AI Partnership Pipeline: What CIS/Cyber Divisions in Colleges Can Offer K-12 Schools
- Greg Mullen
- 1 day ago
- 8 min read
It is becoming clear to an increasing number of schools around the world that Generative AI is no longer a lab curiosity or a tech company experiment. EdSurge’s Jeff Young warns that it could either “reshape the very purpose of schooling or trigger worst-case scenarios if left to chance” (Young, 2023). More than 200 new ed-tech products launched last year alone (Holt, 2024).
Deans who oversee computing and cybersecurity programs are increasingly being asked by superintendents, principals, and technology directors, “Where do we even start?”
The quickest on-ramp is to solve a problem every educator may not know they are already asking:
Make grade-level expectations transparent so students can share in the authority and responsibility for their learning to develop the skills they'll need to succeed outside of a schooling environment.
1. Start With a Standards Navigator — and Give It to Students
Instead of leaving state standards buried in teacher binders, put them in students’ hands. Two proven options illustrate what’s possible:
Elevate Standards Alignment automatically tags existing content to every U.S. standard and visualizes vertical skill progressions (Instructure, 2025).
Exploring the Core, Math K-8—a free iOS app (with in-app purchase)—shows learners every Common Core math expectation grade-by-grade. Founder of this app, Greg Mullen, insists that future iterations of this app can allow students to log evidence of mastery with photos, notes, or quick checks and monitor their academic progress in and across grade levels. Through a student-led AI-driven formative assessment app, students can instantly see what they’ve mastered, what’s missing from earlier grades, and what lies ahead. (Innovations are pending funding and investment.)
Imagine a seventh-grader opening an app like Exploring the Core, Math K-8, and realizing, “I still need to prove fluency with sixth-grade ratio tables; maybe that's why I'm struggling to move on to eighth-grade linear equations.” Pair that insight with Elevate’s machine-learning dashboard and a companion chatbot (using the 1EdTech CASE® standard for secure data exchange). The bot can pose targeted questions, point to just-right resources, and prompt reflection across grade levels, while their teacher prompts students around the classroom to consider effective learning strategies and offer non-technical summative assessments to allow students to separate formative learning via AI technology from summative assessment that promotes achievement of independent sustained learning without support.
This combination moves schooling from teaching students how to be taught to teaching students how to learn and improve over time. Learners pick which standards to tackle when they’re ready, individually or as a group; teachers shift from content deliverers to metacognitive coaches, guiding strategy use, self-reflection, and motivation coaches. In short, transparent standards plus AI guidance under the professional tutelage of professional learners (i.e. "teachers"), can empower students to own their progress regardless of age, grade, or pacing while still pursuing district diploma requirements.
Why it matters
Vertical coherence: Students see how today’s lesson links to future success, a proven driver of motivation (Verma, 2023).
Time savings: Auto-tagging reduces the weeks teachers spend aligning resources.
Equity lens: Transparent standards help close “hidden curriculum” gaps for students who traditionally rely on guesswork to understand expectations.
2. Pair the Navigator With a “Socratic” AI Tutor
Unlike chatbots that spit out full answers, next-gen classroom apps such as NSW EduChat and Khanmigo prompt students with hints and counter-questions (Cassidy, 2024; Pillay, 2024). The tutor nudges learners to articulate reasoning, while teachers coach them on metacognitive moves—predict, test, reflect.
Implementation pointers
Guided questioning protocols: Train staff to model probes like “What do you already know about this standard?”
Privacy & security checks: Follow higher-ed data-governance playbooks so districts avoid compliance missteps (Ohio State University, 2025).
Teacher dashboards: Real-time transcripts help instructors spot misconceptions without hovering.
3. Safeguard Thinking With Analog Assessments
A legitimate fear is that students will lean on AI as a shortcut and not train their brains on the inherent struggles and benefits of thinking without support of technology. Best practice from pilot districts is to end units with AI-free demonstrations: hand-written explanations, oral defenses, or paper-based concept maps. These “clean-room” assessments verify that the mental model belongs to the learner, positioning the language model as a support for what's known as "formative assessment" (assessment for learning) as opposed to what's called "summative assessment" (assessment of learning).
4. Professional Learning for Metacognition
None of this works without the effective leadership and coaching from our professional classroom teachers. If we want students to think about their own thinking (metacognition) effectively while using AI, we first have to help teachers shift from the “sage on the stage” mindset to a “guide on the side.” That shift can’t be accomplished in one big sit-down workshop or a semester-long lecture course; teachers need short, practical practice sessions that are followed up with personalized coaching and peer feedback—just as they will be providing for their students.
Colleges must team up across disciplines.
Designing this kind of rapid-cycle, cognition-focused PD for teachers to coach their students on metacognitive practices is not purely a technical challenge. CIS/Cyber divisions should partner with psychology and education experts who specialize in learning sciences, motivational theory, and classroom coaching. These experts can translate cognitive-load principles, self-regulation research, and social-emotional best practices into micro-workshops that fit teachers’ schedules and align with district goals. Joint teams can also establish evidence-collection protocols—protecting student data while measuring gains in metacognitive skill use—so every PD cycle feeds back into tool refinement and teacher support.
The table below lists three examples of 30-minute “micro-workshops.” Each one targets a single, concrete skill that teachers can apply the very next day:
30-Minute Micro-Workshop | What Teachers Practice | Why It Matters for AI + Metacognition |
Motivational Interviewing Conversational Techniques for Learning as Change | Reflective listening for surfacing students’ own reasoning and feelings, so they hear their thoughts echoed back and can clarify or extend their ideas before turning to the AI tool. Open-ended questions to push learners beyond yes/no answers, prompting them to explain how and why they arrived at a conclusion, which strengthens metacognitive awareness. Giving affirmation boosts students’ confidence and reinforces productive learning behaviors (“I noticed you checked the rubric before revising—great strategy”), making them more likely to keep using self-reflection routines. Be specific! | When students use an AI chatbot, the teacher’s role becomes guiding how they think, not supplying answers. Practicing “ask before you tell” helps teachers pause and let students articulate their reasoning before the bot—or the teacher—jumps in with the solution. |
Rubric Deconstruction Lab | Breaking the assessment rubric into clear criteria shows students exactly what success looks like and where to focus their efforts.
Modeling how to grade an AI-generated draft against those criteria teaches them to spot gaps and revise the work independently. | Students learn to be critics of AI output instead of copy-and-paste users. By comparing the bot’s draft to the rubric, they visualize gaps (“The content is OK, but the evidence isn’t cited”) and plan revisions—an essential metacognitive move. |
Student Exit-Ticket Templates | Using quick end-of-lesson reflection prompts: “One way AI helped me today…” “One thing I still want to try on my own next time…” | Regular, bite-size reflection trains students to notice how they relied on AI, judge its usefulness, and set a personal goal for the next session. Over time these small habits build genuine self-regulation. |
Why “Quick-Hit” Coaching Beats One-Off PD
Lower Cost via Targeted Benefit: Coaching for teachers can involve 30-minute sessions that fit into teachers' common-planning periods or an after-school slot, rather than costly whole-staff presentations. The true cost of whole-staff presentations is not in the amount paid to the presenter but in the time staff are spending in a session that isn't addressing their concerns and decreasing staff productivity by reducing their time to tend to their personal and professional needs.
Immediate practice: Teachers who receive personalized coaching often walk away with one tactic to try the next day, addressing a specific issue they're facing in context of the student dynamics in their classroom, and knowing a scheduled email from their coach will be sent the next day asking the teacher to reflect on the new practice.
Follow-up coaching: One-off feedback is rarely enough; to close the gap between theory and practice, learning how to apply what seems to a teacher like a good theory can take time, especially when a teacher is in an early stage of readiness. Coaches who adhere to effective change models respond to stages of readiness and do not assume all strategies result in sustained change in behaviors.
When these short sessions are repeated, adding one more coaching tool as developmentally appropriate for teachers per session, the result is a growing toolkit that allows them to be intentional and not just intuitive in helping the specific students in their classrooms to use AI thoughtfully rather than mindlessly.
5. A Change Model That Scales
Change initiatives stall without a structured change process. The Trailblazing Change model combines two frameworks, each with decades-long research and evidence for promoting effective change. The first is the Transtheoretical Model (TTM) of behavior change, adapted with five stages of change for identifying stages of readiness to adopt new behaviors. The second is Motivational Interviewing (MI), a conversation tool for encouraging agency-based and personalized change with techniques that shift the authority for change from leadership to staff. Both of these frameworks utilize change processes rooted in the TTM and address deeper aspects of change that intentionally help staff to reflect on beliefs driving resistance which can be helpful for leadership in considering aspects of a change initiative that may not be in the staff's best interest.
The following describe how a few of these Change Processes can be effectively applied by staff and leadership to improve the effectiveness of a change initiative in their school:
Consciousness Raising – Open the next faculty meeting with a five-minute case study: a middle-school team that used an AI “standards navigator” to boost gains in math, followed by a cautionary slide showing students who copied chatbot answers and failed their exams.
Optional Content: two short video clips plus a discussion prompt.
Impact: moves staff from “I’ve barely heard of this” to “I wonder if...”
Self-Reevaluation – During PLC time, ask each teacher to rate, on a 0–10 scale, how well AI-guided, self-directed learning fits their teaching philosophy. Follow up with, “What would raise your score by one point?”
Optional Content: a simple MI-style reflection sheet or interactive form.
Impact: surfaces personal values, turning vague interest into specific growth goals.
Self-Liberation – Offer a “one-class pilot pass”: teachers choose a single period next week to let students use the navigator and chatbot after the teacher coaches a specific aspect of metacognition relevant to using AI for learning.
Optional Content: ready-made lesson template and on-call tech support.
Impact: converts intention into concrete commitment, lowering barriers to first use.
Reinforcement Management – After two weeks, pull chatbot analytics showing a 20 percent drop in “I don’t know” student responses; share the graph in the staff newsletter, allowing individual staff to receive and process that information. Then, publicly thank the pilot teachers and give them a badge for their door as AI Pilots.
Impact: rewards early effort and signals that leadership values measurable progress.
Helping Relationships – Form mixed-grade professional-learning communities where a college CIS mentor, a high-school teacher, and an elementary teacher meet once or twice a month to review anonymized student chat transcripts. Together, they spot misconceptions and design next-step prompts.
Optional Content: a shared online folder with sample chats and comment threads.
Impact: builds support network that spreads expertise and keeps momentum alive.
Call to Action
CIS/Cyber divisions can lead the conversation by bringing a complete package to district partners:
Technology → a secure standards-navigator plus a Socratic chatbot.
Pedagogy → PD modules that build metacognitive coaching skills.
Change process → the Trailblazing Change roadmap, ensuring adoption sticks.
If your college is ready to co-pilot this vertically aligned, agency-driven model, reach out to discuss how we can tailor Trailblazing Change to your partner districts’ readiness levels and cybersecurity requirements. Together we can turn AI from an assessment headache into the engine of a self-directed schooling future.
Greg Mullen
May 22, 2025
References
Cassidy, C. (2024, February 12). The AI chat app being trialled in NSW schools which makes students work for the answers. The Guardian.
Holt, L. (2024). A map of generative AI for education. Medium.Instructure. (2025). Elevate standards alignment.
Instructure. (2025). Elevate standards alignment.
Ohio State University. (2025). AI considerations for teaching and learning. Teaching and Learning Resource Center.
Pillay, T. (2024, September 5). Kristen DiCerbo. TIME. https://time.com/7012801/kristen-dicerbo/
Verma, Y. (2023, October 16). Bridging content gaps: The importance of vertical alignment. TEACH Magazine.
Young, J. R. (2023, November 14). How AI could bring big changes to education—and how to avoid worst-case scenarios. EdSurge.
