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

Author: Professor Lamb

  • Operate, Evaluate, Critique

    Before digital literacy became a course title, three older literacy movements were already arguing over what people needed to know. Remember, ‘literacy’ in modernity has come to mean whatever baseline competence is sought to be made mandatory through mass schooling (as discussed in a previous post).

    1. Computer literacy emphasized the ability to operate machines and software.
    2. Information literacy emphasized the ability to evaluate sources and use information intelligently.
    3. Media literacy emphasized the ability to critique messages, representations, institutions, and power.

    Those three histories eventually converged under the broad label digital literacy. They did not disappear. A shorthand for the resulting structure is:

    Operate. Evaluate. Critique.

    This is not an official framework inherited intact from one scholar or organization. It is the historical pattern of contemporary “X-literacy” movements (e.g., AI Literacy, Algorithmic Literacy, etc.) as well as a way of seeing the three older literacy movements still alive and well under the banner of “Digital literacy.”

    Operate: The Computer-Literacy Tradition

    Computer literacy emerged during the 1970s as computers began moving beyond military, scientific, and corporate specialists.

    Its first question was practical:

    Can you make the computer do something?

    Even that question contained an argument. Some advocates believed ordinary people should learn programming so they could control the machine and use it as a medium for thought. Seymour Papert’s Mindstorms represents this more ambitious tradition.

    Fortunately, for most, another version eventually became dominant: learn to operate commercially available software. Open the application. Create the file. Format the document. Enter the formula. Save the work. Print it, attach it, upload it, or share it. This is the tradition I teach most directly in ITE 152. Students use Word, Excel, PowerPoint, file-management tools, communication systems, and other workplace technologies. The work is procedural, but that does not make it trivial. A person who cannot operate the required tools may be locked out of jobs, education, health care, banking, government services, and other ordinary parts of contemporary life.

    Operation is real literacy because interfaces regulate access. But operation is also the strand institutions find easiest to teach, observe, and test (SIMnet, anybody?). A student either produced the spreadsheet or did not. The formula works or it does not. The document satisfies the specifications or it does not. That measurability gives operation a structural advantage over the other two verbs.

    Evaluate: The Information-Literacy Tradition

    Information literacy entered the vocabulary in 1974 to crystallize people’s ability to use information resources to solve problems. Accessing information is not the same as understanding it. Finding a source does not establish that the source is reliable. Receiving an answer does not mean the answer is true. Librarianship later developed information literacy into a broader educational project concerned with searching, comparing sources, assessing authority, tracing claims, and using information ethically. Its central question became:

    Should you trust what you find?

    Evaluation requires more than spotting obviously fake websites. A source may be accurate but irrelevant. It may be authoritative in one context and inappropriate in another. It may contain valuable evidence while still reflecting the interests, assumptions, and limitations of the institution that produced it.

    The Association of College and Research Libraries now describes authority as “constructed and contextual.” That is a considerable distance from a checklist that tells students a .gov source is good and a blog is bad. Evaluation means exercising judgment under conditions where authority is real but never automatic.

    Critique: The Media-Literacy Tradition

    Media literacy has the oldest of the three lineages. It developed through twentieth-century arguments about propaganda, advertising, popular culture, television, news, and the concentration of communicative power. Its question is not merely whether a message is factually correct. It asks:

    What is this message doing—and who benefits?

    Who produced it? Who paid for it? What audience does it imagine? What emotions does it activate? Whose experience does it represent? Who is missing? What behavior does the medium encourage? How do ownership, advertising, algorithms, and institutional interests shape what can be said? Evaluation examines a claim. Critique examines the system that made the claim visible, credible, profitable, or difficult to escape.

    Media literacy has sometimes been defensive: protect people from propaganda, manipulation, vulgar entertainment, or misinformation. Its more critical traditions go further. They treat audiences as participants who can interpret, challenge, and create media rather than merely receive it. This is the strand most likely to make an institution uncomfortable because the institution itself becomes available for analysis.

    Digital Literacy Braids the Three

    By the time Paul Gilster published Digital Literacy in 1997, these traditions were already flowing.

    A. Computers supplied the operational problem.

    B. Networked information supplied the evaluation problem.

    C. Digital media supplied the critical problem.

    Gilster’s phrase succeeded partly because it was spacious enough to hold all three. Governments, schools, libraries, employers, and technology companies could agree that people needed “digital literacy” without agreeing about what should dominate inside it. That vagueness remains functional.

    A software company can use digital literacy to mean product adoption. An employer can use it to mean workplace readiness. A librarian can use it to mean source evaluation. A media scholar can use it to mean understanding platforms, representation, ownership, and power.

    Everyone can support digital literacy while imagining a different literacy! The framework becomes a tacit battleground among schemas and metaphors. Operation is easiest to demonstrate. Evaluation is harder because judgment is contextual. Critique is hardest because it questions the purposes and interests of the systems providing the tools. This explains the usual shape of institutional digital literacy. A course may devote weeks to operating applications, a shorter unit to evaluating online information, and perhaps one discussion to privacy, algorithms, labor, ownership, or the political economy of technology.

    Three Questions for Any Technology

    The triad gives us a simple way to interrogate a curriculum, a competency framework, or a new technology.

    A. Operate: Can you use it to accomplish a real task?

    B. Evaluate: Can you judge the quality, accuracy, and suitability of what it produces?

    C. Critique: Can you examine the interests, assumptions, and power relations built into the system?

    Let’s apply this to the latest invented X-literacy: AI Literacy. Operating means writing prompts, supplying context, choosing models, working with files, and incorporating output into a practical workflow. Evaluating means checking claims, testing code, inspecting sources, recognizing hallucinations, and deciding whether the output is suitable for its intended purpose. Critiquing means asking where the training data came from, whose labor supports the system, which languages and perspectives it privileges, who owns the infrastructure, who absorbs its environmental costs, and why an institution wants the technology adopted.

    A person can operate an AI system without being able to evaluate its answers. A person can evaluate individual answers without critiquing the system producing them. Each verb reveals a different kind of competence.

    Two Triads, Two Purposes

    I have previously defined digital literacy as using technology effectively, efficiently, and responsibly. That triad still works. It describes the qualities of competent practice. Operate–evaluate–critique does something different. It describes the historical layers inside the literacy itself.

    The two can be combined:

    • Operation should be effective and efficient.
    • Evaluation is necessary for responsible use.
    • Critique expands responsibility beyond the immediate user and task to the institutions, communities, workers, and environments affected by the technology.

    One triad asks how well we perform tasks. The other asks how deeply we understand what we are doing.

  • The Two Pauls of Contemporary Literacy Claims

    Paul Zurkowski coined information literacy in 1974. Paul Gilster popularized digital literacy in 1997. Neither man invented the underlying activities. People evaluated information long before 1974, and they used computers before 1997. What Zurkowski and Gilster gave us were names—portable labels that governments, schools, libraries, and employers could attach to a collection of skills they increasingly expected people to possess.

    The twenty-three years between their two definitions take us from a government report written before the personal-computer revolution to a book written for the early Internet. The technology changed enormously, as it is again today. Yet their problem framing is more relevant than ever. Both Pauls were asking some version of the same question.

    The First Paul: Information Is Not Knowledge

    Paul G. Zurkowski was president of the Information Industry Association when he submitted a report to the U.S. National Commission on Libraries and Information Science in 1974. The report had the unforgettable title The Information Service Environment Relationships and Priorities.

    Buried inside was the first usage of the phrase: information literacy. Zurkowski described information-literate people as those who had learned to use information resources and tools to solve problems. His important verb was molding. An information-literate person does not merely receive, repeat, or store information. The person works on it—molds it productively—selecting, evaluating, combining, and reshaping it into something useful. Or, in Zurkowski’s wonderfully direct formulation:

    Information is not knowledge.

    Possessing information does not automatically entail mature processing of information. Information only becomes knowledge through human attention and judgment. It enters a person’s field of perception, where it may be evaluated, assimilated, rejected, or used to change how that person understands reality and acts within it.

    Zurkowski was writing in 1974. Most people had never touched a computer. Yet he warned about an overabundance of information that exceeded our capacity to evaluate it. His concern was not simply that people lacked access. He feared that society would divide into two groups: a relatively small number of information-literate people who knew how to find, evaluate, and apply information, and a much larger group who did not. The latter might be perfectly capable of reading and writing, have access to libraries, newspapers, televisions, databases, and eventually the Internet, and still lack a reliable way to determine what information was valuable and how to use it. Zurkowski warned the United States to undertake a national effort to achieve universal information literacy by 1984

    Still working on it.

    The Second Paul: Ideas Before Keystrokes

    Twenty-three years later, Paul Gilster published Digital Literacy. By 1997, computers had escaped the research laboratory and corporate data center. They were entering homes, schools, offices, and libraries. The World Wide Web was turning information retrieval into an everyday activity.

    Gilster defined digital literacy as the ability to understand and use information presented through computers in multiple formats and from many different sources. That definition widened the problem. Digital information was not confined to conventional prose. It arrived through websites, hyperlinks, images, discussion groups, databases, and other emerging forms. A person had to understand not only the information itself but also something about the systems delivering it.

    Gilster’s best line was also his simplest:

    Digital literacy is about mastering ideas, not keystrokes.

    That sentence should probably be taped above every computer-lab door. Schools have repeatedly confused computer literacy with learning where the buttons are. Click here. Select that menu. Pass the test. But ideas last longer: Can you formulate a useful search? Can you decide whether a source deserves your trust? Can you recognize when an algorithm is trying to manipulate your attention? Can you synthesize fragments of information into a defensible conclusion? Can you notice when an answer sounds confident but does not make sense? Those are not point-and-click skills. They are habits of critical thinking.

    The Difference Between the Two Literacies

    Information literacy and digital literacy are often treated as synonyms. They overlap heavily, but they entered the world through different doors. Zurkowski was concerned with using information resources to solve problems. His context was professional work, government policy, libraries, and the rapidly growing information industry.

    Gilster was concerned with understanding information encountered through computers and networks. His context was the early consumer Internet, where ordinary people were suddenly navigating a decentralized information environment without librarians, editors, or other traditional gatekeepers standing between them and the source.

    Put simply:

    Information literacy asks whether you can evaluate and use information.

    Digital literacy asks whether you can do that when the information arrives through computer software applications and algorithms.

    The distinction is useful, but the lineage matters more. Gilster’s digital literacy carries Zurkowski’s information literacy into a networked environment. Zurkowski supplied the judgment problem. Gilster placed that problem on a screen.

    We are now repeating this history with AI literacy. Once again, institutions are rushing to define a new literacy. Once again, vendors are eager to sell the tools, training, certifications, and assessments that supposedly produce it. Once again, employers are beginning to shift the burden onto individuals: learn the new systems, adapt to the new workflow, and continually re-skill if you wish to remain employable.

    The interface may be new, but the problem is not. Zurkowski named the problem. Gilster named its digital form. Together, they remind us that modern literacy is not primarily about access, possession, or mechanical operation. It is about evaluation, judgment, and the ability to turn information into understanding and responsible action.

    Further Reading

  • Toward a Genealogy of Digital Literacy

    “Digital literacy” is a young term poured into a very old mold. That mold—literacy understood as an individual, measurable, moralized, and schoolable attribute—took roughly three centuries to construct. By the time the World Wide Web arrived, multiple distinct literacy movements were already underway, ready to be fused into something that appeared new. Here is the historical excavation.

    The “literacy” category has never named a stable skill. In medieval Latin, litteratus meant learned in Latin; a merchant who read the vernacular fluently could still be considered illitteratus. “Literacy” has always named whatever competence dominant institutions recognize and certify. That semantic mobility is what eventually allows literacy to migrate to screens, data, and machines without anyone pausing to notice that the container has changed contents (again with AI today).

    But first “literacy” itself had to be invented as something governable. Literacy campaigns predate mass schooling though and do not necessarily require it. The baseline competence being taught reflects the purposes of its institutional sponsor (e.g. the Church, the nation-state, big tech, etc). Nineteenth-century censuses often used the ability to sign one’s name as a proxy and the growth of compulsory schooling turned 3 R’s literacy (reading, writing, arithmetic) into a population statistic. It became an object of ‘governmentality’ in Foucault’s sense: something the state could know, compare, classify, and remediate. Without the statistic, there could be no measurable “divide”; without the divide, no population-level policy problem or lever for intervention.

    In The Literacy Myth (1979), Harvey Graff gave a name to the durable belief that literacy itself produces economic growth, social mobility, and individual advancement. The historical record repeatedly complicates that story: literacy has often followed economic transformation rather than caused it, and literacy campaigns have frequently served discipline and social control alongside—or instead of—emancipation. Literacy also has a dark institutional twin: gatekeeping. Jim Crow voting tests and the Immigration Act of 1917 used literacy to determine who could participate, who could enter, and who could be excluded. Deficit labels do not merely describe people; they sort them. Keep this history in view when “digitally illiterate” later emerges as a category of employability. Nevertheless, every subsequent “X-literacy” inherits the causal story more or less intact. It becomes the rationale for funding, assessment, and compulsory re-skilling.

  • Digital Literacy Is a Moving Target

    Digital literacy did not begin as a single idea, definition, or policy framework. Long before the phrase became institutionalized and standardized in education, people were already learning how to operate computers, retrieve electronic information, interpret screen-based media, participate in Internet-connected networks, and understand/use automated systems.

    What emerged in the late 1990s and the aughts was not the activity itself, but a new way of classifying heterogeneous practices as a general, teachable, assessable form of skill. The decisive historical change was this: Computing ceased to be treated only as specialist occupational knowledge and began to be treated as a normal condition of education, employment, citizenship, and everyday life.

    Increasingly, routine participation in society presupposes some level of digital competence: paying taxes, driving a vehicle, receiving retirement benefits, or accessing health care now often requires navigating online portals, verifying identity through digital systems, or submitting forms electronically. When was the last time you applied to a job using a paper application and pencil? Even disability services and insurance claims increasingly mandate some level of digital interaction, from uploading documentation to managing accounts through web-based platforms.

    Calling the resulting competencies a “literacy” gave them the status of a social minimum—something institutions (whether government or for-profit firms) could reasonably expect everyone to acquire. But labeling and schematizing something as a literacy is itself an exercise of cultural and institutional power: it defines which competencies count as basic, who is judged deficient, and who must adapt. In the workplace, the steadily rising baseline of technological competence has repeatedly forced workers to re-skill simply to remain employable, transferring much of the burden of technological change from institutions onto individuals and unpaid labors.

    Community colleges also increasingly function as flexible training infrastructures for rapidly shifting labor markets. As employers and industries redefine “entry-level” expectations around digital proficiency, community colleges are tasked with remediating skill gaps or recalibrating to new skill demands as the schematizing cycle now repeats itself with “AI literacy.” Witnessing this repetition makes visible how quickly “literacy” becomes a moving target rather than a stable foundation, as each new technological layer reclassifies prior competencies as insufficient or obsolete. In this sense, AI literacy does not break the pattern of digital literacy so much as intensify it, extending the same logic of continual adaptation into yet another domain of everyday life.

  • Digital Literacy Has Three Sides

    Here is the “Definition” the students crave. Write it down if you have to. Keep in mind though, that the definition isn’t the important part. The skills are.

    Digital literacy is knowing how to use technology effectively, efficiently, and responsibly.

    The Definition is tri-threaded: effectively, efficiently, responsibly. You can be strong in one thread and weak in another. You may possess one, two, or all three. They overlap, but they are not the same thing.

    Effectively refers to ability and capability. Can you use technology to accomplish a real professional task that you could not accomplish before?

    Efficiently, yes, refers to speed, the swiftness of delivery. Can you complete the task at a practical speed, repeat it reliably, or perform it at scale? In some cases, efficiency is the difference between getting something done and never finishing it. Some jobs literally will not hire you or keep you hired without a minimum level of efficiency.

    Responsibly is our little recursive sub-thread because it in turn refers to at least three things: safety, security, and accuracy. It means avoiding risky behavior, protecting yourself and other people, securing information, checking your work, and refusing to spread misinformation or disinformation. It brings together two subjects we address later in the course: cybersecurity and information literacy.

    Information literacy is the ability to search for information, research a question, evaluate sources, verify truth claims, and distinguish fact from fiction. You can be fast and technically capable but still use technology irresponsibly if you confidently spread false information or put someone’s security at risk (including your own).

    Effectiveness asks: Can you do it?

    Efficiency asks: How swiftly?

    Responsibility asks: Who is impacted? It states, quite explicitly and ethically, Do No Harm (to yourself or others).

    True digital literacy requires attention to all three.

  • 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.