March 18, 2026
Engineering employers have repeated the same frustration for more than fifty years. New graduates struggle to communicate effectively. Despite endless revisions to writing instruction and presentation formats, the complaint remains unchanged. After decades of studying engineering communication and observing how young engineers transition into practice, I believe we have been solving the wrong problem.
The true issue is not writing quality. It is the absence of structured reasoning that leads to a clear, defensible recommendation.
At the Pine Grove Hall AI Series, where faculty, industry leaders, and community members gather to understand the implications of artificial intelligence, I discussed an approach that exposes this gap with uncommon clarity. I call it the AI Mirror Test, and it shows us that we must change engineering education now, not later.
The Real Deficit: Decision Thinking
Employers rarely comment on grammar. Instead, they describe behaviors that point to missing decision-making habits:
- Excessive technical detail without direction
- Hesitation to commit to a recommendation
- Data presented with no interpretation
- Weak articulation of trade-offs or risks
These are not communication problems in the superficial sense. They reveal a deeper lack of practice in decisional reasoning. Students are rewarded for correct methods, orderly explanations, and complete analysis. Yet in professional engineering, communication is judged by a single criterion: does this help us decide what to do next?
Most student work answers “what happened.” Industry needs “what should happen.”
The AI Mirror Test
To diagnose this mismatch, I run a simple experiment:
- Take a student’s technical paper.
- Improve its writing quality using AI.
- Present both versions to a practicing engineer.
- Ask which one supports a real decision.
The AI version will always look clearer. But if the original lacked framing, comparative evaluation, quantified implications, or a recommendation, AI cannot supply those absent mental steps.
The result is often eye-opening. The AI-polished version is easier to read, yet no more useful. This reveals where the real deficiency lies: not in presentation, but in the thinking that should precede it.
AI exposes weak reasoning. It does not fix it.
The Root Cause: Academic and Industry Misalignment
Faculty are not failing out of indifference. They simply operate within a communication culture that values completeness, correctness, and theoretical coherence. Industry communication prioritizes choice, consequence, and clarity under constraint. These two traditions are not the same.
A student can earn an A in the academic system yet produce a document no professional could use to guide action. Until assignments require students to make defensible decisions, the gap will persist.
The Structural Fix: Redesign Assignments
Students respond to what we assess. If we ask them only to describe or discuss, they will. If we ask them to make choices, quantify trade-offs, and defend a recommendation, they must reason. This redesign shifts communication from description to decision.
Real improvement requires structure, not slogans. Better writing alone will never create better thinking.
AI as a Thinking Partner
Some educators fear that AI will undermine students’ reasoning. In truth, AI reveals whether reasoning exists at all. When used well, AI becomes a catalyst for deeper thought:
- challenging assumptions
- generating counterarguments
- highlighting unsupported claims
- stress-testing recommendations
AI becomes harmful only when it replaces thought. It becomes powerful when it strengthens thought.
Parents can play a role too. Encourage children to use AI not for shortcuts, but for sharpening their ideas. Have them ask AI to critique their reasoning, not just summarize content.
Students who learn this now will be the most adaptable professionals of their generation.
A Clear Call to Action
If we continue training students to describe rather than decide, the economic value of a technical degree will weaken, employers will look elsewhere, and communities that rely on engineering talent will feel the consequences. The return on investment for higher education will decline quickly in a world reshaped by AI.
But with a shift toward decisional reasoning supported by AI as a reflective tool, we can graduate engineers who are confident, capable, and ready for the complex decisions ahead.
The moment to make this shift is now. Our students’ futures depend on it.
About the Author
Doctor Frank Archibald worked in the aerospace industry and later spent more than 20 years as an employee of Penn State’s Applied Research Laboratory Water Tunnel facility. During that time, he taught multiple mechanical engineering courses and, for fifteen years, led a two-semester capstone design sequence in the Master of Manufacturing Management one-year MSc program. It was through this experience that he recognized the growing misalignment between traditional undergraduate education and professional decision-making environments. He now volunteers with Discovery Space and The Rivet and advises students in the Penn State Wind Energy Club as they compete in the U.S. Department of Energy Collegiate Wind Competition.
Understanding AI Session 4: What’s Next
Wed, April 8 @ 7:00PM — 9:00PM
SESSION FOUR: Education, Local Impact, and the Road Ahead. A forward-looking wrap-up focused on Happy Valley, not Silicon Valley.
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