The TechEd Podcast

What does the rise of AI mean for technical programs? Surprisingly, it's not a new concept to CTE fields. It is embedded in robotics, automation, diagnostics, and data modeling across modern manufacturing facilities today.
In this episode of The TechEd Podcast, Matt Kirchner sits down with Dr. Andrew Neuendorf, Associate Dean of Manufacturing, Engineering, Trades, and Transportation at Des Moines Area Community College (DMACC), to explore what applied AI actually means inside CTE programs and why education must move beyond generative AI.
With a background in English and the humanities, Andrew offers a rare perspective on how artificial intelligence is perceived differently across academic disciplines. From robotics labs to industrial technician programs, he explains where AI has already been embedded for years, where disruption is coming next, and how community colleges can respond with clarity rather than panic.
From design software disruption to AI-assisted troubleshooting and entry-level data modeling skills, this conversation will help technical educators think about applied artificial intelligence in their programs.
In this episode:
Why robotics and automation programs have been teaching AI longer than they realize
The hidden risk inside CAD and design-heavy technical pathways
How students are using AI to troubleshoot equipment faster than faculty expect
Why the “trades are safe from AI” narrative may be dangerously simplistic
Why competency-based education might be a better model in this AI-driven world
3 Big Takeaways from this Episode:
1. Applied AI has already been embedded in CTE for years. Robotics vision systems, PLC-driven automation, driver-assist sensors, and predictive maintenance models have quietly trained students in machine intelligence long before generative AI dominated headlines. The difference today is scale and accessibility, not the existence of AI itself.
2. The future disruption isn’t blue collar versus white collar — it’s discipline by discipline. Andrew argues that assuming the trades are immune to AI disruption is a strategic mistake, particularly in design-heavy roles like CAD and digital modeling. Education must evaluate AI’s impact at the skill level rather than rely on outdated workforce categories.
3. Students may lead the applied AI shift inside technical programs. From uploading robot manuals into NotebookLM to accelerating troubleshooting in automation labs, students are modeling AI-assisted problem solving in real time. Institutions that recognize this and structure learning around it will move faster than those focused solely on policing its use.
Resources in this Episode:
Connect with Andrew on LinkedIn
Other resources:
"Something Big is Happening" by Matt Schumer
Jensen Huang (NVIDIA) CES Keynote
Six Days in China: The Speed, Scale and Strategy Outpacing U.S. Innovation - Todd Wanek, CEO of Ashley Furniture
Try Google's Not
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The labor market is evolving faster than traditional signals of competence can keep up. Roles are shifting, technology is accelerating, and employers are searching for clearer ways to identify real capability. In that environment, degrees remain valuable, but verified skills are becoming increasingly central to hiring and advancement.
In a marketplace now saturated with more than a million credentials, clarity has become the differentiator. Some credentials function as marketing tools. Others function as infrastructure for trust. The difference lies in independence, rigor, defensibility, and industry validation.
In this conversation with Kathleen McNally, CEO of NOCTI, we explore how high-quality third-party credentials create reliable signals for employers, meaningful exit value for learners, and actionable tools for educators. From ISO-backed certification standards to performance testing and stackable micro-credentials, this episode reframes credentials as essential infrastructure for a more agile, skills-driven economy.
In this Episode:
Why employers are shifting from degree-first to skills-aware hiring
What separates a participation badge from a legally defensible certification
How micro-credentials create flexibility without sacrificing rigor
How “exit value” changes the way we think about graduation
Why performance-based assessment strengthens workforce confidence
3 Big Takeaways
1. Verified credentials are strengthening the signal in a fast-moving labor market. As job roles evolve more quickly than traditional degree cycles, employers are seeking precise indicators of job-ready capability. Third-party certifications developed with national industry input provide measurable proof of occupational and technical competency.
2. Quality and independence determine whether a credential carries real weight. With more than a million credentials available, rigor is what separates noise from trust. ISO-aligned, legally defensible certifications built through independent subject matter experts reduce hiring risk and create consistency across regions and employers.
3. Micro-credentials are enabling lifelong learning with structure and momentum. Stackable certifications allow learners to document specific competencies at any stage of their career. Whether entering the workforce, reskilling mid-career, or adding new capabilities such as AI literacy, credentials create flexible on-ramps and sustained pathways for advancemen
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Over the past several years, many leaders have noticed a subtle but meaningful shift inside their organizations: the erosion of basic workplace courtesies, particularly from younger employees new to the professional workplace. Not misconduct; not ethical lapses. But something more subtle. Employees announcing time off instead of requesting it. Cameras off in meetings. Missed meetings treated casually. Messages left unanswered for a day or more. Delegating up.
Matt Kirchner addresses these patterns directly, sharing firsthand stories from his own companies and examining what has changed in professional norms, and why.
This is not a critique of younger employees. We're all for promoting emerging leaders and believe early-career professionals bring energy, ambition, and a willingness to attempt what others might dismiss. But many of these individuals also entered the workforce after losing formative years of in-person education, internships, and social development during COVID-era isolation. As a result, expectations that once felt intuitive often now require explicit instruction.
This episode explores specific areas where standards are slipping and explains how organizations can reestablish expectations without embarrassment or blame. Matt closes with a practical framework for addressing unprofessional behavior consistently and constructively, with the goal of strengthening culture rather than policing it.
Listen to Learn:
The workplace behaviors that are quietly reshaping organizational culture
How pandemic disruptions affected professional social development
Why virtual meeting norms have outsized cultural impact
What “delegating up” reveals about accountability and ownership
A structured approach for restoring workplace standards without alienating newer employees
Resources in this Episode:
Read the article in Products Finishing: "Relearning Common Courtesies in the Workplace"
Follow Matt on LinkedIn
View more resources on the episode page!

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Imagine giving middle and high school students a full week to explore careers without grades, homework, or “regular” classes. That’s the core idea behind Camanche Community School District's J-term: a dedicated pause in the academic grind that turns school into a career-exploration lab, where 5th-12th grade students can test-drive career fields, build confidence, and discover options they didn’t even know existed. 
That's right: J-term is no longer only reserved for the college experience.
What makes this model so compelling is the way it engages students & teachers in a way that the traditional classroom never could. Middle and High School Principals Melissa Phillips and Josh Davis saw higher engagement and clearer direction in this real-world, hands-on week of learning. You see it in the outcomes and reactions: the high school generated 100+ internships, the middle school reported zero office referrals that week, attendance hit a high-water mark, and the community showed up in force.
In this episode:
Why a “no grades, no homework” week can reveal a different version of students and teachers.
How J-term creates equity in exposure by removing barriers families can’t always overcome on their own. 
Why employers and community partners are more likely to say “yes” to short, high-impact experiences than longer commitments. 
How a J-term can reshape course choices and future plans, helping students move from “I don’t know” to a real next step. 
3 Big Takeaways from this Episode:
1. A grades-free, hands-on week can encourage a different level of student engagement. At the middle school, they saw zero office referrals and “not one student” sent down for behavior during J-term. The deeper lesson is that when the goal shifts from performance to exploration, more students lean in because the experience finally matches how they learn best. 
2. J-term is an equity play because it reduces the “who you know” advantage. The high school framed it explicitly as removing barriers for families who “don’t have the means” or the pathway to get students these opportunities on their own. Guardrails like requiring internships not be at a student’s current job help ensure the week is about new exposure, not just extending what already exists. 
3. Short, real-world exposure can change trajectories faster than a semester of coursework. One student had such a strong internship experience that she reworked her schedule to keep going back, and it shifted her likely postsecondary plan toward education. The big insight is that a one-week “test drive” creates clarity either way, helping students confirm a direction or rule one out while the stakes are still low. 
Resources in this Episode:
Read about Camanche CSD's J-Term week
Visit Camanche CSD
See more on the episode page!
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Your organization doesn’t have a data problem. It has an intelligence problem: the gap between having information and being able to act on it with speed, clarity, and confidence. That gap shows up everywhere: hospitals, schools, manufacturers, and any team drowning in dashboards while leaders still wait on someone to “find the story.”
Rick Anderson (Chairman & CEO, The Hive Health) is back to show just how much AI has impacted one organization in 12 months. Enter Corby Furrer (Harvard AI fellow, builder since college) and Will Furrer (former NFL quarterback turned COO). Together, they've built what they call a "trade intelligence platform" - not another analytics tool, but a system that encodes economic expectations, reconciles them against purchasing reality in real-time, and tells people exactly what action to take when behavior drifts off course.
Intelligence isn't about regression models anymore. It's about knowing what "good looks like," verifying AI assumptions through human-in-the-loop, and translating observations into stories that change behavior when delivered by the people who speak the right language (physician to physician, engineer to engineer, teacher to teacher, not consultant to administrator). Sustainable change requires three legs: understanding the rules of the game, seeing what's actually happening (not what's supposed to happen), and coaching insights through stakeholders who can shift behavior.
AI scales when it creates shared clarity people can validate and act on repeatedly, not when it generates reports that collect dust behind the CEO's desk.
In this episode:
Why your organization can have endless dashboards and still lack decision-grade intelligence. 
What must be true for leaders to trust AI results enough to act on them. 
How a data observation becomes a story for the change agent that actually drives behavior change. 
How coding and product building changes when AI can generate code, and why knowing “what good looks like” matters.
Why one-time improvements fade, and what it takes to build a repeatable system.
3 Big Takeaways from this Episode:
1. Decision-grade intelligence starts with clear expectations and a next action. The bridge from data to intelligence = what should happen, reconciling it against what is happening, and using that gap to drive the next corrective step. The takeaway is widely applicable: if you cannot state the intended economic or operational outcome, you cannot reliably diagnose variance or drive consistent performance.
2. If the improvement is not repeatable, it is not a solution. Build a system that codifies the work, monitors performance against targets, and keeps savings from reverting once the project ends. The real value in AI projects is durable behavior change and ongoing detection of the next opportunity, not a one-time finding.
3. Insights only matter when they are delivered to the change agent as a story that drives action. A data observation has to become a narrative that the person who can change the behavior will actually respond to. In the AI era, that elevates a specific skill stack: storytelling, curiosity, and building, because trust and adoption live or die in communication and execution, not in the existence of a model. 
Links & resources
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