A critical success factor for an academic AI CoE is faculty buy-in and leadership. We introduce a Faculty Champion Ladder – a structured program to identify, develop, and recognize faculty champions of AI integration. This “ladder” provides clear rungs or levels for faculty engagement, turning enthusiasm into sustained action and building a community of practice.

Here’s how it works:

• Level 1 – AI Enthusiast: Any faculty member curious about using AI in their teaching or research can start here. We provide basic training (intro workshops on AI tools for education, ethical considerations, etc.). Enthusiasts commit to trying out at least one AI activity in their class or project. At this stage, the CoE offers resources like tutorials and a sandbox environment (e.g. access to a GPT tool via the university license) for experimentation. The idea is to lower the barrier for faculty to start playing with AI and feeling comfortable experimenting.

   

• Level 2 – AI Advocate: Faculty who have dabbled and are ready to do more become Advocates. These are the folks who perhaps piloted an AI-powered assignment or incorporated an AI tool in research and want to share their experience. We formalize their role: Advocates might lead a brown-bag session or departmental meeting to demo what they did, essentially “experiment, share, and learn” with peers. They also start mentoring Level 1 colleagues in one-on-one chats or small groups. To support them, the CoE might give priority access to advanced training or conferences. Advocates are essentially early adopters who spread awareness and knowledge.

   

• Level 3 – AI Champion: This is a select group of faculty who emerge as true leaders in AI integration on campus. Champions possibly lead official CoE initiatives – for example, heading a working group to create an AI ethics policy, or spearheading a grant proposal for an AI education project. They might co-teach an AI-focused course or guide students in AI research projects. We formally recognize Champions through titles or stipends. Importantly, we work with administration so that this activity is recognized in workload or evaluations – similar to how service or research is recognized. “It’s important that every professor feels they can experiment, and that this is recognized as part of what we value as an institution,” as one Center for Teaching and Learning director noted. Our Champion program aligns with that ethos by giving institutional weight to these efforts (e.g. letters from the Provost thanking them, or an award at year-end).

   

The “ladder” metaphor implies progression – faculty can ascend as their involvement deepens. To facilitate this, the CoE sets criteria for moving up and provides development at each stage. For instance, to progress from Advocate to Champion, a faculty might need to complete an advanced training series and lead a multi-department AI initiative. We offer that advanced series (perhaps a multi-week AI in Curriculum design course, or involvement in The Generator-style lab groups like one school did). We also pair rising Champions with established ones or external experts for mentorship.

In addition, the ladder is tied to recognition and incentives. Enthusiasts might receive a digital badge or a certificate. Advocates could get a small grant (e.g. $1,000 mini-grant to implement an AI idea in their class). Champions might get funding for attending an AI in education conference or a stipend for the extra time they invest. Recognizing faculty efforts is key – as many have noted, integrating AI often means extra initial workload for teachers on top of their busy schedules. Our program ensures these efforts are visible and celebrated (for example, featuring them in a newsletter or on the CoE website as “AI Faculty Fellows”).

By formalizing the Faculty Champion Ladder, we create a pipeline of AI leaders on campus. This bottom-up energy is crucial: faculty must be the ones to lead the transformation in education with AI. They’re the content experts and closest to student learning. Our program simply empowers and rewards them for stepping up. Over time, as more faculty climb the ladder, an organic community forms – Champions mentoring Advocates, Advocates inspiring Enthusiasts – fostering a culture where people “build communities of practice to share knowledge about using AI in teaching and research”.

Through this approach, the CoE isn’t just a top-down directive; it becomes a grassroots movement within the faculty. And it ensures that when key faculty leaders retire or move on, others are in the pipeline to take their place, sustaining the AI innovation momentum.

Year-1 Milestone Timeline

The first year of an Academic AI CoE is crucial to build credibility and momentum. We help you lay out a Year-1 milestone timeline – a month-by-month plan of what the CoE will achieve in its inaugural year. This timeline keeps the team focused and allows the rest of campus to see progress at regular intervals.

Months 1-3 (Q1): Setup and Community Building

• Kickoff & Charter Approval (Month 1): Right after launch, hold a high-profile kickoff meeting. This might be an event where the President/Principal or Dean officially announces the CoE, sharing the approved charter and mission. By the end of Month 1, the charter should be formally approved by relevant academic bodies.

   

• Faculty Champion Program Launch (Month 2): Recruit initial cohort of Level 1 Enthusiasts and Level 2 Advocates. Perhaps aim for at least one or two representatives from each department. Host the first faculty workshop on “AI in Education 101” as a foundational training. This month we also set up CoE governance meetings (Steering Committee meets for the first time to refine plans).

   

• Pilot Use-Case Identification (Month 3): Work with Champions/Advocates to pick a few pilot projects. For example, identify 3 courses for AI integration pilots (like using an AI tutor in a large freshman course, or an AI-assisted assignment in a writing class) and maybe one operational pilot (like using AI for library support or administrative process). Define what success looks like for each pilot. At the same time, establish the CoE’s online presence (website or intranet page listing resources and events).

   

Months 4-6 (Q2): Pilot Implementations and Policy Development

• Pilot Projects Execution (Months 4-5): The selected pilot courses/projects from Q1 go live. Faculty Champions get the needed tools and support (maybe a grad student or instructional designer working with them). For instance, Month 4 could see the AI tutor deployed in the freshman course and gathering data on student usage. CoE members gather feedback continuously.

   

• Policy Drafts (Month 5): As pilots progress, start drafting guidelines/policies around AI. A notable one is an Academic Integrity & AI Policy – how students can or cannot use tools like ChatGPT in assignments. Also perhaps an AI Research Ethics guideline for faculty and students. These drafts are created by a small task force (which may include some Champions, admins, and students) facilitated by the CoE.

   

• Mid-Year Showcase (Month 6): Hold a mid-year “report out” event or webinar. Pilot project leads share early results (e.g. “In our pilot, students who used the AI tutor improved quiz scores by 10% on average” or “We learned that students need clearer guidance on using AI appropriately – which fed into our draft policy.”). Also, present the draft policies to faculty for input. This showcase builds broader awareness and buy-in, and it’s a check-in to adjust course for the second half of the year.

   

Months 7-9 (Q3): Expand Reach and Solidify Practices

• Curriculum Integration Plan (Month 7): Using lessons from pilots, develop a plan to scale AI integration. For example, identify additional courses for the next academic term that will incorporate AI, or departments that want to create new AI-related electives. Essentially, move from isolated pilots to a phased rollout. If in Month 3 we had 3 pilots, perhaps plan for 10 courses in the next wave. Also, finalize the academic integrity policy by this time so it’s in place for wider use (if needed, get it passed by academic council).

   

• Student Engagement Initiatives (Month 8): To truly embed the CoE on campus, involve students directly. By Month 8, host something like a Student AI Hackathon or Competition or start an “AI Ambassador” student club with CoE support. This gives students agency and creates excitement. It’s mid academic year (if we assume a start in September, Month 8 would be April/May) – a good time to capture student interest before the year ends.

   

• Faculty Development Ongoing (Month 7-9): Continue monthly or bi-monthly workshops. Perhaps an advanced series for the initial Champions (deep dive into AI ethics, or pedagogy with AI), and introductory ones for new faculty who missed earlier sessions. Aim that by end of Q3, at least, say, 30% of faculty have attended at least one AI training – a metric to gauge reach.

   

Months 10-12 (Q4): Evaluate and Plan Forward

• Year-End Conference or Symposium (Month 10 or 11): Organize a campus (or even regional) symposium on “AI in Education and Research.” Invite faculty to present their projects, students to demo what they built, and perhaps external speakers. This positions your CoE as a thought leader and consolidates all the knowledge gained in year 1. It also serves as a celebratory milestone event. Around this time, measure the success of year 1 pilots formally (collect data, testimonials, etc.) to include in a final report.

   

• Evaluation & Future Roadmap (Month 11): The CoE team and steering committee conduct a thorough evaluation of Year 1 goals vs. outcomes. Which objectives were met? (e.g. number of courses updated, number of faculty champions active, any improvement in student outcomes or faculty sentiment). Identify shortfalls and lessons. Use this to draft a Year 2 plan – likely expanding successful pilots campus-wide, pursuing new grant funding, etc. Also, by Month 11 we should be securing any needed funding for Year 2 (if initial grants were for one year, apply for renewals or new sources now).

   

• Publish Year-1 Report (Month 12): Compile a report or at least an infographic to share with university leadership and the community. It should highlight achievements: “Trained 50 faculty, engaged 500 students, integrated AI into 15 courses, secured $X in new funding, developed 2 new policies.” Also include quotes or case studies (e.g. a professor’s testimonial: “Using AI in my class improved student engagement dramatically”). Conclude with what’s next, building excitement for the upcoming year. Finally, ensure leadership acknowledges the team – maybe an end-of-year award ceremony for Champions, etc., to maintain enthusiasm.

   

By following this timeline, the CoE avoids the pitfall of being an initiative that is announced with fanfare and then fizzles out. Instead, there are tangible outputs and events nearly every quarter, demonstrating value. The timeline also emphasizes a feedback loop – we don’t wait till year-end to adjust; the mid-year showcase and regular meetings allow iterative improvements. Each milestone is like a “mini-win” to keep stakeholders invested: early pilots success leads to policy, which leads to larger adoption, culminating in institutional impact by year’s end.

Our team will work closely with you to customize this timeline to your academic calendar and priorities. But broadly, it ensures that in the first 12 months, the CoE goes from a concept on paper (charter) to a living, breathing part of campus life with proven contributions. As a result, by the end of year one, the AI CoE is not just an idea but an established entity with faculty champions, student engagement, initial successes to point to, and a clear mandate to grow further. This creates a strong foundation for securing ongoing support (internal and external) and scaling up the initiative in subsequent years.