Pillar 5Enablement Produces: Stakeholder Engagement Plan
AI Engagement & Collaboration
Make AI something the campus does with people, not to them.
Aligns withOECD AI PrinciplesNIST · Govern
The idea
This pillar emphasizes building a vibrant, inclusive AI ecosystem where students, faculty, and staff actively engage with AI technologies in ways that advance the institution's mission. It moves beyond mere tool adoption to cultivate meaningful collaboration, shared learning, and strategic partnerships, both internal and external, that strengthen teaching, research, operations, and community impact.
This pillar addresses how the institution builds shared understanding and participation: stakeholder engagement, communities of practice, and the shared-governance coordination that higher education requires. It addresses a common cause of adoption failure, in which a technically sound AI strategy does not succeed because faculty, staff, and students received conclusions rather than participating in forming them.
To make engagement concrete, this pillar specifies structured participation channels (senate consultation, student input, and staff forums) that inform actual decisions, and at least one community of practice in which effective approaches are shared. It draws on the AI literacy and role-based competency built under Campus Readiness (Pillar 6), so that participants engage from knowledge rather than apprehension. The relevant measure is not whether a forum was held but whether stakeholder input demonstrably affected a decision.
Engagement also extends outward, to the partnerships, external collaborations, and community relationships through which the institution's AI capacity grows. This work is coordinated with the capability-building of Campus Readiness (Pillar 6) and the ownership defined in Roles & Responsibilities (Pillar 7), so that participation, proficiency, and accountability reinforce one another rather than running as isolated initiatives.
Why it matters
In higher education, legitimacy is a precondition, not a nicety, faculty can withhold the buy-in any AI initiative needs to hold. Engagement is also how literacy spreads, how shadow AI surfaces (people tell you what they're using when they trust the conversation), and how the institution catches problems early, while they're still cheap to fix.
Engagement & collaboration
This pillar works on two fronts: how people participate in shaping AI, and how the institution partners to extend its capacity. The participation and partnership work below is what keeps AI something the campus does with people, not to them. The capability side — how proficient people actually become — is built under Campus Readiness (Pillar 6).
How people participate, and how the institution partners
Engagement and collaboration is the frame. This pillar works on how people participate in shaping AI and how the institution partners — internally and externally — to extend its reach and keep practice current.
Inclusive participation & shared governance
Standing channels for senate consultation, student input, and staff forums that feed real decisions, so people help form conclusions rather than receive them.
Communities of practice
Standing peer groups that share what's working across units, turning isolated experiments into shared institutional learning.
Internal partnerships
Coordination across the teaching center, IT, the library, HR, and student affairs so effort compounds instead of duplicating.
External partnerships
Relationships with peer institutions, consortia, vendors, and community organizations that extend capacity and keep practice current.
This is where the pillar stops being a principle and becomes work. Each practice below is something you can build, assign, and evidence, with a concrete first move, an owner, and what proof looks like. Move one practice up one level at a time.
1
Stakeholder map & engagement plan
A living map of who is affected and a plan for how each group is informed, consulted, or co-designing.
3Communities of practice exist by functional area with institutional support (time, space, sponsorship).
5CoPs feed documented patterns into guidance and training; participation is recognized in workload.
Next moves
Charter CoPs for the highest-activity areas (teaching, advising, IT, research).
Give each a sponsor, a venue, and a connection to the enablement budget.
Harvest patterns from CoPs into official guidance twice a year.
Who owns it
HR / Professional development · Functional leads
Evidence it exists
CoP rosters
Meeting cadence
Patterns adopted into guidance
In practice
Worked example
From announcement to co-design
A provost's office drafts an AI teaching policy and sends it to the senate for a vote; the senate, blindsided, tables it and trust erodes. Under this pillar the sequence inverts: a faculty community of practice surfaces real classroom dilemmas, a working group co-drafts guidance, and role-based workshops build literacy before any vote. The policy that finally moves is stronger and passes, because the people bound by it helped write it, and the engagement channel becomes the early-warning system for the next issue.
Watch for
Letting external partnerships outrun the internal engagement and shared governance that should shape them.
Consultation that gathers input after the decision is effectively made.
Ignoring shared governance until a vote forces the conversation.
Readiness & maturity questions
Readiness asks: are we prepared? Use these to surface blind spots before you build, honest “no” answers are where the work is. Representative prompts for reflection, not a scored test \u2014 for the scored version, use the Maturity Assessment.
AI Community of Practice
Has a formal AI Community of Practice (CoP) been established?
Are diverse stakeholders (faculty, staff, students) invited?
Is the CoP's purpose and scope clearly defined?
Are meeting schedules and participation expectations communicated?
Is leadership sponsoring or endorsing the CoP?
Are marginalized or underrepresented groups intentionally included?
Are there clear feedback loops to decision-makers?
Does the CoP have logistical and administrative support?
Are goals aligned with institutional AI priorities?
Are CoP activities documented and shared?
Partnerships & External Collaboration
Have we mapped existing or potential AI partners (vendors, institutions)?
Are we clear on goals and expectations for external collaborations?
Do we have guiding principles or MOUs for AI ethics in partnerships?
Are internal teams aligned before external engagements begin?
Are we engaging nonprofits, civic orgs, or diverse community partners?
Are partnership activities reviewed for mission alignment?
Is there a vetting process for AI vendors?
Do contracts include data privacy, security, and IP terms?
Are faculty and students involved in partnership formation?
Is there a contact person or office responsible for AI partnerships?