor.