文学能力是 AI 安全基础设施

Literary Competence Is AI-Safety Infrastructure

> By Li Xiaolai · 2026-04-30 · [Original link](https://lixiaolai.com/articles/2026-04-30/reading-stamina-as-the-core-of-future-education)

The message came back in forty seconds — three crisp paragraphs, no hedging, no ellipses. A working professional had asked an AI assistant to explain the thematic significance of a particular narrative technique in a novel, and the model answered with the fluency of a seminar leader: a claim about unreliable narration, two supporting details, a closing observation about the author's intention. Every sentence parsed. The vocabulary was right. The argument had the shape of an argument.

And yet something was off. The professional — an experienced AI user, someone who had learned to prompt well and verify often — sensed it. Not a factual error: the character's name was right, the plot point was correctly described. Something more like a seam showing at the join between two ideas. The second paragraph's central claim did not quite follow from the first. The "therefore" was load-bearing, and the load was not there. The conclusion arrived too cleanly, tying together threads that had not actually been braided.

The professional read it again. The unease persisted. But there was no procedure for narrowing it — no experiment to run, no citation to check, no counter-source to consult. The discomfort was real; the instrument for locating its source was missing. After a pause, the answer was accepted, forwarded, cited in a report that would reach other people.

The problem was not the AI's confidence. The problem was the evaluator's silence.

There is a gap between *receiving* an AI output and *evaluating* it — and that gap is not closed by knowing more about how AI works.

If the case for AI literacy as technical skill rested on intuition, this article would be easier to write. It does not.

Kestin and colleagues at Harvard ran a randomized controlled trial in which 194 undergraduate physics students were assigned either to a custom AI tutor or to active-learning instruction.¹ The AI tutoring group showed learning gains 0.63 to 1.3 standard deviations larger than the active-learning control — in two sessions. Students reported higher engagement and higher motivation. The study was peer-reviewed, published in *Scientific Reports*, and designed to prevent hallucination by using pre-written expert answers vetted for accuracy. These are not conditions that inflate results; they are conditions that test a real claim under reasonably rigorous controls.

The Kestin study is the best experiment in the field. An argument against AI tutoring that passes over this evidence has not done its work. The honest engagement begins with granting what it shows: AI tutoring, properly designed, efficiently delivers content knowledge in structured domains. The effect sizes are not marginal. The design is credible.¹

The institutional consensus behind this evidence is substantial. The World Economic Forum's 2025 AI Literacy Framework identifies core competencies as algorithmic thinking, prompt engineering, understanding AI bias, data literacy, and critical thinking about AI outputs.² Purdue University enacted an AI Working Competency graduation requirement in December 2025, the first major research university to do so.² These frameworks reflect genuine policy deliberation, not panic. They represent the considered position of institutions that have spent decades thinking about what students need to know.

The cost-asymmetry argument reinforces the case. Khanmigo grew from roughly 40,000 to 700,000 student users in a single year.³ One-on-one AI tutoring, available at no cost to a student without resources, placed against one-on-one human tutoring at $50 to $200 per hour — the equity argument for AI tools is real, and the cost differential is not marginal.

What the Kestin authors themselves wrote, however, matters precisely here. In their limitations section, they noted: "we do not presume that structured AI tutoring will always outperform in-class active learning in all contexts, for example, those requiring complex synthesis of multiple concepts and higher-order critical thinking."¹ The study tested understanding, applying, and analyzing — roughly Bloom's taxonomy levels one through four. The authors specified what remained outside its scope.

The question is not whether AI tutoring works at content delivery. It does. The question is whether content delivery is the binding constraint.

When someone says "I need to be able to evaluate AI outputs," they are describing at least three different problems. Most discussions of AI literacy elide this. The conflation matters, because the skills required for each problem are genuinely different — and the one that is hardest is the one that receives the least attention.

The first task is **stylistic authorship detection**: given a piece of text, was it written by an AI? This is a pattern-matching problem. The question is whether the prose has recognizable stylistic signatures of AI generation — the specific rhythmic flatness, the preference for certain connective phrases, the register that is simultaneously confident and toneless. This task has been studied directly.

The second task is **factual fabrication detection**: does this citation exist, does this statistic trace to a real study, does this historical event match the historical record? This is a verification task. The skill it requires is knowing *how to check* — database access, cross-referencing, source tracing. Methodologically, it is closer to fact-checking journalism than to literary analysis.

The third task is **reasoning-coherence evaluation**: does this argument actually hold together? Does the conclusion follow from the premises? Is this claim internally consistent with what was asserted two paragraphs earlier? Is the confidence of the assertion matched by the quality of the support? This is a different kind of work — not pattern-matching, not verification, but the evaluation of argumentative structure itself.

Farquhar and colleagues at Oxford published what is currently the most sophisticated automated approach to the third task: semantic entropy, which detects confabulation by having the model generate multiple candidate answers and checking whether they cluster around a consistent meaning.⁴ The method is elegant. It is also, unavoidably, a model-side operation: it requires access to the model's internal probability distributions and multiple generations. A person who receives a single confident output from a chatbot interface has none of this.

More importantly, the authors named a class of failures the method cannot reach: what they call "structural errors" — mistakes that are systematic and consistent across all of the model's outputs because they originate in what the model learned during training.⁴ These are precisely the failures most dangerous to end-users, because they are the ones the model produces with the most confidence and least self-correction. Semantic entropy cannot detect them. What is left, above the ceiling of automated detection, is human judgment.

A controlled study of this boundary — the German PMC11914838 experiment — is instructive. Thirteen humanities scholars and twenty-two medical professionals attempted to identify which of a set of German-language medical student essays were AI-generated.⁵ The medical professionals achieved 72% accuracy; the humanities scholars achieved 65%. The difference was statistically indistinguishable (OR 1.37, 95% CI 0.5–3.9). The humanities scholars rated ChatGPT prose as linguistically *higher quality* than the human student writing — not merely comparable: superior, by their literary judgment.⁵

The study found what it found, and it is worth understanding precisely what it tested: task one. Stylistic authorship detection in a non-literary domain, in German, on medical student essay prose. The literary training of the humanities scholars conferred no particular advantage at recognizing *which* text was AI-generated from surface features alone. The study did not test whether literary training predicts accuracy at evaluating whether the *argument* in a medical student essay holds together. It was not designed to; it measured something different.

The task distinction — and specifically the claim that literary training is relevant to task three — is not supported by a direct experiment. That experiment has not been run. This is the article's honest empty space: the structural argument for literary competence as the capacity for reasoning-coherence evaluation is grounded in mechanism, analogy, and convergent evidence, not in a controlled study that directly measures whether literary readers catch AI reasoning failures at higher rates than non-literary readers. The argument calls for that experiment. The absence of it is a gap that must be named, not concealed.

What the structural argument proposes is this: task three is what a careful reader of a densely argued work does — constantly, implicitly, over hundreds of pages — as a matter of practised habit. The evaluation of whether an assertion earns its confidence, whether a transition is warranted, whether a conclusion is genuinely supported or merely claimed, is not logic applied to isolated propositions. It is a cultivated sensitivity to argumentative texture, built by sustained exposure to writing that either demonstrates or fails to demonstrate these qualities at scale.

The binding constraint in AGI-era AI use is not content delivery. The binding constraint is task three — and task three is unsolved by automated tools and unaddressed by the technical-competency frameworks. What addresses it is a human capacity. The capacity has a name, and the research that describes it has been hiding in plain sight in the wrong department.

Maryanne Wolf, in a Medium / Thrive Global article, wrote: "Deep reading, like the reading brain circuit itself, is not a given; it is built by use, or it atrophies from disuse."⁶ The observation is not metaphorical. The deep reading circuit — the network of regions involving semantic, phonological, and pragmatic processing, along with working memory and analogical reasoning — assembles through practice. It does not arrive pre-built. Children who are given sustained, difficult text in the appropriate developmental windows build circuits that children who are not given such text do not build, or build incompletely. Wolf's clinical documentation of this in college students — who arrive at university increasingly unable to sustain long-form reading without losing the thread — is not an RCT, but it is expert clinical observation accumulated over decades of working with the specific population at stake.⁶

What does that circuit, once built, actually enable? Not retrieval. Not comprehension in the sense of knowing what the words mean. Something more specific and harder to name: the felt distinction between a text that is doing genuine argumentative work and a text that is performing argumentative confidence while carrying almost nothing. This is what Kyle Chayka, writing in *Behavioral Scientist*, calls connoisseurship — and his argument about algorithmic culture illuminates the reading problem from an unexpected angle.⁷

"What we gain with algorithmic feeds in terms of availability — having instant access to a broad range of material to be scanned at will — we lose in connoisseurship, which requires depth and intention."⁷ Chayka is writing about music, film, cultural objects generally — the way algorithmic recommendation systems flatten evaluative discrimination into passive consumption. But the mechanism he names is the same one at stake in AI-output evaluation: connoisseurship requires depth and intention, not access. "Having to consciously accrue a collection and think through what you enjoy most about a particular creator or body of culture means becoming a connoisseur."⁷ The keyword is *consciously accrue* — the deliberate, cumulative engagement that builds an internal standard that access alone cannot provide.

Wolf's circuit and Chayka's connoisseurship are describing the same process from different sides: the neural and the cultural account of what sustained engagement with difficult, excellent writing builds in the reader. The reader who has spent years with demanding texts — not merely consuming them, but wrestling with them, losing the thread and finding it again, tracing the architecture of arguments that earn their conclusions — builds something that an AI-assisted reader does not. Not knowledge. A calibration instrument.

The cognitive offloading literature makes the mechanism precise. Risko and Gilbert's foundational framework, confirmed by Grinschgl and colleagues across 516 participants in three experiments, established that cognitive resources released by offloading to external tools appear *lost*, not freed — they "do not contribute to the formation of memory" unless the learner has an explicit goal to learn.⁸ The student who uses an AI to draft an essay does not bank the cognitive effort; they simply do not do the work. The same logic applies to reading: a student who uses AI summaries in place of sustained reading does not free cognitive resources for higher-order analysis. They do not develop the circuit that higher-order analysis sits on.

The Lydiard analogy is illustration, not mechanism. Arthur Lydiard's foundational contribution to distance running was the recognition that base aerobic conditioning — slow, sustained, high-volume effort — enables specific performance work later. The structural logic maps to reading: substrate enables structure; inverting the ratio damages the substrate. No quantitative ratio from sports physiology maps cleanly to cognitive development. The analogy illuminates; it does not prove.

The direct experiment named in the previous section has not been run; what follows here is the structural argument. Wolf's circuit, Chayka's connoisseurship, and Risko and Gilbert's offloading framework converge on the same mechanism — and together they name what cannot be outsourced.

What cannot be outsourced is the calibration instrument itself — the internal standard for argumentative quality that no amount of access to AI outputs will build, and that AI-mediated reading actively prevents from developing, because the cognitive work that builds it requires engagement without the scaffold.

Robert Bjork's research on learning, replicated across decades and laboratories, converges on a finding that is both robust and counterintuitive: conditions that lend themselves to rapid performance gains often fail to support long-term retention, whereas conditions that seem to impede immediate learning tend to consolidate into durable capacity.⁹ This is the desirable-difficulties framework — the insight that productive struggle, interleaving, and spacing are not obstacles to learning but the mechanism of it. Retrieval practice, in the most carefully controlled studies, improves recall roughly 50% better than restudying the same material.⁹ Interleaved practice produces test performance of 63% versus 20% compared to blocked practice on equivalent tasks.⁹ The mechanism is not mysterious: difficulty forces the cognitive work that consolidates memory and builds transferable skill. Ease measures immediate performance without building durable capacity.

The Khanmigo case is a genuine architectural counter-instance. Khan Academy's AI tutoring tools include a Text Releveler that scaffolds canonical literary and historical texts for struggling readers, a Chunk Text feature that breaks down dense passages and encourages comparison between the simplified and original versions, and a Socratic-question approach that withholds answers and guides students toward reasoning rather than providing it.³ These are not friction-removers; they are friction-mediators. A scaffold that makes a challenging text accessible to a student who would otherwise be locked out is a different design from one that simply removes challenge. The article's critique is not of scaffolding as such. It is of what follows scaffolding — or rather, what currently fails to follow it.

Adaptive learning platforms, as documented in a systematic scoping review of the peer-reviewed literature, are designed around engagement optimization — real-time adjustment of teaching strategies to maintain learner engagement, and reduction of cognitive load as students progress, are the explicit design propositions of personalized adaptive systems.¹⁰ Bjork's research predicts what happens when learning is engineered for flow: short-term gains that fail to consolidate into the durable capacity that the harder, less comfortable conditions would have built. One study cited in the review found 88% of learners reporting flow states with personalized adaptive systems — and flow state is precisely the condition Bjork's desirable-difficulties research argues against. Flow is comfortable; learning, in the sense that builds durable capacity, is not comfortable. It is the adaptive systems' explicit business claim — seamless adjustment to maintain engagement — that puts them in architectural conflict with the productive discomfort that stamina requires. Faded scaffolds reduce engagement metrics. The business model does not reward fading.

The meta-analytic evidence grounds the architectural argument. Alrawashdeh and colleagues synthesized 27 studies across 12 countries and found an overall effect size of g=0.29 for personalized adaptive learning on reading literacy.¹¹ Modest, but not zero. The finding that complicates the picture: teacher-absent conditions showed larger effects than teacher-present conditions in the meta-analytic data.¹¹ This is counterintuitive — if adaptive learning supplements teacher instruction, teacher presence should help. The pattern suggests the measured gains are concentrated in lower-order, drill-and-practice domains where automated feedback excels and teacher-facilitated discussion is not needed. Higher-order comprehension — the kind that builds stamina, requires sustained difficulty, involves extended argument-tracking — is the domain where teacher presence matters most; it is also the domain where the meta-analysis cannot isolate effects because studies report outcomes inconsistently.

Cheung and Slavin examined the most widely deployed configurations specifically.¹¹ Comprehensive standalone EdTech programs — programs like READ 180 and Fast ForWord, deployed at scale in schools as standalone reading interventions — produced effect sizes of 0.04 to 0.06. Near-zero. The most widely deployed tools, in the most common school deployment pattern, show effects at the floor. The pattern fits the Bjork prediction exactly: the gains adaptive learning shows are concentrated where the technology drills lower-order, easily measurable skills. The higher-order, teacher-facilitated, productive-discomfort work — the work that builds reading stamina — is what the technology systematically removes from the learning environment, and it is that removal that matters for the capacity the AGI era requires.

The engagement metric and the stamina metric pull in opposite directions. The business model has selected for the wrong one.

The reading decline is older than the AI it now compounds.

Bone and colleagues analyzed the American Time Use Survey across 236,270 respondents over twenty years, tracking reading for pleasure from 2004 to 2023.¹² The proportion of Americans reading on a given day fell from 28% to 16% — a 43% relative decline, occurring at a rate of roughly 3% per year (the steepest single age-group decline was among adults 66 and older; the ATUS excludes online and screen reading, so the measure covers traditional and e-reader formats specifically). The SES gap widened substantially: postgraduate-educated readers were 2.79 times more likely to read daily than high-school-educated readers by 2023.¹²

The adolescent data comes from a different and complementary source. Twenge and colleagues analyzed four decades of the Monitoring the Future survey — roughly 50,000 students per year, over a million teenagers across forty years — and found that American 12th-graders reporting daily reading fell from approximately 60% in the late 1970s to 16% by 2016.¹³ Approximately one in three reported reading no books for pleasure in the year preceding the survey. The data window ends in 2016 — six years before the release of ChatGPT. The smartphone was the proximate driver of this collapse; the smartphone became ubiquitous between 2010 and 2014 in the adolescent population. AI inherits this trend; it did not originate it.¹³

What AI adds to a substrate already weakened is a different cost structure. When the substrate was strong, using AI for the wrong task was a productivity error. When the substrate is weak, using AI for the wrong task is an epistemic crisis — the user cannot catch what they used to be able to catch, and they do not always know they cannot.

The MIT Media Lab study names the cognitive mechanism, with appropriate hedging. Kosmyna and colleagues measured EEG brain connectivity in 54 participants across three writing conditions — unaided, search-engine-assisted, and LLM-assisted.¹⁴ The hierarchy was clear: brain-only condition produced the strongest alpha and beta network connectivity; LLM-assisted condition produced the weakest. Critically, when participants who had been assigned to the LLM condition switched to writing unaided in a fourth session, their brain connectivity remained below that of the brain-only group — "under-engagement" that persisted even after the tool was removed. The 83% figure is more robust than the connectivity-magnitude finding: 83% of LLM-using participants were unable to quote from essays they had written minutes before.¹⁴ The authors coined "cognitive debt" to describe the pattern. The study used n=54 (with n=18 completing the crossover session) and remains unpeer-reviewed as of this writing; the directional finding survives these constraints, but specific magnitudes should not be treated as settled.¹⁴

Gerlich's survey of 666 participants found a significant negative correlation between frequent AI tool use and critical thinking scores, mediated by cognitive offloading, with the 17–25 cohort showing the highest AI dependence and the lowest scores — a correlational pattern, not a causal demonstration, but consistent with the directional argument.¹⁵

The substrate had been weakening for two decades from non-AI causes. The AGI era raises the cost of the weakness. EdTech's dominant deployment, designed to remove productive discomfort and optimize engagement, accelerates both — and it does so at exactly the moment when the substrate's absence becomes most expensive.

The capacity this article defends is trainable.

A 14-year longitudinal study of 1,962 Taiwanese older adults found that reading at least once per week independently predicted a 46% lower risk of cognitive decline at 6, 10, and 14 years of follow-up, even after controlling for other cognitive activities — television, radio, games, socializing.¹⁶ The AOR was 0.54 (95% CI: 0.34–0.86), consistent across all education levels. The scope of the finding matters: the population is older adults (age 64+), and the finding addresses cognitive maintenance, not cognitive development in youth. The dose-response signal is specific to reading and genuine.¹⁶

The home library data is more sweeping. Sikora and colleagues analyzed the PIAAC dataset — 162,955 adults across 31 societies, sampled between 2011 and 2015.¹⁷ Growing up with 80 or more books in the adolescent home independently predicted higher adult literacy, numeracy, and ICT problem-solving skills — *beyond* the effect of parental education or the individual's own educational attainment. The effect was loglinear, with the greatest returns at smaller library sizes, and it persisted into working age across three continents.¹⁷ The channel by which this operates is neither mystery nor metaphor: early exposure to books, before most institutional instruction, builds the substrate that all subsequent cognitive work draws on.

The neurobiological grounding for this was confirmed in two separate studies of children. PMC9588575 documented SES-associated differences in reading-relevant brain regions — reduced left-hemisphere structural and functional connectivity, reduced cortical surface area in the reading network — linked to reduced access to books and lower parent-child reading frequency.¹⁸ PMC12309101 confirmed the mediation pathway in 1,534 Chinese students across grades one through six: the number of books at home and the age at which reading was initiated mediated the relationship between SES and reading ability.¹⁸ The causal direction is inferred from developmental logic, not from an experiment — but the biological correlates of book-poor environments are observable in childhood, not as speculation but as neuroimaging data.

These are equity findings with cognitive consequences. The reading-environment gradient is steep. Postgraduate-educated Americans read daily at 2.79 times the rate of high-school-educated Americans.¹² Wealthy children are more likely to grow up with book-rich homes. Institutional reading — mandated, demanding, with teachers present to facilitate the difficulty — was one of the few channels by which children without book-rich homes were required to engage with difficult text. The defensible claim is *exposure*, not *equalization*. Institutional reading curricula did not equalize outcomes; the SES gap persisted through fifty years of mandated instruction. Exposure to demanding text is necessary, not sufficient; the gap persisted because the institutional version was inadequate, not because the exposure itself was misguided.

The Bourdieu critique — that literary education has functioned historically as a class-stratifying mechanism, marking cultural competence as a proxy for belonging — deserves engagement rather than dismissal. The record of how demanding reading curricula have been deployed institutionally makes the critique descriptively accurate. The article's claim is not to preserve the canon but to preserve the *commitment* to sustained difficult reading as a developmental requirement. Whose books, in what languages, by what authors — those are downstream questions about implementation. The upstream question is whether the capacity the reading builds should remain an institutional priority. That question is independent of which texts carry the training load.

The EdTech equity case is aspirational: results "remain inconclusive for low-income settings," and few large-scale studies examine how SES interacts with EdTech effectiveness.¹⁹ The institutional reading case has a different problem — it has documented failures at equalization — but an important asymmetry: free AI tutoring at Bloom levels two through four is a genuine intervention, and the cost argument for it is real. If the article's thesis holds — if the binding constraint in the AGI era is the higher-order evaluation capacity that AI tutoring does not build — then free tutoring targeted at the wrong capacity is not the equity win it appears to be. The tutoring reaches more students; the skill that most needs developing is left untouched.

Princeton University selected Maryanne Wolf's *Reader, Come Home* as its Pre-read for the Class of 2030, announced in April 2026.²⁰ President Eisgruber wrote that deep, immersive reading was "at the heart of a Princeton education" — and that challenging books offer students "something distinctive, valuable, and irreplaceable." The institutional recognition is arriving. The question is whether it arrives in time, and whether it arrives only at institutions whose students already have book-rich homes and parents who read.

That question reaches past education. The capacity to track a long argument to its conclusion, to recognize when confidence is unearned, to notice when a structure that looks like reasoning is only performing it — these are the cognitive preconditions for informed collective judgment: for following the reasoning of a public health emergency, for evaluating the trade-offs in a policy debate, for distinguishing a well-constructed democratic argument from a well-constructed democratic-sounding argument. An electorate that cannot evaluate reasoning under conditions of confident uncertainty is not merely less educated. It is more manipulable — not by the AI, but by whoever controls what the AI confidently says.

Literary competence, reading stamina, the circuit Wolf describes and the connoisseurship Chayka names — these are the infrastructure on which genuine evaluation of AI output depends. The decision to protect them, or to abandon them, is a decision about what kind of epistemic commons the next generation will inherit — and whether that commons will be capable of the work that the AGI era, more than any previous era, requires of it.

1. Kestin, G., et al. "AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting." *Scientific Reports*, 2025. doi: 10.1038/s41598-025-97652-6. https://www.nature.com/articles/s41598-025-97652-6 ↩ ↩ ↩
2. World Economic Forum. "Why AI literacy is now a core competency in education." May 2025. https://www.weforum.org/stories/2025/05/why-ai-literacy-is-now-a-core-competency-in-education/ | Purdue University. "Purdue Unveils Comprehensive AI Strategy; Trustees Approve AI Working Competency Graduation Requirement." December 2025. https://www.purdue.edu/newsroom/2025/Q4/purdue-unveils-comprehensive-ai-strategy-trustees-approve-ai-working-competency-graduation-requirement/ ↩ ↩
3. Khan Academy. "ELA AI Teacher Tools." *Khan Academy Blog*, 2025. https://blog.khanacademy.org/ela-ai-teacher-tools/ ↩ ↩
4. Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. "Detecting hallucinations in large language models using semantic entropy." *Nature*, 630, 625–630, June 2024. doi: 10.1038/s41586-024-07421-0. https://www.nature.com/articles/s41586-024-07421-0 ↩ ↩
5. "Detecting Artificial Intelligence–Generated Versus Human-Written Medical Student Essays: Semirandomized Controlled Study." *PMC11914838*, 2025. https://pmc.ncbi.nlm.nih.gov/articles/PMC11914838/ ↩ ↩
6. Wolf, Maryanne. "Skim Reading Is the New Normal: The Effect on Society." *Medium / Thrive Global*, 2018. (Quote confirmed verbatim from this piece: "Deep reading, like the reading brain circuit itself, is not a given; it is built by use, or it atrophies from disuse.") See also: Wolf, Maryanne. *Reader, Come Home: The Reading Brain in a Digital World*. Harper, 2018. Well, T. "The Reading Crisis in College." *Psychology Today*, March 11, 2025. https://www.psychologytoday.com/us/blog/the-clarity/202503/the-reading-crisis-in-college ↩ ↩
7. Chayka, K. "How to Cultivate Taste in the Age of Algorithms." *Behavioral Scientist*, 2024. https://behavioralscientist.org/how-to-cultivate-taste-in-the-age-of-algorithms/ See also: Chayka, K. *Filterworld: How Algorithms Flattened Culture*. Doubleday, 2024. ↩ ↩ ↩
8. Risko, E.F., & Gilbert, S.J. "Cognitive Offloading." *Trends in Cognitive Sciences*, 20(9), 676–688, 2016. https://pubmed.ncbi.nlm.nih.gov/27542527/ | Grinschgl, S., Papenmeier, F., & Meyerhoff, H.S. "Consequences of cognitive offloading: Boosting performance but diminishing memory." *PMC8358584*. https://pmc.ncbi.nlm.nih.gov/articles/PMC8358584/ ↩
9. Bjork, E.L., & Bjork, R.A. "Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning." In *Psychology and the Real World*, 2011. https://bjorklab.psych.ucla.edu/wp-content/uploads/sites/13/2016/04/EBjork\_RBjork\_2011.pdf | Bjork, R.A., & Bjork, E.L. "Desirable difficulties in theory and practice." *Journal of Applied Research in Memory and Cognition*, 9(4), 475–479, 2020. ↩ ↩ ↩
10. "Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement." *PMC11544060*, 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC11544060/ ↩
11. Alrawashdeh, G., Fyffe, S., Azevedo, R., & Castillo, C. "Exploring the impact of personalized and adaptive learning technologies on reading literacy: A global meta-analysis." *Educational Research Review*, 2023. doi: 10.1016/j.edurev.2023.100541. https://www.sciencedirect.com/science/article/abs/pii/S1747938X23000805 | Cheung, A.C.K., & Slavin, R.E. "Effects of Educational Technology Applications on Reading Outcomes for Struggling Readers: A Best-Evidence Synthesis." *Reading Research Quarterly*, 2013. doi: 10.1002/rrq.50 ↩ ↩ ↩
12. Bone, J.K., et al. "The decline in reading for pleasure over 20 years of the American Time Use Survey." *iScience*, vol. 28, no. 9, 2025, art. 113288. doi: 10.1016/j.isci.2025.113288. https://pmc.ncbi.nlm.nih.gov/articles/PMC12496190/ ↩ ↩ ↩
13. Twenge, J.M., Martin, G.N., & Spitzberg, B.H. "Trends in U.S. Adolescents' Media Use, 1976–2016: The Rise of Digital Media, the Decline of TV, and the (Near) Demise of Print." *Psychology of Popular Media Culture*, 8, 329–345, 2018. doi: 10.1037/ppm0000203. https://psycnet.apa.org/record/2018-41062-001 ↩ ↩
14. Kosmyna, N., Hauptmann, E., et al. "Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task." *arXiv:2506.08872*, June 2025. https://arxiv.org/abs/2506.08872 ↩ ↩ ↩
15. Gerlich, M. "AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking." *Societies*, 15(1), 6, January 2025. doi: 10.3390/soc15010006. https://www.mdpi.com/2075-4698/15/1/6 ↩
16. "Reading activity prevents long-term decline in cognitive function in older people: evidence from a 14-year longitudinal study." *PMC8482376*, *International Psychogeriatrics*, 2020. https://pmc.ncbi.nlm.nih.gov/articles/PMC8482376/ ↩ ↩
17. Sikora, J., Evans, M.D.R., & Kelley, J. "Scholarly culture: How books in adolescence enhance adult literacy, numeracy and technology skills in 31 societies." *Social Science Research*, 77, January 2019. doi: 10.1016/j.ssresearch.2018.10.003. https://www.sciencedirect.com/science/article/abs/pii/S0049089X18300607 ↩ ↩
18. "Socioeconomic status and reading outcomes: Neurobiological and behavioral correlates." *PMC9588575*. https://pmc.ncbi.nlm.nih.gov/articles/PMC9588575/ | "Influence of socioeconomic status on children's reading abilities: the mediating role of home learning environment." *PMC12309101*. https://pmc.ncbi.nlm.nih.gov/articles/PMC12309101/ ↩ ↩
19. "Bridging EdTech gaps: Examining learning equity in low-income educational settings." *ScienceDirect*, 2025. https://www.sciencedirect.com/science/article/abs/pii/S0738059325001968 | Wiley. "Adaptive, but Equitable? Exploring the Impact of Machine Learning-Based Adaptive Support on Educational Debts in Undergraduate Chemistry." *Science Education*, 2025. https://onlinelibrary.wiley.com/doi/10.1002/sce.70042 ↩
20. Princeton University. "Reader, Come Home by Maryanne Wolf selected as Princeton Pre-read." April 8, 2026. https://www.princeton.edu/news/2026/04/08/reader-come-home-maryanne-wolf-selected-princeton-pre-read ↩

- Wolf, Maryanne. *Reader, Come Home: The Reading Brain in a Digital World*. Harper, 2018. — The primary source for the deep reading circuit argument; SC1-B draws on Wolf's clinical observations and the broader neuroscientific argument the book makes. Library-only for the interior; the book's thesis is the article's foundational intellectual context.
- Carr, Nicholas. *The Shallows: What the Internet Is Doing to Our Brains*. W. W. Norton, 2010. — The foundational popular argument that sustained attention and deep reading are being structurally degraded by hypertext and scanning habits. The present article picks up where Carr left off and applies the substrate argument specifically to AI-era evaluation.
- Newport, Cal. *Deep Work: Rules for Focused Success in a Distracted World*. Grand Central Publishing, 2016. — The professional application of the attention-economy critique; relevant background for the primary audience, who have likely encountered Newport's framework and whose existing vocabulary the article builds on.
- Chayka, Kyle. *Filterworld: How Algorithms Flattened Culture*. Doubleday, 2024. — The full book-length argument from which the Behavioral Scientist quotes (SC3-C) are drawn; the connoisseurship argument is more fully developed here than in the cited article.
- Bjork, R.A., & Bjork, E.L. "Desirable difficulties in theory and practice." *Journal of Applied Research in Memory and Cognition*, 9(4), 475–479, 2020. — The most recent synthesis of the desirable difficulties research program; complements SC4-A (the 2011 chapter) and is more directly available to readers wanting to engage the primary source.
- Cheung, A.C.K., & Slavin, R.E. "How Features of Educational Technology Applications Affect Student Reading Outcomes: A Meta-Analysis." *Educational Research Review*, 2012. (84 studies, 60,000+ K-12 participants) — The broader meta-analysis from which the standalone-program figures (SC4-B) are drawn; the 2012 synthesis covers a wider evidence base than the 2013 struggling-reader focus paper.
- PMC11047126. "Cognitive reserve over the life course and risk of dementia: a systematic review and meta-analysis." *Frontiers in Aging Neuroscience*, 2024. — The cognitive reserve meta-analysis (SC5-B) mentioned in the evidence dossier as reserve evidence. Contains the early-life vs. late-life cognitive reserve comparison; the article does not cite it because the "reading reduces dementia risk by 18%" formulation cannot be supported (reading is one component of broadly-defined cognitive reserve), but the meta-analysis informs the argument about why early cognitive investment matters.

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