Most leadership development in a technology organization looks the same. A workshop, a cohort, a facilitator, a shared reading, ninety minutes on the calendar. I have run these and I have sat in them. The thing that almost never happens in that room is someone saying out loud that they do not understand the reading. Senior people especially will not do it. The cost of admitting confusion in front of your peers is higher than the cost of nodding along, so people nod along, and the conversation stays at the altitude where everyone is safe.
Over the past few weeks I have been designing a set of self-paced learning labs for Enterprise Technology. Each one pairs a foundational text with a translation into our work and a set of practice moves. The first is Wenger on communities of practice. The second is Roy Pea on distributed intelligence, which asks how intelligence travels through a system rather than sitting in any one head. The third is David Weinberger’s Small Pieces Loosely Joined, which opens on the difference between the Hoover Dam, where every dependency was planned in advance, and the Web, where almost nothing was and whose designers chose it that way. All of these readings and associated insights have been key ingredients when I have taught my Disruptive Innovations class that my good friend and colleague, Scott McDonald, and I co-designed back in my Penn State days.
The interesting part to me is that I have finally figured out a compelling use for AI within the flow of instruction that seems to work. Each lab also ships with its own UT Sage Tutor. That is the piece I want to share, because it is doing something I did not fully anticipate when I started.
The Office of Academic Technology and Enterprise Technology partnered to build Sage at UT for course tutoring. An instructor uploads their materials, Sage builds a tutor scoped to that course, it lives inside Canvas, and it teaches Socratically rather than just handing over answers. It has been available campus wide since last fall and it works really well. What I did here was point that same capability at something else entirely, which is leadership development for our own staff.
The tutors in these labs are trained on at least two things. The first is the source text, so the tutor can genuinely discuss Wenger or Pea rather than gesture at them. The second is a large document I assembled by scrubbing our own website, everything Enterprise Technology says publicly about who we are, how we are organized, what we believe, and what we run.
That second corpus is what makes it work in the context of ET. A tutor trained only on the reading can tell you what a community of practice is. A tutor that also knows our org chart, our governance bodies, our service portfolio, and our stated values can help you work out whether the thing you have been calling a community of practice at UT actually is one.
So each lab runs two scaffolds at once. There is a public one, the text and the structure and the practice moves, which everyone in the lab shares and can argue about together. And there is a private one, the tutor, where you can be exactly as confused as you actually are. No cohort, no facilitator, no assessment, nobody watching. You can ask the question you would never ask in the room. The private scaffold is not a retreat from the social one. It is what makes the social one possible, because the confusion gets worked out first, and what people carry into the group conversation is an argument instead of a nod.
I have shared this with about a dozen people so far, and everyone is blown away by the conversations they can have with the Sage Tutor. What comes back to me is not “I read the Wenger text.” It is a specific argument about whether one of our teams is a community of practice or just a reporting line. If Wenger tells us learning is social, then that is the conversation I was hoping for.
The labs are built in combination with Claude Code and are published to GitHub Pages inside our own enterprise GitHub organization, gated so that only the people we have granted access can read them. Within each learning scaffold there are one button copy starter prompts that a learner taps and is taken to Sage to start a conversation. The number of conversation screenshots I got last week was very cool.
After building the first two tutors I noticed I was repeating myself, so I built a front end plugin for Claude that walks a designer or instructor through the steps of building one. It asks for the real learning objectives before anything else, pushes for outcomes you could actually observe a person demonstrating, and keeps pulling the designer back toward grounded pedagogy before a single line goes into a prompt.
Most of us build a tutor by describing the personality we want and hoping instruction falls out of it. This works the other way around, and it turns out the instructional design is what makes the tutor good, not the prompt craft. I was surprised how easy it was, and more surprised by how repeatable.
When people ask me how AI is disrupting education, I now have a real-world example of how to move AI into the learning process rather than around it. I have known for a couple of years that using AI responsibly actually makes me understand the problems I am trying to solve in a deeper way. Why? Because learning is a social endeavor, and Sage is the closest thing I have found to a colleague who has read everything and still wants to hear what you think.
If you are a UT Austin campus member and want to try this out or talk more about these ideas, drop me a comment or hit me on Teams.
