Transforming Workforce Development at Holyoke, MA Dean Technical High School

At yesterday's annual advisory board meeting, the ongoing partnership between Dean's educators and industry was clearly evident. The meeting focused on refining program direction, evaluating student performance outcomes, and anticipating the skills students will need as industry continues to evolve. Having witnessed this collaboration for over 20 years, I can attest to how this model demonstrates the value of maintaining strong connections between educational institutions and the industries they serve while preparing students for successful careers.

William J. Dean Technical High School in Holyoke, Massachusetts, exemplifies how strategic planning and comprehensive needs assessments can transform career and technical education programs even under challenging circumstances. The school has operated under some unique constraints as part of the Holyoke Public Schools district, which was placed under state receivership in April 2015 due to chronically low graduation rates and test scores. The state takeover, which stripped local School Committee and superintendent decision-making power in favor of a state-appointed receiver, created both challenges and opportunities for educational transformation. After nearly a decade under state control and with some resistance, Holyoke is poised to regain local control in July 2025, making it the first Massachusetts district to successfully exit receivership. 

Now operating as the Holyoke High School Dean Campus, this institution serves up to 400 students with a predominantly Latino population, where more than 90 percent qualify for free or reduced-price lunch, nearly 50 percent receive special education services, and 35 percent are English Language Learners.The school's evolution demonstrates the importance of feasibility studies in educational program development. Through collaborative partnerships, Dean has completed in-depth organizational assessments and this data-driven strategy helped faculty and staff better understand  student behaviors, develop effective communication strategies, and create supportive learning environments.

Dean now offers nine specialized Career, Vocational and Technical Education (CVTE) programs spanning Advanced Manufacturing, Automotive Technology, Carpentry, Cosmetology, Culinary Arts, Diesel Technology, Electrical, Health Assisting, and Programming and Web Development. Each program's establishment and growth stems from careful strategic planning that balances local industry demands with student career aspirations. 

The Programming and Web Development program stands as a prime example of the school's industry-aligned approach. For over two decades, I have served alongside other working professionals on the program's advisory board, offering continuous input that keeps the curriculum aligned with rapidly evolving technology and current hiring standards. This long-term partnership ensures graduates move on to college or enter the workforce with relevant, up-to-date skills.

At the meeting, Joel McAuliffe, Director of Career and Technical Education, emphasized the school's commitment to providing diverse opportunities. Dean’s philosophy reflects the comprehensive approach to workforce development that emerges from thorough feasibility studies and ongoing community needs assessment, ensuring programs remain relevant to both student aspirations and economic realities. 

And finally A GIGANTIC shoutout to two of the many amazing faculty at Dean - Pepe Pedraza and José Gastón. Your students are very fortunate!

Why We Need to Rethink How We Define Skilled Technical Workers

The way we classify skilled technical workers is broken—and it's impacting both workers and the economy. A new study reveals that the current definition misses hundreds of thousands of technically skilled jobs, from modern stonecutters using laser technology to aircraft assemblers working with complex systems.

Issues In Science and Technology posted an interesting article titled Retooling the Definition of the Skilled Technical Workforce. The article, authored by Guy Leonel, Vicki Lancaster, Sarah McDonald, and Cesar Montalvo claims the way we classify skilled technical workers is broken—and it's hurting both workers and the economy. Their study reveals that the current definition used by The National Science Board misses hundreds of thousands of technically skilled jobs, from modern stonecutters using laser technology to aircraft assemblers working with complex systems.

The researchers claim The National Science Board's definition of the skilled technical workforce relies on outdated survey data that focuses on education credentials rather than actual skills. This approach uses the Department of Labor’s Occupational Information Network (O*NET) system, which asks workers to rate their knowledge across 14 STEM domains on a 1-7 scale. Jobs that don't score at least 4.5 get excluded—even if they require sophisticated technical skills. Take stonecutters: They've evolved from using hand chisels to operating CNC machines, CAD software, laser scanners, and water jet cutters, yet they're not considered part of the skilled technical workforce under current definitions.

The researchers propose focusing on actual job skills rather than degrees and using real-time job posting data instead of small, outdated surveys. When they analyzed 91,000 job postings with over 5,000 skills, they found 56% more occupations qualified as skilled technical work compared to the current system. Eighty-four additional occupations—including sheet metal workers, automotive repairers, and aircraft assemblers—were identified as requiring advanced technical skills.

Getting the definition right has real consequences: better workforce planning, improved career guidance for nondegree credentials, enhanced economic competitiveness, and more recognized pathways to middle-class careers. As technology rapidly transforms work, our methods for measuring skilled technical jobs must evolve too. The current system can't keep pace with how AI, automation, and digital tools are reshaping occupations. By embracing real-time job data and focusing on actual skills, we can build a more accurate picture of America's technical workforce and better support the workers who keep our economy running.

I strongly encourage reading the entire Issues In Technology and Science article.

Maxwell's Four Equations That Changed Everything (Explained Without Numbers)

This past spring semester, when a faculty member had to take an unexpected leave at the 5th week of a 15 week semester, I picked up an Electromagnetic Fields and Waves course at The University of Hartford. It is one of the most difficult courses in an Electrical Engineering program with some pretty heavy math. While it is still fresh, here's a no-math explanation of Maxwell’s equation set, the fundamental principles underlying nearly all contemporary electrical engineering and technology.....

In the 1860s, James Clerk Maxwell identified four equations that unified electricity and magnetism into a single electromagnetic theory. These mathematical expressions revealed how electric and magnetic fields interact and generate each other, providing a framework for understanding electromagnetic phenomena throughout the universe.

Equation 1: Gauss's Law for Electricity 

Electric charges produce electric fields which surround them. For example, the electric field generated by a charged balloon when close, causes your hair to stand up. That charge extends through all directions and as the amount of charge increases, a stronger electric field is produced.

Equation 2: Gauss's Law for Magnetism 

The fundamental principle revealed by Gauss's Law for Magnetism demonstrates that magnetic monopoles do not exist. Electric charges exist independently as positive or negative entities but magnets exist only as dual poles that include north and south magnetic ends. For example, cutting a magnet into two pieces produces two smaller magnets that each contain both north and south poles. 

Equation 3: Faraday's Law 

The relationship between shifting magnetic fields and electric field generation is defined through Faraday's Law. The operation of power plant generators depends on this fundamental principle. A magnet will generate electricity when it moves through a coil of wire because the altering magnetic field produces electric current. For example, you may remember from a high school science experiment that passing a magnet through a copper coil can result in enough electric current flow to light up a lightbulb.

Equation 4: Ampère's Law with Maxwell's Modification 

Flipping things around, Equations 4 identifies an opposite process, demonstrating how electric currents together with changing electric fields produce magnetic fields. For example, the movement of electric current through wires produces magnetic field deflection that enables the operation of doorbell systems, MRI machines, and those giant electromagnets you see used on cranes in junkyards.

Together, these four equations show that electric and magnetic fields are interconnected - they can transform into each other and propagate through space as electromagnetic waves, including light, radio waves, and X-rays. They form the theoretical foundation for virtually all modern electrical technology.

Beyond Perfect: Career Advice for the Class of 2025

Another year, another semester, another graduating class. Cannot believe it has been 46 years since I graduated from UMass Amherst. Here’s some advice to the class on 2025 based on what I’ve learned over the years.

Focus on what you can actually control. The job market conditions and hiring companies and callback responses from your dream company remain beyond your control. You maintain full authority to handle your application submissions and interview preparation and rejection response approaches. 

Stop trying to have the "perfect" career path. Your very first job position does not need to be perfect. Your second one won't be either. Your success depends on acquiring knowledge and your ability to manage any circumstances that arise. Many new graduates turn down suitable job offers because they choose to wait for opportunities that may never appear. 

You cannot stop difficult coworkers or bad managers from occurring. Every workplace has them. You maintain control over your ability to handle situations and learn from them and your understanding of when it is time to move on. Devote your energy to people who demonstrate a willingness to transform themselves. 

Technical problems will break in ways you didn't expect. You cannot stop all bugs or system failures from occurring. Your control extends to your troubleshooting approaches and your team communication during failures as well as your documentation of learned lessons for future reference. 

Your college grades are history now. The grades you earned in college remain in the past regardless of your academic performance. Your success depends on how you utilize your current knowledge and your speed in acquiring and processing new information. 

Achieving success (however you define it) does not mean you control everything. The most successful develop expertise in handling any unexpected challenges that emerge.

How 6G Will Improve on 5G in the Same Spectrum

A couple years ago, I spent some time developing 5G wireless technology content for faculty to use in their classrooms. Here comes the next generation.... 6G…. and (of course) AI is playing a major role in network management.

The transition from 5G to 6G represents more than just a numerical increment—it's a
fundamental rethinking of wireless network design. Many people believe new spectrum bands are needed for meaningful improvements between generations but the situation is more complex. The radio waves used by 5G in its sub-6 GHz and mmWave 24-40 GHz bands can be utilized more efficiently by 6G technology to achieve significant performance improvements. These enhancements would represent fundamental changes to network reliability and capacity and intelligence rather than minor adjustments without needing costly spectrum license purchases. The innovations would emerge from rethinking both signal transmission physics and network management intelligence. 

Advanced MIMO Systems: 6G technology could implement massive arrays with 1,000+ elements to generate extremely precise beams which reduce interference and boost capacity beyond the 5G maximum of 64-128 antenna elements. 

Smarter Waveforms: The waveform technology in 6G would surpass OFDM by implementing adaptive waveforms which modify their patterns according to environmental conditions. The system functions like an automobile which adjusts its body shape to achieve better aerodynamics during specific situations. 

AI Network Management: Like recent advancement in 5G, 6G networks will employ AI to forecast user activities and data requirements so they can distribute resources before users initiate their requests. 

Cell-Free Architecture: Your device would establish simultaneous connections with multiple transmission points which work together to provide seamless coverage throughout the network. The 6G radio technology functions as an environmental sensor to help the network optimize signal paths through its ability to detect physical obstacles and movement patterns. 

When We'll See It: Based on historical wireless technology evolution patterns and current industry roadmaps, commercial 6G networks are likely to launch around 2030-2032, following a progression similar to previous generations:

·      Initial technical requirements and vision documents are already being developed (2023-2025)

·      Research and standardization work will accelerate through 2026-2028

·      The first official 6G specification (3GPP Release 20 or 21) is expected around 2028-2029

·      Early trial deployments would follow in 2029-2030

·      Commercial availability would begin in leading markets by 2030-2032

Several major telecommunications companies including NTT DoCoMo, Samsung, and Huawei have published 6G whitepapers targeting 2030 for initial deployment. Various international research initiatives like the EU's Hexa-X project and China's national 6G promotion group are working toward this timeline.

6G's improved features would achieve 2-3 times better efficiency without spectrum expansion but the largest benefits would emerge from incorporating additional high-frequency bands.

Some Of My Favorite AI Tools For Engineering Students

As an engineering professor, I've seen how AI tools are transforming how we tackle our coursework, from solving complex equations and debugging code to creating visualizations and polishing lab reports. Whether you are wrestling with thermodynamics problems at midnight or designing circuits for a project, these AI assistants will help you work smarter and learn more effectively. Here's a list of some of my favorite AI enabled resources for engineering students. This list is in no way complete!

 

For Problem-Solving and Calculations:

·       Wolfram Alpha - Exceptional for advanced mathematics, physics, and engineering calculations. It can solve differential equations, perform matrix operations, and provide step-by-step solutions.

·       Symbolab - Great for calculus, linear algebra, and showing detailed problem-solving steps.

·       MATLAB Online - While not purely AI, it includes AI/ML toolboxes and is essential for many engineering courses. We all use it!

 

For Research and Learning:

·       Claude - Helpful for explaining complex engineering concepts, debugging code, and providing detailed technical explanations.

·       Gemini (Google's AI) - Excellent for research and technical explanations, with strong integration with Google services and ability to analyze images and technical diagrams.

·       ChatGPT - Good for general engineering questions and concept clarification.

·       Perplexity AI - Excellent for research as it provides citations and up-to-date information.

 

For Programming and Code:

·       GitHub Copilot - Invaluable for coding assignments in Python, C++, MATLAB, and other languages commonly used in engineering.

·       Replit AI - Integrated coding environment with AI assistance.

·       Gemini Code Assist - Google's coding assistant, particularly strong with Google Cloud and web development.

 

For Design and Visualization:

·       DALL-E 3 or Midjourney - Useful for creating diagrams, conceptual designs, or visualizations for presentations.

·       Canva AI - Helpful for creating professional presentations and posters.

 

For Writing and Documentation:

·       Grammarly - Essential for lab reports, technical writing, and documentation.

·       Quillbot - Useful for paraphrasing and improving technical writing clarity.

 

Specialized Engineering Tools:

·       Ansys AI - For simulation and analysis in mechanical/aerospace engineering.

·       PSpice - Industry-standard circuit simulation software .

·       CircuitLab - For electrical engineering circuit analysis.

 

Study and Organization:

·       Notion AI - Great for organizing notes, creating study guides, and managing projects.

·       Anki with AI plugins - For creating smart flashcards for technical terms and formulas.

 

These are just some of many excellent AI tools that I use.... some are more "AI" than others. Most colleges and universities offer free or discounted access to many of these tools. I'd recommend starting with one or two that match your immediate needs and gradually exploring others as you progress through your coursework. Always check your university's academic integrity policies regarding AI use in assignments.

A Response - Rethinking Engineering Education for the AI Era

In my last post, Reimagining Engineering Homework with Simulators in the Age of AI, I argued that traditional electrical engineering homework focused on calculations is now easily solved by AI, requiring educators to shift to simulator-based assignments that develop higher-order skills like design, troubleshooting, and systems thinking. By using circuit simulation tools, students can engage in active experimentation and real-world problem-solving that requires distinctly human engineering judgment that AI cannot replicate.

I received the following comment on the post: 

I agree that it makes no sense to assess students' ability to make calculations that the simulators they are familiar with already make. The problem, though, is that even the tasks you suggest (e.g., create a design that meets specifications) can be already be accomplished by a variety of generative artificial intelligence platforms. Which begs the questions: what will the electrical engineers we are training actually do when they graduate, and what will they need to know in order to do it?

I’d be a liar if I said I was not asking myself the same questions. Here’s my reply:

You raise a crucial point that goes to the heart of modern engineering education. The rapid advancement of AI tools that can handle both calculations and design tasks challenges us to fundamentally reconsider what students need to learn.

I think the key lies in developing capabilities that remain distinctly human, even as AI handles more routine tasks. Future electrical engineers will likely need to excel in:

Systems thinking and integration - While AI can generate designs meeting specific parameters, engineers must understand how components interact within larger systems, identify trade-offs, and make judgment calls that balance competing constraints beyond what can be easily quantified.

Problem definition and formulation - Perhaps most critically, engineers need to determine what problems to solve in the first place. AI can optimize solutions, but it still requires human insight to identify the right questions and define meaningful specifications that serve real human needs.

Critical evaluation and verification - Engineers must be able to assess AI-generated solutions, spot errors or limitations, and validate that designs work in real-world conditions with all their messy complexities.

Innovation at the intersection - The most valuable engineers will combine domain expertise with an understanding of what AI can and cannot do, using these tools creatively to solve problems that neither humans nor AI could tackle alone.

Rather than competing with AI on tasks it can already do, engineering education must focus on these higher-level skills while using AI tools as aids in the learning process itself.