Overeducated, Ph.D.

Writings, Regrets, and Re-skillings during the AI Revolution

Author: Professor Lamb

  • Digital Literacy Is a Terrible Name for a Useful Class

    Like I said yesterday, I have over five degrees. Five shiny paper diplomas with my full name in cursive. Yet many of the skills that have mattered most in my professional life were not learned in a college classroom.

    I learned them on the job—sometimes because I needed to get my own work done and sometimes because it was my job to help someone else get theirs done. While working in IT technical support, I had to troubleshoot the everyday problems people encountered in Microsoft Word, Excel, PowerPoint, and Outlook. Supporting those applications required me to earn Microsoft Office Specialist certifications in all four. Some of those skills came from formal certification training. Many came from using the software, getting stuck, solving problems, and helping other people solve theirs.

    In other words, I learned by doing. That is also what students do in ITE 152. Unfortunately, the official name of the course is “Digital Literacy.” I have never liked that name. It sounds like robots have taken over an elementary school.

    “Digital literacy” is also too vague. It obscures the practical value of the course. A better description would be “Technology Skills for Office Work” because we teach students how to use the productivity and collaboration software they are likely to encounter while applying for jobs and working in professional environments.

    The course is opinionated about its tools: we teach the Microsoft suite. Google, LibreOffice, Apple, and other alternatives exist, but Microsoft applications remain standard equipment in many medium-sized and large organizations.

    More importantly, we do not merely talk about the software. Students use it. They create and format documents. They organize and analyze information in spreadsheets. They build presentations. They manage files, communicate, collaborate, troubleshoot problems, and learn how to figure out unfamiliar features.

    Employers often expect workers to arrive with these abilities, even though many students have never received systematic instruction in them. I did not learn all of them in school either. In some cases, I had to develop them while being paid to support other people who didn’t know how to use them.

    I earnestly believe this will be one of the most useful courses you take in college. What you gain is not simply a set of concepts but a set of practiced habits. By the end, you will have built muscle memory and procedural know-how—practical, bill-paying skills that help you create polished work, solve everyday problems, and present yourself professionally.

  • I Am Overeducated. That Is Not the Same as Smart.

    My name is Professor Lamb, and I am probably more credentialed than 99 percent of the people on the planet. That is not a boast. It may be more of a liability.

    I have two bachelor’s degrees—one Bachelor of Arts and one Bachelor of Science—two master’s degrees in very different fields, and a Ph.D. That is before we get to the certificates, certifications, and other pieces of paper I have accumulated.

    None of this means I am smarter than 99 percent of people. I am definitely not. It mostly means that I became very good at school.

    Most of the time I spent being lectured at, studying, and sitting in classrooms was probably wasted. I have forgotten at least 80 percent of what I learned, probably more. There are entire classes from which I cannot recall a single useful fact. Don’t ask me to account for all the hours I spent studying Latin, German, Welsh, and other languages I never learned to read or speak well, despite honest efforts. I am definitely not a polyglot, despite deep cosmopolitan appreciation for other cultures and languages.

    Of course I am not anti-education. Quite the opposite–I’m a Professor of Information Technology after all. I love learning new things, and I hope I remain curious for the rest of my life. I enjoy teaching students about technology, preparing content and planning lessons. Getting paid for this is a true honor and privilege that I do not take for granted.

    But “regrets” is in the blog tagline, so here’s one: I wish I had spent more time becoming good at things I loved—developing skills that brought me passion, joy, independence, and the ability to make things useful and delightful. Education can be valuable. Credentials can be valuable. But neither is the same as growth, developing good judgment, or becoming skilled at something that brings you—and perhaps other people—real joy.

  • Hello, blog!

    Every blog starts with a post nobody reads. This is that post. Consider it the sound check.

    I’m Professor Lamb. I teach information technology — networking, data structures and algorithms, artificial intelligence — at a community college. IT is my second career. The first one gave me a doctorate and a set of instincts about how knowledge gets made, which turns out to be either an enormous advantage or an expensive detour, depending on the week.

    What the subtitle means

    Writings. Writing is how I think, and thinking in public is the whole point of a blog. Expect drafts, revisions, and the occasional retraction.

    Regrets. Not wallowing — accounting. Career changes have real costs, and pretending otherwise is bad advice dressed up as encouragement. I want to be honest about the ledger.

    Re-skillings. Plural on purpose. I re-skilled once already, deliberately and slowly. Now the ground is moving under everyone at once — me, my students, and the field I teach.

    What you’ll find here

    Notes from the classroom about what actually helps people learn hard technical material. Reports from the field on where AI genuinely changes the work and where it’s mostly vendor noise. Career-change writing for people considering the leap, already mid-leap, or quietly regretting one. And the occasional post about how I’m using these tools myself — including the ones that helped build this site.

    Bias toward evidence over forecasts. Bias toward specifics over vibes. If I make a claim, I’ll try to tell you how I know.

    That’s the sound check. More soon.