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  • 1.  Operational Use of AI

    Posted 20 days ago

    Hello Everyone.  My name is Len Niebo, EdD and I am the CIO at The Citadel Military College in Charleston, SC.

    I just completed my doctoral degree with a dissertation that focused on AI adoption in public higher education institutions in South Carolina.

    I am looking for feedback on my research to see if the findings here in South Carolina are similar or divergent in other regions or nationally.  

    I would appreciate feedback on the following two research questions that I have revised to make them less academic and more operational in nature:

    • What does it take for an institution to commit to AI in its day-to-day operations - and what gets in the way?
    • What are the real barriers - budget, culture, policy, or competition - that determine whether an institution moves forward with AI or stays on the sidelines?

    Thank you in advance for your responses.

    Len Niebo, EdD
    Chief Information Officer (CIO)
    Information Technology Services
    Bond Hall
    The Citadel
    p: (843) 953-4357

    e: LNiebo@citadel.edu



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    Leonard Niebo
    Chief Information Officer (CIO)
    The Citadel, The Military College of South Carolina
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  • 2.  RE: Operational Use of AI

    Posted 19 days ago

    Thanks for starting this conversation, Leonard. Below are my thoughts. For context, I lead AI efforts at Metropolitan State University of Denver, a regional comprehensive, mostly-open access university in downtown Denver with roughly 17,000 students. 

    • What does it take for an institution to commit to AI in its day-to-day operations - and what gets in the way?
      • Coordination, money, and buy-in from key leaders including COO, CFO, and president. But perhaps most importantly, good documentation. We've found that our initiatives related to training for employees have been effective and there is a significant amount of engagement, particularly on the staff side (faculty are a bit more apprehensive, as a whole), and the "wins" are coming from demonstration of clear use cases and clarity around data usage. However, when building and deploying enterprise-wide agentic AI solutions, we can only move as fast as the accuracy of the data (qualitative and qualitative) we are feeding into "the machine." For example, we have a agentic AI tool for our budget managers that has been trained on documentation managed by our Controller's Office. For compliance reasons, the training info is accurate and consistently maintained. Finding the next use case has proven to be a tad more complicated, however, because the training information in other units is not necessarily updated and/or organized in a way that allows for deployment of agentic AI; it's unstructured or there are discrepancies across knowledge sources, making it difficult for the language models to function well and produce the value that outweighs the compute costs. 
    • What are the real barriers - budget, culture, policy, or competition - that determine whether an institution moves forward with AI or stays on the sidelines?
      • It's culture. Other things, too, but culture is the biggest one. Uncertainty is exhausting for many and adapting is hard. And people like the things they've been using -processes, tools, etc.- and feel comfortable with them so nudging them towards something new or encouraging them to think outside the box is a difficult task. But faculty are the most powerful body on most campuses and when those with influence voice their concerns about things like environmental impact or academic integrity, others hear that and often step away from making changes that would improve teaching and learning, offload the administrative tasks associated with being a college professor (e.g., assignment writing, syllabus development); the results is a circling of the wagons. 
        • We're making progress, but I've realized I have to leverage my capital as a tenured professor who's been at MSU Denver for 15 years (the last 4 in administration) and also have to get the right people "on the bus" including our Faculty Senate president and leadership from our Council of Chairs. 

    Hope this is of some value.

    Sam



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    Samuel Jay, Ph.D.
    Executive Director, AI Strategy & Digital Learning
    Metropolitan State University of Denver
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  • 3.  RE: Operational Use of AI

    Posted 19 days ago

    Len,

    First off, congratulations on completing your doctoral!! That's awesome!!

    I just successfully defended my dissertation for my Doctor of Public Administration. I work in higher ed risk management so my dissertation focused on that. 

    Working in a smaller public university what I have found in my experience and those who I interviewed, budget is always a big factor. The other is reviewing legacy software systems. Its common that over time colleges and universities often have multiple software systems around campus purchased by various department for various needs. However, as you can imagine often this results in duplicate systems, each offering similar but also specific needs by department. And of course over time some systems become obsolete, or the continued subscription price becomes unsustainable. 

    So in turn a full campus review of systems needs to take place, typically this process starts through the purchasing department in conjunction with technology services. Most current software platforms are encouraging and incorporating AI work agents at an additional cost. Most departments on the enterprise side of the house are on board with these work agents due to limited staffing across departments. Of course there will be some that are resistant to change, but across the board it's typically seen as helping with workload. 

    We've recently worked on an AI governance policy for the enterprise side of the house, separate from the the academic policy. This is a crucial first step before purchasing or allowing the implementation of AI work agents or other AI assistance. The next step is reviewing our software needs and what AI assistance makes the most sense from a purchasing and budget stand point.



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    Mike Crutchfield, DPA
    Director of Risk Management
    Missouri Western State University
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  • 4.  RE: Operational Use of AI

    Posted 18 days ago

    Hi Leonard:

    good questions.  I would pause first and ask...

    how do you define the use of AI in operations?  what does that look like?  for example, does that mean replacing admission officers with AI agents to answer questions and read applications?  does that mean using AI as a way to assess student success?  

    your question is a good one but i feel like it needs to be built out more...

    to your second question, what are the barriers to move forward with using AI for what?  to do what? to solve what problem?  make what process better?  

    again, a good question but it feels to me like it needs to be built out more...

    John 



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    John Haller
    Higher Education Professor and Consultant
    University of Miami
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