Cohort Insight Report · Leading Teams in the AI Era
Leading Teams in the AI Era
An open badge that asked school and system leaders to name a challenge in how their teams learn, then try a leadership move against it and talk it through with peers along the way. This report reads every transcript the eight participating educators produced.
How the cohort engaged
Leading Teams in the AI Era ran as an open community badge, offered in partnership with the Association of Washington School Principals for state clock hours. Educators enrolled individually, and some members of the Mark Cuban Foundation Teacher Fellows took part alongside special education leads, district and association staff, and independent consultants. Everything below is reported in aggregate. Location was recorded for too few participants to support a geographic breakdown.
Each dot is one educator in one session, in badge order. Sessions 1, 3 and 4 were designed as 25-minute peer conversations and Session 2 as a 15-minute solo reflection, with about 90 minutes of applying a leadership move between sessions. Recorded peer conversations mostly ran 12 to 35 minutes. Eight educators started, and participation across the four sessions ran 7, 5, 6 and 4.
When educators met. The badge opened July 21 and closed September 8. Most sessions landed in August as the school year began, with a short September tail.
Word count covers participants' own speech only. Three peer pairings opened a room but were canceled before any conversation was recorded, and they are not counted. One educator completed three sessions on a single day.
All 23 Ideas were generated by the platform and written back to the educator in the second person, so none are treated here as educator voice or competency evidence. One was a system notice about missing audio. The three Small Wins are the only hand-written artifacts in the record.
Success Signs are observable evidence indicators from the AI for Deeper Learning Framework. No one selected a sign on a Small Win, so these counts come from the transcripts: distinct educators who described a sign they had already seen, or one they hoped to see, mostly in Session 3.
Every sign already observed sits with adults, which fits a badge built for leaders of adult learning. The student-facing signs are hopes for now, and they point to where the next layer of evidence would come from.
The shape of the learning
Eight educators brought a live problem into this badge, and most were already acting on it. Five described a move under way in a team or system they lead: protected learning time on team calendars, a challenge to build an AI workflow for a disliked task, shared learning before an AI policy vote, AI scenarios in leadership training, and an AI literacy course redesigned to start from mindset.
The paired format made exchange visible. Partners picked up each other's language and extended it, and one participant opened a solo reflection by restating advice just given to a peer. Growth also reads across sessions. One educator moved from naming a gap in staff AI knowledge to planning a conference where staff choose their own starting band, and closed by describing how reflection had made them a better listener.
Two places are thinner. Impact is mostly anticipated, with two participants describing a sustained change in the people they lead. Talk also stayed at the level of approach, and few walked a partner through an agenda or workflow in reusable detail. Cautious colleagues appear only as others described them, as expected in an open badge that draws people already leaning in.
they needed to hear it from their peers.
providing the autonomy to my staff team to use AI and how they see fit for a problem that they pick.
Honestly, like, during this badge, I've learned probably more about myself as a learner than a leader.
Five themes that surfaced
Where a number appears, it counts distinct educators out of the 8 with a recorded session. Counts were set by reading each transcript in context rather than by keyword, and they are floors. In a group this size one person moves a count by 12.5 points, so read the pattern before the figure.
At least five described making learning visible across a team before asking anyone to change practice. A policy committee studied AI together before drafting anything. Team calendars gained protected learning time. One educator is planning a roundtable where staff show each other the workflows they built.
At least four described adoption that starts from the educator's own problem. Staff pick a task they dislike and build a workflow for it. Educators choose the growth band they start in. Leadership training uses situations people already face. Assigned uses were described as taking choice away.
At least four connected hesitation to identity: experienced educators hearing AI as a claim about their expertise, and personal preference blurring into professional responsibility. Participants treated that caution as reasonable and looked for ways to keep judgment with the educator rather than argue people out of it.
At least five answered the third learning target as learners. One said the badge taught them more about learning than leading. Another credited self-reflection with making them a better listener. Several said they could not ask colleagues to experiment without visibly practicing first.
Student-facing practice appears mostly as intention. Three described what they want for students: AI that scaffolds without handing over answers, and real access for multilingual learners. One district had approved an AI assistant for teachers and none yet for students, which limits what a teacher leader can model.
Themes were built from 22 recorded sessions and 3 hand-written Small Wins. Transcripts were read in full, session by session, then coded and re-reviewed against the whole corpus. These are automatic voice transcriptions, and passages where a participant read the session prompt or the self-assessment aloud, a stray transcription-service tag, and recognition noise were set aside before any coding or quoting. Platform-generated Ideas were treated as already-analyzed artifacts rather than as educator voice.
What this badge was built to develop
Leading Teams in the AI Era carries three learning targets. This report reads them against three competencies from the AI for Deeper Learning Framework: Lead Teams for the first two targets, Exchange for the badge's peer design, and Why for the third target's focus on the leader's own identity as a learner. Evidence depth reflects the richness and specificity of the available record, not a performance grade or evaluation.
Leaders develop the professional capacity of school personnel through job-embedded learning.
The deepest record in the badge. Five of eight described a team learning move already in motion, and two more committed to a first step before their next session. Diagnosis was strong: at least seven located their challenge in conditions such as time, fear amid budget and staffing pressure, uneven knowledge, or tools not yet approved. Evidence of impact is earlier, with two describing a sustained change in the people they lead.
Exchange reflections, ideas, and insights about AI with others.
Observed directly in the paired transcripts. In conversations between two participants, partners built on each other's language and examples, and one reflection carried a partner conversation forward into the next session. Seven of eight valued peer exchange in their own words, several as a way out of isolation in their role. Most exchange stayed at the level of approach rather than artifacts, and one participant named that directly.
Define a personal why for AI, ground it in a clear vision for teaching and learning, articulate a stance on responsible and ethical use, and identify the uniquely human skills needed.
Present for every participant, with the learner-identity thread concentrated among the four who reached Session 4. They described themselves as learners first and named modeling as the leadership move that follows from it. The ethical stance arrived in specific forms: keeping human judgment with the educator, keeping student data out of tools, and protecting the thinking students do.
Explore the full framework map
All six strands and 33 competencies, showing where this cohort's evidence landed beyond the three competencies above.
These conversations touched competencies well outside the three the badge is read against, which is worth seeing but not worth grading the badge against. Every rating below was set by reading the full transcript corpus competency by competency, counting distinct educators with genuine evidence and setting aside passages where a participant was reading the session prompt or the self-assessment aloud.
Evidence depth reflects the richness and specificity of available evidence, not a performance grade or evaluation. Bold items mean documented evidence is present. Lighter items mean the pattern is emerging or not yet surfaced in this cohort's data. With eight participants, one person is 12.5 percent of the group, so a single educator with genuine evidence reads as two pips and a passing mention as one. The professional habits strand carries this record, as a badge for leaders of adult learning would lead you to expect, and PH3 is rated on what the paired transcripts show. Student-facing strands sit lower because participants described them mostly as intention. CT1 and LM5 are the exceptions, carried by leaders who train staff to check AI output and who led a district through writing an AI policy.
sideby's AI for Deeper Learning Framework is informed by the UNESCO AI Competency Framework for Teachers, the colleague.ai AICE Framework, Teach AI with EC + OECD & code.org frameworks for AI learning, and the Hewlett Foundation Deeper Learning Competencies, evolving as AI evolves and shifts what this technology can mean for deeper learning and education.
Where evidence is strongest, session by session
This view reads across the whole framework, so it picks up everything the cohort touched. Three peer sessions and one solo reflection make up Leading Teams in the AI Era, and each drew on a different part of the map. Evidence depth reflects the measured volume of participants' own transcript speech in each session, not a performance grade or evaluation.
Session counts come from the platform export and are distinct participants, not runs. Every recorded session produced a transcript. Three peer pairings that opened a room and were canceled before any conversation are not counted.
What got written down
Three Small Wins were written by badge enrollees during the badge window, all logged as Successes and all still private. Private means visible only to the person who wrote it; launched means shared with the community. They are summarized rather than quoted, so the wording is ours and the substance is theirs.
Written the same day as this educator's final badge session. They combined AI analytics with a simple reflective cycle (what happened, what it means, what action comes next) and found it let them isolate and improve one specific area at a time. It is the third learning target in practice: a leader's own learning routine, written down in a form a team could borrow.
A business unit was redesigned so students worked as entrepreneurs, identifying customer needs, budgeting, pitching and revising from peer feedback. The educator noticed students stop asking what the teacher wanted and start asking whether customers would buy the product. The entry does not describe AI use, and it was written before this educator enrolled in the badge.
An educator was awarded a mini grant to prototype, by spring, an AI-supported progress monitoring system for special education teams, aiming at clearer data, a lighter workload and more timely family communication. It records a plan rather than an outcome, from an enrollee who did not record a session in this badge.
All three entries are shown. Small Wins carry no badge attribution in the platform, so these were filtered by person and date. All three belong to educators who were enrolled in another sideby badge during the same weeks, and only the first can be tied to a session in this one. They appear because they are the record, not because each reflects this badge.
Cohort voice across the badge
Every line below is exactly as a participant said it in one of the four sessions, from a group that included classroom teachers, special education leads, district and association staff, and consultants. These are voice transcriptions, so the false starts and run-ons are theirs and are left as recorded.
And I'm trying to reframe it to make it more durable that like when the tools change in three months from now and that to me, I see this like the most important thing is teachers are having to rethink how they approach teaching and how they approach learning and assessment.
When it comes for to priority tasks and decision making, because of us going over hallucinations and what that looks like we're teaching our staff that hey have some type of knowledge of what it is you're researching or talking to AI about before you even start prompting it because it can spit out incorrect information that sounds factual.
And that's a mindset shift. That's a mindset approach. And that takes time. And it takes them being able to see others do this, get sparks of how it's working for them, translate that into their own context, reflect on it, what's working, what's not. And it's developing this, this new mindset approach one small step at a time.
Do we carry that same learner stance their entire life. And does that show up right now in all of these new things that people are having to learn or engage with for X number of years. That doesn't matter. So thinking about people around my office that don't do much with AI. Well, I wonder if they were just, you know, closed in a box in school like okay the teacher told me to do this. So that's what I'm going to do.
I mean, it can be so helpful because there is a lot of human error on our day to day. So having that soundboard and integrating it within a daily workflow can be really great for the people that are willing to do it.
Also bearing in mind that reflection is not an individual sport, right?
And so I it's and it's so easy for that to be the immediate response and feeling that threat and I love working with teachers to help bring the fist down. I always say it's like it's a you feel threatened you put the fists up. Whoa, whoa, whoa, you know what you're coming at me like you're coming at my expertise as an educator and that's a real, that's a big deal.
And then some of their, you know, it did shift, there was a shift in how they felt about it because, you know, we showed them the good we showed them the bad but the good to me the good outweighed the bad it's just you just need to know what it is in order to use it effectively.
Every week, regular meeting and then if we pop in some of the conversation relay with the AI and everybody sounds like, oh, they start to share their own opinion, whatever. That's another way because as I told you, the teacher is just so so so busy.
So I'm having to, like in our team meetings, actually schedule time.
So, I just, it's the intentionality of building that stuff in there, not in a way advanced level like we're not, we're not building a bot we're not doing a genetic thing we're not doing a co work thing or whatever the case may be. It's like, let's just let's engage it at a somewhat superficial level and just see what it does. And hopefully that creates and generates some excitement for people, you know, but anyway, that's, that's the piece for me.
That's good and that's, you know, and it's common sense but I mean to be able to articulate that and write it down, you're organizing what you want to do and then once you see it, it's like okay, you're more accountable you're owning it.
So they know actually how to use the AI, rather than just say, OK, give me the answer.
Like, it's not just about getting the AI policy. It's about starting with your vision and mission. And does that even have to slightly shift in light of AI, right?
And I'm like, Hey, how could we be together?
I intend to really lean into this, remain open minded and try and be an example for others.
From one badge to the bigger questions
Leading Teams in the AI Era is a completed badge, and its record is useful well past the people who took it. What follows is what it showed about learning alongside other leaders, where the badge goes next, and the questions it puts to anyone working on AI for deeper learning.
Participants arrived mid-stream, with a district AI policy recently adopted, a workflow agent in daily use, or learning time newly written into a team's calendar. The sessions gave that work a structure to name, test and talk through with a peer, and the strongest transcripts came from people using the badge to think about something real.
For the educators who stayed through the sequence, the thread holds together: a challenge named in Session 1, a move committed to in Session 2, a report on what happened in Session 3, and a reflection on their own learning in Session 4. Four completed the full arc, and their transcripts read as one continuous piece of thinking.
Pairings crossed roles and levels, from classroom co-teachers to system staff. Some of the most useful exchanges came when one partner could see a problem from a level the other could not, such as a wider system view meeting a partner's day-to-day constraints.
The prompts already ask participants to be specific, and the richest exchanges came when someone described an actual workflow, policy process or training plan. Carrying that expectation through each session, so partners bring and walk through one real agenda, prompt or workflow every time, would give colleagues more they can take straight back to a team.
The badge already invites participants to post and launch Small Wins, and that invitation could carry more weight if it came up throughout. None of the 23 Ideas or 3 Small Wins has been launched yet, while entries like a reflection routine written the same day as a final session are exactly what another leader could use. Naming what is worth launching after each session, especially right after a peer has heard what worked, would help more of this reach the community.
One educator described committee members who came out less apprehensive once they had heard colleagues weigh the benefits and drawbacks together. Others described adoption that began with each educator's own problem rather than an assigned use.
One educator wanted time saved on tracker updates returned as face-to-face work with students and families, so staff still feel useful. Another wanted it to become time to learn. Both treated the hour as a leadership decision as much as a personal one.
One participant's district approved an AI assistant for teachers and none yet for students, while encouraging responsible use for both. That gap shaped what a classroom leader could demonstrate to colleagues and to students.
Several participants connected hesitation to professional identity and treated it as reasonable. This record has no first-person cautious voice in it, which an open badge drawing people already leaning in would produce, so the careful view appears here only as others described it.
Two participants described a sustained change in the people they lead, and most described what they hoped to see. A badge built around leadership moves may need its own way of looking back, later, at whether a move took hold.