Scholarly Resources

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Academic search literacy in the age of generative AI: A conceptual framework for navigating the search-language ecology
Academic search literacy in the age of generative AI: A conceptual framework for navigating the search-language ecology
The continuity thesis stated outright. Four eras of search, with GenAI as the latest language layer and expressly not a break from the past. They see co-creation, session-to-session variance, and failed reproducibility, then file all of it as trade-offs inside a category called search. Their sharpest challenge is that Boolean accountability never fit conceptually fuzzy fields, where citation chaining and expert consultation always did the work. That evidence shows mediation moving onto conversation well before AI, and every one of those conversants was answerable to someone. Where it stops: their model prompt asks for a 500-word summary, and everyone in the paper is enrolled, with a committee or a rubric. Note the terminology collision. Their "accountable search" is a property of a documented method, not of an agent.
·sciencedirect.com·
Academic search literacy in the age of generative AI: A conceptual framework for navigating the search-language ecology
Reading Between the Lines, Part 1: A Cognitive Framework for AI in Scholarly Publishing - The Scholarly Kitchen
Reading Between the Lines, Part 1: A Cognitive Framework for AI in Scholarly Publishing - The Scholarly Kitchen
Ghildiyal proposes a Cognitive Responsibility Framework dividing mechanical cognition, which AI performs well, from discovery, discernment, and judgment, which must remain human, and names the accumulating risk cognitive debt rather than hallucination. Worth reading for one passage: an editor who reads the AI summary before the manuscript has let an interpretation intervene before observation begins, so the risk is not that AI is wrong but that it mediates the relationship between the scholar and the evidence. That is the locus-of-mediation problem, reached from editorial workflow by someone with no stake in this argument. His mediation sits in front of the document rather than in place of it, which is why his remedy is sequencing: read first, then use the tool. The Shock concerns the case where nothing sits in the middle to be read first.
·scholarlykitchen.sspnet.org·
Reading Between the Lines, Part 1: A Cognitive Framework for AI in Scholarly Publishing - The Scholarly Kitchen
When AI Agents Can Complete the Assignment: Practical Strategies for Designing Tasks That Still Require Human Thinking | Published in Journal of Instructional Design and Technology
When AI Agents Can Complete the Assignment: Practical Strategies for Designing Tasks That Still Require Human Thinking | Published in Journal of Instructional Design and Technology

Agrees with the shock argument on the point most of the literacy literature misses: the problem sits inside the exchange, not in the output. Austin does not ask students to evaluate what the system produced. She aims at the back-and-forth itself.

Parts company on what to do about it. The protocol works by forcing the exchange to leave a residue, through timestamped checkpoints, decision logs, pasted verbatim output, staged submissions, and then evaluates that residue. It manufactures an inspectable artifact where the exchange left none. Read alongside the UVA archival protocol, this is the same move in a different register: hold the document still, or compel one into existence. Useful as a confirming case rather than a counter, and as the clearest available marker of where instructional design and information science diverge. Austin’s accountability runs to certification of an enrolled student, so the exchange has to terminate in an evaluable state. Where there is no grade and no enrollment, the residue cannot do the work, and warrant has to come from a stewarded record that persists past the exchange.

Two durability problems. The design premise depends on current model deficits (no cross-session memory, no local context, capitulation under pressure) that are already eroding as memory and persistent context ship. And the rationale column defends each move by naming what the agent cannot fake, which makes the criterion adversarial to the tool rather than derived from how understanding forms.

·joidat.scholasticahq.com·
When AI Agents Can Complete the Assignment: Practical Strategies for Designing Tasks That Still Require Human Thinking | Published in Journal of Instructional Design and Technology
When does AI support thinking, and when does it replace it?...
When does AI support thinking, and when does it replace it?...
Chan builds a typology of nine ways secondary students describe using AI to learn, and organizes them around a single boundary: the same interaction either extends thinking or substitutes for it, depending on how the learner positions the system. Working from Vygotsky, distributed cognition, and the extended mind rather than from LIS, she arrives at a claim adjacent to Information Shock: AI is not a tool delivering outputs but a form of cognitive mediation operating inside the act of thinking, and learning has relocated from the object to the moment-to-moment exchange. Where she parts company is the remedy. Having placed cognition inside the exchange, Chan turns to reflective prompts, metacognitive checklists, and better learner positioning, the literacy frame Information Shock argues cannot reach the second mode. Notably, no one in her model is accountable for what the learner leaves believing; the burden falls entirely on the learner's own regulation. A preprint, self-report only from a single cohort fresh off an AI literacy course, so suggestive rather than conclusive, but a rare case of the learning sciences naming the mediation shift in their own vocabulary.
·arxiv.org·
When does AI support thinking, and when does it replace it?...
Generative AI Compresses Expertise Signals and Accelerates the Withdrawal of Genuine Contributors from Online Communities
Generative AI Compresses Expertise Signals and Accelerates the Withdrawal of Genuine Contributors from Online Communities
Ching (working paper, 2026) argues that AI harms knowledge communities less by changing what people can produce than by destroying what their production signals about them. Once machine output is indistinguishable from expert work, the signal value of genuine effort collapses, and the most skilled contributors, who invested most in that signal, are the ones who withdraw. A survival analysis of 24,304 Stack Overflow contributors finds the highest-reputation users leaving fastest as AI capability climbs. The finding feeds the enclosure argument in Information Shock from the supply side: the open commons that trained these systems thins at its source. Notably, what Ching has to control away as a confound, users going to a chatbot instead of the platform, is what Information Shock reads as the phenomenon itself: the conversant taking the mediating seat the document repository used to hold. Early-stage, self-flagged as suggestive rather than causal, but a rare supply-side complement in a literature focused on the user's side of the screen.
·osf.io·
Generative AI Compresses Expertise Signals and Accelerates the Withdrawal of Genuine Contributors from Online Communities
How AI Will Reshape Public Opinion
How AI Will Reshape Public Opinion
Dan Williams argues LLMs are a "technocratising" force: unlike social media, they push users' beliefs toward expert consensus by giving easy, polite access to accurate information, and he holds that the right test isn't whether they're perfectly reliable but whether they beat the alternatives people would otherwise consult. A useful counterpoint to the Information Shock argument, since Williams contends commercial and legal incentives make these systems more accountable than social media or human experts, not less.
·conspicuouscognition.com·
How AI Will Reshape Public Opinion
🚨 New Preprint 🚨 Can making people aware of AI sycophancy protect them from its harmful effects? Across 6 experiments testing different interventions, we found that the answer is no. AI companies… | Meryl Ye | 11 comments
🚨 New Preprint 🚨 Can making people aware of AI sycophancy protect them from its harmful effects? Across 6 experiments testing different interventions, we found that the answer is no. AI companies… | Meryl Ye | 11 comments
In two preregistered experiments, backed by a pooled analysis covering about 3,982 people, Ye, Kraut, and Rathje warned users that an AI chatbot was flattering them before they ever used it. Some read a warning. Others watched the same bot validate people on opposite sides of the same argument. The warnings worked on judgment. Users rated the AI as less objective and trusted it less. The warnings did nothing to belief. Those same users walked away more certain and more extreme in their positions. Knowing the exchange was working on them did not stop it from moving what they believed.
·linkedin.com·
🚨 New Preprint 🚨 Can making people aware of AI sycophancy protect them from its harmful effects? Across 6 experiments testing different interventions, we found that the answer is no. AI companies… | Meryl Ye | 11 comments
AI Hallucinations in Academic Research: Why Libraries Matter More Than Ever - Information Matters
AI Hallucinations in Academic Research: Why Libraries Matter More Than Ever - Information Matters
Parvin argues that AI hallucinations in academic research are fundamentally a data-quality problem, and that libraries can address them by improving metadata, structuring repositories for machine readability, and verifying AI-generated citations against trusted databases. That prescription works for AI's first mode of authorship, where the system produces document-like objects that can be checked. Information Shock says it does not reach the second mode, where a person learns inside a conversation and the fabricated citation is just the residue of an exchange the verification toolkit was never built to examine.
·informationmatters.org·
AI Hallucinations in Academic Research: Why Libraries Matter More Than Ever - Information Matters
What if AI systems weren't chatbots?
What if AI systems weren't chatbots?
Ghosh and colleagues argue that the tech industry's rush to make AI conversational has real consequences: it collapses diverse sources into single authoritative-sounding answers, erodes users' critical thinking over time, and obscures the choices being made on their behalf. Their diagnosis lines up with Information Shock from the user's side of the screen. What they document in detail is exactly what happens when the document dissolves into the exchange and no one has built the tools to navigate what replaced it. Where they part company with Information Shock is in the remedy. Their prescription is to pull AI back out of conversation and into specialized, non-conversational tools that restore the inspectable, stable properties of documents. Information Shock says that retreat won't hold, because the conversational paradigm is already the primary site where people come to know things, and the field has to meet them there.
·arxiv.org·
What if AI systems weren't chatbots?
LLMs can't jump
LLMs can't jump
Is the future work creativity and logical leaps?
·wispaper.ai·
LLMs can't jump
The "Cognitive Offloading" Paradox
The "Cognitive Offloading" Paradox
Note on AI and cognitive learning studies. Love the concept of AI ad "intellectual collaborator." Analogous to the authoritative conversant framing in Information Shock.
intellectual collaborator
·drphilippahardman.substack.com·
The "Cognitive Offloading" Paradox
View of Theorising notions of searching, (re)sources and evaluation in the light of generative AI
View of Theorising notions of searching, (re)sources and evaluation in the light of generative AI
Olof Sundin's CoLIS 2025 paper reaches nearby ground from a different angle, arguing that AI is rendering sources progressively invisible and forcing LIS to rethink search, sources, and evaluation. A useful companion to the Information Shock argument, running on the source-visibility axis rather than the mediating-unit one.
·publicera.kb.se·
View of Theorising notions of searching, (re)sources and evaluation in the light of generative AI
SRI_Trust_in_human_AI_interactions_2026-06-09.pdf
SRI_Trust_in_human_AI_interactions_2026-06-09.pdf
A multidisciplinary white paper from the Schwartz Reisman Institute that surveys nine fields' accounts of trust in AI and lands on trust as the defining problem for responsible deployment, arriving at the same destination as the shock argument from the opposite direction: by integrating existing frameworks rather than by identifying what has ruptured beneath them.
·static1.squarespace.com·
SRI_Trust_in_human_AI_interactions_2026-06-09.pdf