These can be moved from “derailment” to “productive” with good facilitation + concrete artifacts.

• Tool wars / constant change → solvable by going tool-agnostic
  Fix: teach workflows (brief → constraints → iterate → critique → revise) and let tools be optional. Provide 1–2 “supported tools” and a compatibility list.

• Academic integrity becoming the only topic → solvable by redesigning for process evidence
  Fix: require checkpoints (drafts, critique notes, version history), reflection prompts, in-class making, and grading that rewards decisions + revision.

• Anecdote wars → solvable by shifting to shared scenarios + protocols
  Fix: use 2–3 standardized case studies and a rubric-driven discussion (“What would we do in this scenario?”).

• Perfectionism (“if not foolproof, don’t do it”) → solvable by adopting a “risk tier” approach
  Fix: categorize uses as Low / Medium / High risk and only start with low-risk classroom practices.

• Scope creep → very solvable with tight outcomes
  Fix: define success as “everyone leaves with one implemented change” (one revised assignment, one rubric, one policy paragraph), not “solve AI.”

You can make progress, but you need an institutional lane (chair/dean/IT/union/legal).

• Policy anxiety without authority → partly solvable with a clear decision path
  Fix: create a “policy parking lot” + identify who owns what + timeline. In PD, produce department guidelines even if campus policy is pending.

• Privacy/data concerns → partly solvable with an approved-tool guidance sheet
  Fix: publish a short “What you can/can’t input” rule, a list of approved tools, and safer alternatives. PD can draft this; admin must bless it.

• Equity/access gaps → partly solvable with design choices
  Fix: tool-optional pathways, free-tier alternatives, in-class access options, ADA-friendly materials. Structural gaps still need resources.

These are values/identity-level or society-level issues. You can’t “solve” them in PD, but you can keep them from hijacking everything.

• “AI should be banned” loop → not solvable, but containable
  Move: acknowledge, then pivot to “Given access exists, what classroom practices protect learning?”

• Ethics spiral (copyright/labor/energy) substituting for pedagogy → not solvable, but bounded
  Move: set one ethics framework + one discussion window, then convert concerns into concrete practices (disclosure, citation habits, bias checks).

• Fear of replacement / identity threat → not solvable quickly
  Move: normalize the fear, then re-center on what humans do best (judgment, taste, critique, context, relationships) and build supportive practice.

“You do not have to like AI to participate and share your insights in this work.”

“Skepticism is a form of expertise. Critical voices help shape better, more ethical uses of AI.”

“Faculty decide when, how, or if AI is used. The work we do focuses on agency, not adoption.”

“Think of this as a sandbox, not a mandate. Try it once, critique it hard, keep what works, ditch the rest”

“This space welcomes curiosity, critique, and even outright dislike—what matters is thoughtful engagement.”