Three threads, one question.

When and how should language models express, recognize, or withhold empathy? We study the signals, costs, and pedagogy of affective AI — always through the lens of what genuine care looks like.

• Empathy Appropriateness

  A signal-cost framework for when and how LLMs should express empathy.

  Empathic LLMs can comfort users or quietly override their judgment, and current safety frameworks have no principled way to distinguish the two. A signal-cost account specifies when expressing empathy is appropriate and when it imposes costs on the recipient — directly informing how dialogue agents should be tuned.

  Data collection · Phase 2 analysis

• Teaching Empathy in AI Design

  ELM-based pedagogy for teaching empathy in AI design.

  Most AI curricula treat empathy as a UX feature rather than a design competency. Grounding pedagogy in the Elaboration Likelihood Model trains designers to reason about *when* empathic AI is appropriate — a skill that becomes consequential as conversational systems mediate health, education, and care.

  Revising · re-targeting 10th EduTeach

• Asymmetric Calibration of Social Support

  7,959 Reddit dyads reveal how support-givers and seekers miscalibrate. ACII 2026.

  LLMs are evaluated against a single "support" benchmark, but real support is a dyad — what a seeker wants and what a giver offers rarely line up. Mapping that asymmetry at scale shows where current empathy benchmarks systematically miss, and gives the field a measurable target for calibration.

  Data analysis · LLM scoring in progress

• Empathy × Expertise at Scale

  Extended replication of Haas (2015) across 8.3M medical Q&A pairs.

  A foundational finding on how expertise shapes empathic communication has anchored a decade of theory but rested on a small sample. Replicating it at 8.3M-pair scale, with a decision-under-stress reframing, tests whether the effect holds — and clarifies *why* expertise should matter for empathy.

  Reframing · decision-under-stress angle; writing not yet started

How do agents choose partners, share knowledge, and build trust in open ecosystems? We see coordination as a form of care — agents looking out for one another.

• When Agents Interact

  When and how LLM agents engage with one another in open ecosystems.

  As multi-agent systems become composable, the open question shifts from "can agents cooperate" to "when should they engage at all." A principled account of partner choice, timing, and withdrawal is foundational for ecosystems where attention and compute are real costs.

  Literature review · drafting

• CDMK · Coordination Artifacts as Meta-Knowledge

  Multi-agent coordination artifacts as a form of organizational meta-knowledge.

  As organizations deploy fleets of LLM agents, the prompts, tools, and protocols that coordinate them become a new layer of organizational knowledge. CDMK theory gives managers a way to govern and reuse this layer instead of rebuilding it project by project.

  Self-review · approaching submission

How is AI reshaping the spaces where people help each other? We investigate how care, knowledge-sharing, and community survive — and change — in the age of AI.

• AI in the Syllabus

  How GenAI policy shows up in university syllabi (240 UofT courses and counting).

  Universities are setting de facto GenAI policy not in central documents but in individual syllabi, and almost no one is tracking it. Building the first systematic corpus of how instructors actually govern AI use gives administrators evidence rather than anecdote when policy is revised.

  Data collection · sourcing syllabi

• Parenting, Personality & Adolescent Mental Health

  Parenting styles × personality × adolescent mental health in 221 Toronto dyads.

  Adolescent mental health is now a population-level concern, yet parenting-style research has largely stalled at main effects. Testing parenting × personality interactions moves the field toward context-sensitive intervention design rather than one-size-fits-all advice.

  Writing · revising draft

• Who Still Uses Reddit?

  Sustainability of healthcare Q&A communities (r/AskDocs) in the LLM era.

  LLMs are absorbing the medical Q&A traffic that once sustained communities like r/AskDocs, and if those communities collapse so does a public archive of lay-medical knowledge. The project specifies how to measure community sustainability under LLM substitution and which design levers might keep these communities viable.

  Framework complete · awaiting data collection