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

Author: Professor Lamb

  • Plausible Physics Is Not Physical Understanding

    Series note. To mark the completion of my ECE 5864 Critical Engineering capstone, I’m publishing a seven-part adaptation for a special week of ITE 135: AI Awareness. These posts are longer and more formal than my usual entries because they began as a graduate research paper. This is Part 3 of 7.


    Assessment and Analysis

    Read critically, Ding et al’s most consequential contribution is the evidence it compiles against the field’s own marketing and hype. The product category is named for understanding the world, yet the authors document generative AI systems failing precisely at physical reasoning while succeeding at visual plausibility. That gap is diagnostic of what these systems optimize: a video generator is trained to make the next frame look right to a human eye, so gravity, friction, and thermal behavior are learned only insofar as they leave visible traces in pixels. Internal coherence is rewarded; correspondence to the world is, at best, a by-product. This gap could produce a new category of physical AI failure: not merely videos with broken physics, but robots whose plausible-looking predictions generate unsafe behavior. A hallucination on a factory floor, not to mention in a school, hospital, or road environment, may cost lives.

    Their own evidence also licenses a critique of the survey’s own central move. Ding and colleagues resolve the field’s definitional dispute by declaring understanding and prediction to be two functions of a single underlying idea. It is an elegant synthesis, but I think it is too slippery still. Current laboratory studies and frontier demonstrations suggest that the two functions come apart in practice: a system can be excellent at generating plausible futures while holding no rule-governed model of the present. If that is so, then ‘understanding’ and ‘predicting’ are not two aspects of one capability but two distinct claims. The shared slippery label permits a vendor to demonstrate the second while marketing the first.

    The survey’s “missing benchmarks” observation compounds this problem, and for the developer audience it is arguably the paper’s most important point: deployment of world-model systems is proceeding without an agreed method for validating opaque learned controllers across distribution shifts, model updates, and changing operating conditions. Industrial robotics is not devoid of safety governance; existing machinery standards address robot cells, guarding, protective functions, integration, and foreseeable human access. The unresolved gap is narrower but consequential: these instruments do not yet provide a canonical, model-specific method for establishing whether a learned controller remains reliable after fine-tuning, software updates, sensor drift, or exposure to conditions outside its training data. Without such evidence, a vendor demonstration can become a de facto performance test whose task, conditions, and footage are selected by the vendor.

    Two further limitations of the survey should be named. As a survey, the paper produces no original experiment; its findings are curated from others’ work, and its authority rests on the quality of its taxonomic synthesis rather than on evidence it generated itself. In a field moving this quickly, any survey’s shelf life is also short, and this one already predates several systems now central to public discussion, including the FLUX 3 lineage I take up in the next installment, my Use Case Analysis.

    Ding and colleagues provide definitional clarity and the taxonomy that discussion of world models has so far lacked. The state of the field they document nevertheless remains premature: researchers have demonstrated potential, but not safety or reliability, and quality-oriented benchmarks remain fragmented. Once again, Silicon Valley’s “move fast and break things” ethos is entering a high-impact domain with physical consequences before civil society and government institutions have developed adequate model-specific assurance and accountability mechanisms.

    Reference

    Ding, Jingtao, Yunke Zhang, Yu Shang, Yuheng Zhang, Zefang Zong, Jie Feng, Yuan Yuan, et al. 2025. “Understanding World or Predicting Future? A Comprehensive Survey of World Models.” ACM Computing Surveys 58 (3): Article 57.

  • What Is a World Model?

    Series note. To mark the completion of my ECE 5864 Critical Engineering capstone, I’m publishing a seven-part adaptation for a special week of ITE 135: AI Awareness. These posts are longer and more formal than my usual entries because they began as a graduate research paper. This is Part 2 of 7.


    Technical Summary

    A. Introduction

    In “Understanding World or Predicting Future? A Comprehensive Survey of World Models,” Jingtao Ding and eleven colleagues at Tsinghua University (published in ACM Computing Surveys 58, 3, 2025) provide the first comprehensive peer-reviewed survey of world-model research. Its central move is to resolve the term’s contested definition by organizing the entire literature around two functions: i. building internal representations that help a machine understand the world and ii. predicting future states of the world to guide decisions.

    B. Description

    The problem that Ding et al. address is that “world model” has come to mean different things to different research communities, all hiding beneath the marketing hype. One lineage, descending from Ha and Schmidhuber’s 2018 work and from model-based reinforcement learning, treats a world model as a compressed internal representation that an agent consults to make decisions. Ha and Schmidhuber used learned latent representations to produce the first reported solution of the CarRacing benchmark. In a separate VizDoom experiment, they trained a policy entirely inside an environment generated by the model and transferred that policy successfully to the original game (Ha and Schmidhuber 2018).

    A second lineage, energized by video generators such as Sora and FLUX, treats a world model as a simulator that renders plausible future states of the physical world. The “Joint Embedding Predictive Architecture,” a.k.a. JEPA, proposed by Yann LeCun, former Chief AI Research Scientist at Meta, argues that a machine should both represent the world abstractly and use that representation to imagine outcomes before acting. The embedding in that name points to a mechanism both lineages increasingly share: instead of learning only from text data or reproducing every image pixel, many current systems compress video, lidar, motion, and other sensor data into a ‘latent’ or abstract space, which consists of numerical vector representations intended to preserve important event information and dynamic relationships while omitting overwhelming raw detail. Prediction then happens in that compressed space rather than at the pixel level, which is what makes the approach computationally tractable at scale, but also what makes its omissions consequential.

    The authors conduct a systematic review in three passes. First, they survey the internal-representation branch: world models inside model-based reinforcement learning agents, and the more recent finding that large language models (LLMs) appear to acquire latent world knowledge, including spatial and temporal structure, without being explicitly trained for it. Second, they survey the future-prediction branch, tracing a progression from video generation toward interactive embodied environments, from producing footage of the world to producing worlds an agent can act in. Third, they examine three application domains (autonomous driving, robotics, and social simulacra), showing that each domain draws on their two functions in different proportions. The authors attempt to unify these two fields as two functions of one underlying idea: understanding the present and predicting the future.

    The paper closes with an open problem and findings that are largely negative. Current video generators achieve visual realism but fail systematic tests of physical law: the authors cite diagnostic studies showing errors in gravity, fluid, and thermal dynamics, and evidence that scaling produces case-based rather than rule-based generalization. So far world models ‘memorize’ situations without inducing the physics beneath them. Finally, the authors document that evaluation is fragmented: because the field pursues divergent goals with heterogeneous methods, no canonical benchmark or metric for “being a good world model” yet exists.

    References

    Ding, Jingtao, Yunke Zhang, Yu Shang, Yuheng Zhang, Zefang Zong, Jie Feng, Yuan Yuan, et al. 2025. “Understanding World or Predicting Future? A Comprehensive Survey of World Models.” ACM Computing Surveys 58 (3): Article 57.

    Ha, David, and Jürgen Schmidhuber. 2018. “Recurrent World Models Facilitate Policy Evolution.” In Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2450–2462.

  • World Models: The Next Frontier of AI Hype

    Series note. To mark the completion of my ECE 5864 Critical Engineering capstone, I’m publishing a seven-part adaptation for a special week of ITE 135: AI Awareness. These posts are longer and more formal than my usual entries because they began as a graduate research paper. This is Part 1 of 7.


    Introduction

    World models are AI models that form—through massive and deep machine learning on video, vision, language, and robotics data—an internal representation of a physical environment’s state and dynamics to predict how that environment may change and evaluate possible actions within it. They are a key enabling approach for an emerging generation of physical-AI systems and robotics. They are also the latest frontier of Silicon-Valley-inspired hype and market speculation because they could enable so-called ‘agentic AI’ to control autonomous machines in real-world environments by making inferences, selecting actions, and predicting their effects. If the technology proves viable at scale, world models could enable robots, cobots, autonomous vehicles, and other physical-AI systems to replace some human labor or better collaborate with human workers. The potential impact is immense but remains largely speculative at present.

    Last month, the World Economic Forum selected world models as one of its Top 10 Emerging Technologies of 2026. Its report presents them as a foundation for AI systems that can plan, reason about, and act autonomously in physical settings, including robotics, industrial operations, climate modeling, and transportation (WEF 2026, 29–31). Unfortunately, their business-facing report glosses over a central critical-engineering risk: a world model may be coherent within its own representation yet still grossly misunderstand the world, wreaking havoc and harm in the environment. Critical errors and ‘bug’ defects may remain hidden until a world model is actually deployed and encounters real people/places/things outside its training environment.

    World models are an active area of research, development, and technology investment. Major engineering obstacles remain, including model degradation over time, stringent reliability requirements for systems operating around people and property, computational efficiency at scale, and high energy and environmental costs. Terminological confusion and a proliferation of hype-driven buzzwords further complicate analysis and assessment. None of these uncertainties has dampened enthusiasm: Silicon Valley is wagering heavily and publicly that world models will become both deployable and lucrative in the near future.

    This critical-engineering report is written for developers, technology managers, workers, and other public stakeholders who may be affected by or curious about emerging world-model and physical-AI systems. In the next installment, I summarize the most comprehensive peer-reviewed survey to date of the field’s competing definitions, main technical approaches, major application domains, and unresolved problems. I argue that “world model” currently functions as both a technical category and a marketing device. Vendors can slide rhetorically from a model’s ability to generate plausible representations to claims that it understands physical reality well enough to act safely in settings such as schools, malls, and elder care. Although world models show substantial promise for representing, predicting, and controlling spatiotemporal environments, limited demonstrations do not establish the calibrated reliability and accountability required in such settings. Given what is at stake in the natural and social world, these systems demand critical scrutiny before broad deployment.

    References

    WEF (World Economic Forum). 2026. Top 10 Emerging Technologies of 2026. Geneva: World Economic Forum, pp. 29–31 and 47–48.

  • Why Genealogy?

    Genealogy is the history of things that claim to have no history. It takes something that presents itself as natural, timeless, or unified—a value, an institution, a category of person—and shows it to be an assemblage built piecemeal out of contingent struggles, whose current meaning is a late capture rather than an original purpose. Standard histories narrate the development of given subjects. Genealogy asks how those subjects came to be given in the first place. And it writes in order to unsettle the present, not to commemorate the past.

    Friedrich Nietzsche (1844–1900), the German philosopher who started out as a professor of classical philology, coins the modern sense in On the Genealogy of Morality (1887). His target is morality itself—guilt, conscience, altruism, the ascetic ideal—things his contemporaries treated either as eternal or as tidy products of usefulness. His question is not “what is good?” but “how did good get made, by whom, against whom, and what is this value worth for the life that holds it?” qua an inquiry into the value of values. Nietzsche’s genealogy is openly a polemic. He writes it to diagnose the present — European nihilism, the sick animal called man — and to prepare a revaluation. It is history in the service of a fight, and it says so.

    Michel Foucault (1926–1984), the French philosopher-historian who held a chair at the Collège de France he named “History of Systems of Thought,” turned this into a research program. His methodological manifesto is the 1971 essay “Nietzsche, Genealogy, History”; the worked examples are Discipline and Punish (1975, on the birth of the modern prison) and The History of Sexuality, volume one (1976). Foucault fastens on Nietzsche’s German vocabulary. Genealogy rejects the search for Ursprung, a pure origin where a thing’s essence was already present waiting to develop, in favor of Herkunft, i.e. descent (the tangled, accidental lineages, tracked through bodies and through what he calls gray, meticulous, patiently documentary archives), and Entstehung, i.e. emergence (a thing arises as an episode in a struggle, and whoever currently holds it holds it the way one holds captured ground). To this he adds the power/knowledge axis: the objects we think we simply know—madness, delinquency, sexuality, even the inner soul—are constituted by mundane techniques like timetables, examinations, case files, and confession, rather than discovered lying around. And he names the genre’s temporal direction:

    Genealogy is a history of the present.

    It starts from something contemporary whose self-evidence bothers you and traces the contingent assembly that produced it until it stops looking necessary.

    Now contrasting genealogy with ordinary history-writing comes down to four things:

    1. What counts as a unit. Biography, intellectual biography, and political history receive their objects ready-made—a person, an oeuvre, a nation, a war—and narrate their development; the object anchors the story. Genealogy makes the object’s very existence the puzzle: not the life of a delinquent, but how “the delinquent” became a kind of person one could be; not the biography of an author, but how “the author” became a function we attach to texts (Foucault has a famous essay on exactly that). Biography in particular presupposes the thing genealogy interrogates—a continuous self whose intentions explain outcomes. For Foucault, the modern individual with an examinable interior is itself a product of disciplinary practice: “the soul is the prison of the body.”
    2. The shape of time. Picture conventional history as a single confident arrow: origin, development, arrival—the present as destination, faintly justified by the journey. Genealogy is the opposite shape: a tangle of thin lines converging on the present from scattered points labeled accident, reversal, appropriation, forgotten struggle. History’s continuity and teleology versus Genealogy’s discontinuity and contingency: the present as one outcome among possible others, held in place by force and habit rather than destiny.
    3. The Posture. Professional historiography since Leopold von Ranke—the nineteenth-century German historian who gave the discipline its ideal of archival neutrality, showing the past “as it actually was”—aspires to a view from nowhere. Genealogy denies there is one. It is openly perspectival, written from a present stake, and its success is measured not by completeness but by whether it loosens the seemingly self-evident. Foucault liked saying the point was to show things weren’t as necessary as all that. This also marks the difference in function: political history often explains or legitimates (the nation’s story, the institution’s founding), while genealogy problematizes.
    4. The Evidence. Not the correspondence of great men nor their ideas, but regulations, drill manuals, architectural plans, confession handbooks—power’s small print.

    Two caveats to keep in mind. Exposing something’s “low origins” doesn’t by itself refute a value—that would be the genetic fallacy. Nietzsche’s real question is what a value does to the life that holds it, and Foucault’s is what contingency reopens. Relatedly, genealogy needn’t debunk at all: Bernard Williams (1929–2003), the British moral philosopher, wrote a vindicatory genealogy of truthfulness. The same method of genealogy is now deployed to show why a value deserves keeping.

  • Digital Literacy From Above and Below

    A student can fail a digital literacy assignment while being digitally fluent in a world the instructor cannot even see. The student may struggle to format a Word document, attach the correct file to an email, or organize a spreadsheet. At the same time, that student may move effortlessly through online communities governed by their own symbols, references, genres, reputations, and unwritten rules.

    The institution recognizes the first set of practices as digital literacy. It creates course objectives for them, purchases textbooks about them, measures them with rubrics, and eventually certifies that the student possesses them.

    The second set may remain nearly invisible. No institution designed it. No professor controls it. No credential recognizes it. Yet it is still learned, practiced, policed, and used to determine who belongs.

    One literacy comes from above. The others grow from below.

    The Autonomous Model

    In Literacy in Theory and Practice (1984), the British anthropologist Brian Street distinguished between autonomous and ideological models of literacy.

    The autonomous model treats literacy as a neutral, self-contained skill. A person either possesses it or does not. Once acquired, it can supposedly be transported from one situation to another and will produce generally beneficial effects: better reasoning, greater employability, economic development, and fuller participation in society.

    This is the model institutions find easiest to use. If literacy is a stable skill, it can be defined. If it can be defined, it can be divided into competencies. Competencies can become learning outcomes. Learning outcomes can be assessed. Assessments can produce scores, certificates, course credits, and categories of people.

    The autonomous model turns literacy into something an institution can administer. Digital literacy is frequently presented this way. Students are expected to acquire a standard collection of abilities that will supposedly travel with them into college, employment, and civic life. The applications may change, but the institution still imagines a transferable substance called “digital literacy” residing inside the individual.

    This model is useful. I make part of my living teaching within it. It is also incomplete.

    The Ideological Model

    Street’s ideological model begins somewhere else. Literacy is not merely a technical ability stored inside an individual. It is a social practice embedded in particular relationships, institutions, purposes, histories, identities, and distributions of power.

    That means literacy is plural and lowercase: many literacies, not The Literacy (i.e., autonomous model). The literacy used to complete a tax form is not the same practice as the literacy used to interpret a religious text, participate in a group chat, maintain a medical chart, write computer code, create a meme, or decode the emotional politics of an Instagram comment section. These practices may share tools. They may all involve reading and writing. But they do not operate according to one universal set of rules.

    The Netflix series Adolescence gives us a disturbing example of vernacular digital literacy. Detective Inspector Bascombe and the other institutional adults can see the emojis exchanged beneath social-media posts, but they cannot interpret what the symbols mean within the young people’s social world. Bascombe’s son has to explain the coded layer. The adults can recognize the characters on the screen. They are not technically unable to read them. What they lack is membership in the community that gives the symbols their meaning.

    An emoji does not carry one permanent definition. Meaning depends on who is using it, where it appears, what has already happened, and which community is reading it. A heart may be affection in one context, mockery in another, and membership in a coded ideology somewhere else. The scene dramatizes Street’s argument powerfully. Literacy is not simply decoding a visible sign. It is knowing what that sign does inside a particular social relationship.

    The adults’ illegibility is also partly functional. Vernacular codes help communities recognize insiders and exclude outsiders. Young people continually create language that parents, teachers, police officers, employers, and algorithms cannot immediately interpret. If the adults master it, the community modifies it, abandons it, or moves elsewhere. This does not make vernacular literacy innocent or inherently progressive. A community-generated literacy can sustain friendship, creativity, humor, and mutual aid. It can also transmit misogyny, racism, conspiracy theories, cruelty, and violence.

    The Literacies an Institution Can See

    A college sees the literacies it is equipped to recognize. It can see whether a student formatted a document correctly, selected a suitable chart, cited a source, or submitted a file in the required format. Those practices are visible to the learning-management system and legible to a rubric. The college may not see the student translating for family members, administering an online community, comparing sellers across digital marketplaces, editing videos on a phone, maintaining a multiplayer-game guild, or interpreting the shifting language of a peer network.

    These activities do not automatically satisfy the objectives of a digital literacy course. They are not substitutes for the professional codes students came to college to learn. But they are evidence that students do not arrive without literacy. They arrive with literacies the institution may not recognize.

    That changes the instructional problem. The question is no longer:

    “How do I give literacy to people who lack it?”

    The better questions are: Which literacies do my students already practice? Which literacies does the institution require? Who benefits from those requirements? Which additional codes will give students greater agency? And how can I teach an official code without pretending it is the only legitimate one?

    Students deserve explicit access to the sanctioned literacies that control employment, education, and public life. Refusing to teach those codes does not weaken their power. It merely reserves them for people who learned them somewhere else. I should teach students how to write the professional email, build the spreadsheet, evaluate the source, and navigate the institutional portal. These are real, practical, sometimes bill-paying skills.

    But I can teach them honestly. I can say: This is the code employers currently reward. This is how the institution expects the document to look. These conventions are enforced, but they are not natural. You can learn them without mistaking them for proof of superior intelligence or moral worth.

    Digital literacy from above gives us standards, assessments, credentials, and access to institutions. Digital literacies from below give communities ways to communicate, create, resist, conceal, belong, and sometimes harm.

    Further Reading

  • 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.