Dean
Lead faculty hiring and tenure/promotion decisions
What You Do Today
Authorize faculty positions, guide search processes, and make or recommend tenure and promotion decisions. These are career-defining decisions for faculty and shape the college for decades.
AI That Applies
AI analyzes candidate research impact, teaching effectiveness data, and peer comparison metrics. Provides data to inform tenure discussions without replacing faculty judgment.
Technologies
How It Works
The system ingests candidate research impact as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — data to inform tenure discussions without replacing faculty judgment — surfaces in the existing workflow where the practitioner can review and act on it.
What Changes
Tenure review data becomes more comprehensive and comparative. You have better evidence to support difficult decisions.
What Stays
Making tenure decisions — betting on a scholar's future trajectory, weighing different forms of excellence, and defending decisions to disappointed candidates — requires academic wisdom.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for lead faculty hiring and tenure/promotion decisions, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long lead faculty hiring and tenure/promotion decisions takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your department chair or principal
“What's our time-to-fill for the roles that are hardest to source, and where in the funnel do we lose candidates?”
They influence which ed-tech tools get approved and funded
your instructional technologist
“How would we validate that an AI screening tool isn't introducing bias we can't see?”
They support the tech stack and can show you capabilities you don't know exist
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.