Advancing
Digital
Equity
A portrait of learning from a national crew of educators who spent 12 weeks examining digital barriers — and building the moves to address them.
How the crew engaged
Each dot is one substantive session. Paired = synchronous 20–30 min peer conversation. Solo = individual 10–15 min guided reflection.
Success Signs are observable evidence indicators from the AI for Deeper Learning (AI for DL) framework — mapped from this crew's Small Wins and session transcripts.
High draft volume reflects active private reflection — not low engagement. Drafted Ideas and Small Wins are only visible to the individual learner who received or created them.
Each node is one crew member. Lines show paired sessions; line weight reflects number of sessions together. 18 cross-organizational connections formed over 12 weeks.
The shape of the learning
This crew came in asking how to use digital tools more equitably. They left asking a harder question: who decides what counts as access, and what do we owe the people the system keeps missing?
Across 51 sessions and more than 91 documented Ideas and Small Wins, crew members moved from awareness of digital barriers toward concrete, context-specific leadership moves. The most significant learning happened when educators stopped diagnosing a systemic problem from a distance and started working it from inside their own sphere of influence — one conversation, one walkthrough, one audit at a time.
What made this crew distinctive: they named fear directly. Fear of AI tools, fear of FERPA violations, fear of looking incompetent in front of staff. That directness produced more specific — and more useful — leadership moves.
For facilitators, the question this data raises: what comes next for people who identified clear barriers and made initial moves, but haven't had structure to go deeper?
The barrier wasn't teachers' ability. It was fear, and a missing mental model. Understanding AI conceptually isn't enough. Teachers need physical, usable tools like prompt banks to sustain independent use.
Teachers need physical, usable tools like prompt banks and purpose-built GPT setups to sustain independent use. Understanding AI conceptually isn't enough.
Most of what looks like a student problem is actually a design problem. I stopped being fooled by the difference.
Five themes that surfaced
What came up repeatedly and with specificity across transcripts, Ideas, and Small Wins — not just once, and not in passing.
What looked like a tool problem was a trust, confidence, or design problem. Tools exist; support doesn't. Access is issued; belief is withheld.
A teacher's comfort with a tool determines what students can access. Supporting teacher digital fluency is an equity intervention — not a professional development nicety.
Fear of AI, FERPA, incompetence — this crew named fear with precision. Most sharply: fear is causing some schools to exclude students with IEPs from AI tools entirely, framing restriction as protection. That is not protection. That is inequity.
Decision-makers aren't using the same tools as students. Student experience — slow logins, navigation barriers, undifferentiated exam prep — rarely anchors adult decision-making. This crew named the mindset shift explicitly.
The most effective moves were targeted, built on relationships, and designed to outlast the person who made them.
Competency Strand Activity
This map shows where crew learning clustered across the five competency strands of the AI for Deeper Learning framework. Bold items reflect areas with documented evidence. Lighter items represent areas not yet surfaced in this crew's data.
Bar indicators reflect depth of evidence in crew data — not performance quality or evaluative grade. Bold items = documented evidence present. Lighter items = not yet surfaced in this crew's data.
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
Five Learning Targets (LTs) aligned to PSEL and CCDEI standards. Evidence depth reflects richness of crew data — not a performance grade or evaluation.
PSEL = Professional Standards for Educational Leaders (national). CCDEI = standards for cultural competency and equity in educational leadership. Both frame ADE badge learning targets and 6 equity clock hours.
Leadership Moves in action
Concrete actions taken inside each crew member's sphere of influence, using existing resources as design levers. These are documented steps — not aspirations.
Used existing collegial relationships — not hierarchy — as the lever. Surveyed comfort levels, built FERPA understanding, created purpose-built GPT setups. A one-way support effort became peer-to-peer exchange.
After student support specialists flagged virtual labs as the top barrier for neurodiverse students, audited all 9 class sections and built a walkthrough video bank. Students asked for more.
Discovered the program assumed basic tech fluency nobody had provided. Added a structured check-in: "Where might you need support to get back to the work that matters?" Removing the assumption accelerated learning.
Identified no protected time for leaders to build digital fluency as the core structural barrier. Used an upcoming statewide platform rollout as leverage for superintendent-level advocacy.
Equity had been left out — not removed, just never included. Named it directly in the meeting: "We think we are, but we don't know."
After a learning walk surfaced equity gaps, built a committee and — before leaving the site — wrote a postmortem for the incoming coach flagging who was ready to keep going and what needed to happen next.
Faced with pressure to allow personal computers, chose to maintain school-issued Chromebooks for all students — accepting a lower performance ceiling to ensure no student is visibly disadvantaged by what they bring from home. "There's a known known baseline tech access in every kid's life."
Collected real challenges from the room, ran a live prompt, and surfaced the output in real time — no slides, no theory. "In two minutes, Claude spit this out and they could see the finish line." Exposure as the entry point for skeptics.
What got documented
Small Wins are things crew members tried and learned from. Ideas are helpful thoughts, concepts, or goals. Both are generated through the sideby platform — some reflected back automatically from session transcripts, some written directly by learners. Drafted = private to learner; Launched = shared with the full crew.
A coach discovered their program assumed a level of basic tech fluency nobody had actually provided. They added a "back to basics" moment to every coaching session. One participant mentioned they'd never understood time zones on their calendar — they figured it out together. That person started asking about everything. "When someone believes they can learn it, they're more likely to ask about it — and that's when their learning accelerates."
A leader's partnership with a classroom teacher rippled outward — parent volunteers started asking to be taught. Three family learning nights followed. Families said: "this school is for us too."
After a learning walk surfaced equity gaps, a leader built a committee and wrote a postmortem for the incoming coach before leaving the site. The design principle: how do I make this work not need me?
Recognize that good form looks different for every teacher and learner. Coach toward each person's best form — not toward a single standard. Applies to coaching for digital equity as much as differentiation.
Move beyond using AI like a search engine. Package specialized knowledge into a custom GPT with a knowledge base and instructions — making your expertise accessible without requiring you to be in the room.
Stories make a moment vivid. Ideas make it reusable. If we can help people move from "here's what happened" to "here's the point," we make learning portable across contexts and cohorts.
Crew voice across the experience
Direct quotes from sessions, Small Wins, and Ideas — de-identified by role and context.
I LEARNED THAT AI IS ABOUT CONNECTING HUMANS AND BRINGING THEM OUT OF THE TECHNOLOGY NOT SUCKING THEM INTO IT FOR ETERNITY.
AI doesn't know what good differentiation looks like. The human has to hold that vision. That doesn't feel like a new idea, it feels like something I've believed for a while that finally has the right words around it.
I could absorb a thousand peer-reviewed studies about the value of technology for kids and still see a teenager hunched over a phone and not be convinced. I'm naming that as bias, as identity, as something I'm not through yet.
The richest learning conversations happened in the TLC because the stakes were contained and people chose what to surface. The design question isn't about making growth visible. It's about who holds the key to what gets seen and when.
The data that AI is trained on is historically trained on data that is already biased in what's been collected and how it's been collected, and it kind of re-emphasizes those loops.
Students seemed comfortable with these tools at a surface level. Many teachers created templates for students to make copies of. I wonder what it could look like to task students with using these tools to create their own templates and learning materials instead.
Equity isn't just about access. It's about whether students have enough agency over the tools to actually benefit from them.
Digital equity needs to become a more intentional piece of how I facilitate leadership learning, not just something I'm adjacent to. The question I'm sitting with is less about what it is and more about how to actually bring it in.
If we don't instruct and support kids on how to do that, I think the tool can be even more inequitable for certain students. They can't critically assess things if they don't have the foundational skills to do that.
This idea that students with IEPs need to be protected more against their own cheating. We can't have students with disabilities using these tools because they're just going to cheat.
It just gives you vocabulary to look at administrative choices through another lens. Where and when during the day we teach English 11, how would digital equity view that choice? Digital equity is just like another little filter.
Platform & course terminology
What comes next for this crew
Three time horizons based on what's in the data — for thinking about follow-up, iteration, or a future cohort.
- Surface Launched Small Wins in a crew-wide discussion — most of the richest learning stayed in Draft
- Document leadership moves with the design principle underneath, not just the action taken
- Name the 18 unique cross-org pairings — that network is a resource beyond the badge
- Prompt each crew member to identify one concrete next step before the crew disperses
- LT 5 (evaluating impact) needs more structure — consider a mid-badge iteration checkpoint
- LT 4 (AI stance) was uneven — a dedicated reflection prompt earlier in the sequence could help
- The "back to basics" protocol is a transferable facilitation move — document it explicitly for the next cohort
- Paraprofessional access came up organically — consider making it an explicit Empathy Exercise option
- Most of the richest thinking stayed in Draft. What would make Launching feel safer or more useful?
- This crew identified barriers with real specificity in 12 weeks. What supports continued iteration after the badge?
- State-level members raised equity omission in policy work. Can those stories connect across cohorts?
- When protected learning time is structurally unavailable, what does equitable digital onboarding look like?
The work doesn't stop when the badge does.
Every leadership move documented here was designed to outlast the person who made it. That's what equity work looks like when it takes root.