Education
13 items across 13 editions. First seen Sun 13 Sep, last seen Mon 26 Oct.
- Tomas Lamanauskas, deputy secretary-general of the International Telecommunication Union, told Al Jazeera on the sidelines of the UNCTAD World Investment Forum in Doha on Sunday that Africa and Latin America together "account for just 3.3 percent of global data centre capacity" and that 2.2 billion people worldwide remain without internet access. Closing the digital infrastructure gap "would require an estimated $2.8 trillion in investment by 2030", he said.
- The adoption gap is narrower but real: "approximately 29 percent of working-age people in developed countries use generative AI, that figure drops to 16 percent in developing nations". He also said "only seven percent of African languages are represented in AI translation tools". His framing: "a great divide could really mean an opportunity divide, and really could deepen the socioeconomic divide".
- On jobs he cited estimates that AI "could displace 90 million jobs, but also create 170 million new ones", and warned that even with a net positive "an estimated 40 percent of workplace skills could change by 2030". Al Jazeera notes a December 2025 UNDP report cautioning that AI could reverse decades of narrowing economic disparities between nations, and cites the February 2026 International AI Safety Report putting about one billion people using AI. These are an official's cited estimates in an interview, not new measurements, and Al Jazeera does not source each figure to a specific study.
- In arXiv:2610.31579, 32 participants learned up to five science topics by chatting with five assistants built on the same model, GPT-5-mini. Unknown to them, each assistant's briefing had 0, 25, 50, 75 or 100% of its facts replaced by plausible falsehoods, and every assistant spoke with full confidence.
- Across 104 quizzes and 1,040 answers, the abstract reports "scores fell from 7.4 to 2.9 out of 10, and the planted option was chosen on 60% of falsified items against 11% of correctly taught ones (odds ratio 12.5)". Transcript coding showed that whenever the assistant stated a falsehood, "it was adopted about 65% of the time at every falsification level".
- Absorbed falsehoods were held with high confidence, 8.4 out of 10, and "the discrimination of confidence for correctness fell from an AUROC of 0.70 to chance, while post-task trust ratings barely moved". The paper notes participants returned to the chat more when the assistant was worse, and that returning went with more absorption, not less.
- A preprint, submitted 22 October and not peer reviewed, with 32 participants and one model. The paper reports the assistant itself "was almost never sycophantic and never conceded under pushback" — the effect measured is human deference to a confident model, not model sycophancy.
- The policy paper, published 21 October, says "around 800,000 digital and technology roles are projected to need filling" over the next decade in sectors backed by the Industrial Strategy, and that "[t]ogether with industry, our ambition is to open around 200,000 genuine entry-level jobs, including apprenticeships" over the same period, with government and employers committing to "around 20,000 entry-level jobs a year through a single Skills Deal for employers."
- Its first strand is "Inspire: reaching one million young people with meaningful AI and technology learning through TechFirst by 2028." The accompanying press release says all secondary schools can now access TechFirst, with the one-million ambition pulled forward by a year and including "400,000 from the most disadvantaged schools."
- The release announces "10,000 new work experience placements" in the digital and tech sector and names Microsoft, Cisco, IBM, Cognizant, AtkinsRéalis, Arm, Kainos and QinetiQ as "opening up 10,000 opportunities over the next three years." It says nearly 1 million young people are not in employment, education or training.
- These are ambitions and employer commitments rather than funded guarantees, and the documents give no mechanism for enforcement if the 20,000-a-year figure is not met.
- The working paper, "How Heavily Are PhD Students Offloading Their Scientific Writing to AI? Evidence from US STEM PhD Dissertations" by Daniel P. Gross, Dror Shvadron and Hansen Zhang, reports no detectable AI writing before 2023 and AI writing in "29% of dissertations filed in 2026".
- Nature reports the paper analysed dissertations from more than 226,000 STEM doctoral graduates completed between January 2019 and May 2026 at US universities, assessing abstracts with the AI-detection service Pangram, and that the share rose from 1.7% in 2023 to 18.8% in 2025 and 29.4% in the first five months of 2026.
- Nature reports use was highest in mathematical and computer sciences at 36% of abstracts, and that about 41% of dissertations by graduates educated in non-English-speaking countries had AI-generated abstracts against 23% for those from English-speaking countries.
- Using LinkedIn profiles, Nature reports that among PhD graduates who did their undergraduate degrees in the United States, those with AI-generated abstracts were 17% less likely to be employed in academia and 16% more likely to be in industry. The paper has not been peer reviewed, and the figures depend on one detection service whose error rate the coverage does not give.
- Nature reports that the tool, IdeaLens, "was able to spot AI-derived ideas 91% of the time" and "works by analysing the 'outline' of a work rather than the text itself." The preprint says it was trained on 1M FineWeb documents using silver labels from Pangram.
- In a controlled study the authors report IdeaLens's AI flag rate fell from 95% to 7% as models wrote from increasingly detailed human plans, while the text-based detector Pangram 4 still flagged 92%; when models wrote from AI-derived plans, IdeaLens flagged above 96%. On a new set of 50 stories humans wrote from AI-generated plans, IdeaLens flagged 68% as AI against 8% for Pangram 4.
- The distinction matters for conferences and journals that are, as co-author Rishanth Rajendhran of the University of Maryland told Nature, "overwhelmed" with artificially generated content: it separates who wrote the sentences from who had the idea.
- Nature quotes Nikhil Garg of Cornell Tech cautioning that because the tool is fallible "it should not be used to judge the human authenticity of individual cases," and that an AI "tell" in one domain "may simply be good human writing in another." The underlying paper is a preprint submitted on 5 October, before this window; the Nature news report is the development inside it.
- At the IMF and World Bank annual meetings on Monday, IMF Managing Director Kristalina Georgieva said AI was hitting the global labour market like a "slow-moving tsunami", with young graduates first in line: "People to worry about, first and foremost, [are] young people that are coming into the labor market." CNBC reports the IMF estimates about 40% of jobs worldwide will eventually be "augmented, downgraded or eliminated", with exposure about 60% in advanced economies and 23% in low-income countries — the lower figure "just because AI is further away".
- Singapore President Tharman Shanmugaratnam, who co-chairs the World Bank's Jobs Council, said Covid-era and financial-crisis-era support "now has to be thought of not as a crisis measure, but as part of the structural, the menu of structural policies", because "the first rung in employment, your first job upon graduation, is now seeing less demand". He said that in the US "for the first time in a century, demand for graduates has gone down", with the wage premium for a degree also shrinking.
- World Bank President Ajay Banga estimated 1.2 billion young people will turn 18 over the next 15 years and that the private sector, which already generates 75% to 85% of jobs in any country, will have to absorb most of them. Georgieva said AI-related investment and trade have helped keep global expansion near 3% despite an energy supply shock, and estimated AI adoption could lift growth by 0.5 to 1 percentage point in advanced Asia and 0.2 to 0.8 percentage point in emerging Asia.
- These are institutional estimates of exposure, not measured job losses, and the IMF's 40% figure covers jobs "augmented" as well as eliminated. Shanmugaratnam cautioned that growth alone may not deliver jobs — China, India and developing Southeast Asia are "growing quite well, but job creation is weak" — and that displacement will come "task by task", which "gives us time".
- Berkeley News says researchers "designed a series of randomized controlled trials and recruited 1,222 participants online". In the first experiment, 354 participants either solved 15 basic fraction problems unaided or had ChatGPT open alongside; the AI group "started off more accurate", but after 12 problems the tool was removed and "almost immediately, the people in that group stopped solving questions accurately".
- The pattern repeated in a second, larger fractions experiment with 667 participants, where "the AI users got answers wrong or gave up entirely when the AI assistance was withdrawn" while the unaided group persisted and did better at the end. A third experiment with 201 participants on an SAT reading-comprehension prompt also saw persistence and accuracy drop when the tool was removed.
- Co-author Brian Christian, a research fellow at Berkeley's Center for Human-Compatible AI, is quoted: "We showed, ironically, that they are often helping us in ways that are kind of unhelpful." Berkeley says the team included scholars from Carnegie Mellon, MIT, Oxford and UCLA.
- A preliminary draft circulated earlier this year; the in-window development is that the team presented the updated, peer-reviewed paper this week at the Conference on Language Modeling. The figures reported here come from the university's own write-up rather than from the paper, which was not opened.
- Anthropic announced Claude Frontier Academy on 2 October as a $100 million commitment to train 10,000 engineers it calls Frontier Deployed Engineers by the end of 2027. The residency runs in three phases: a multi-day in-person programme built around a simulated enterprise deployment, a 12-week residency leading a real Claude project at the engineer's own organisation, and a final assessment and credential.
- Two badges are awarded, "Claude Resident Engineer" after the first phase and "Claude Frontier Deployed Engineer" after the residency, with the first credentials expected in early 2027. Launch partners named are Accenture, Bain, Capgemini, Commonwealth Bank of Australia, Deloitte, McKinsey, Morgan Stanley and Novo Nordisk, with cohorts in San Francisco, New York and London.
- A lab issuing its own professional credential is a different kind of lock-in from an API contract: the skill being certified is deployment of one vendor's models, and the certifying body is the vendor. Steve Corfield of Anthropic is quoted saying "a small team of high-agency people with the right skills...can transform an entire company".
- Everything here is Anthropic's announcement. There is no breakdown of how the $100 million is spent, no cohort sizes, no fee structure, no stated pass rate or assessment standard, and no independent accreditation.
- The paper (arXiv:2609.21194, submitted 17 September 2026) reports a controlled between-subjects experiment with 59 undergraduate computer science students, 55 retained for analysis, completing three introductory C programming tasks with either ChatGPT-4.5 or conventional web search without generative AI.
- It reports that "ChatGPT-assisted students achieved higher coding scores (89% vs. 69%) but lower recall scores immediately (41% vs. 53%) and after 48 hours (39% vs. 52%)", with no significant difference between groups in how much recall was lost over the 48 hours.
- Students in the ChatGPT condition "attributed less of the submitted code to themselves (45% vs. 81%)", and self-reported mental effort increased less across tasks (Holm-adjusted p = .047).
- All four authors list the University of New South Wales, Sydney. The paper states that "confirmatory physiological tests did not detect significant differences" and that "substantial data loss limits their interpretation". It is a preprint.
- Epoch AI, in an analysis by Tara Abrishami updated on September 18, reports that "the share of math preprints on arXiv that acknowledge AI has risen rapidly, from 4% in April to 25% in August".
- Epoch says that in August 6% of all papers explicitly acknowledged that AI made a substantial research contribution, a category it describes as work roughly at the level of a coauthor.
- The rise is not explained by new authors entering the field: restricting to papers with at least one author who published regularly before 2023 keeps the acknowledgment rate within 1.5 percentage points of the overall figure each month.
- Epoch classified acknowledgments across about 51,000 maths preprints using AI models, with 93% agreement against human review of 200 randomly selected acknowledgments. The measure depends on voluntary disclosure, so it tracks disclosure behaviour as well as use.
- Epoch AI, publishing on 14 September, reports that the share of US adults who used AI on 6–7 days in the previous week rose from 8.2% in March 2026 (90% CI 7.2–9.3) to 18.7% in August 2026 (90% CI 16.7–20.8). Any weekly AI use rose from 50.0% to 56.4%, and the share using AI on just one day fell from 17.3% to 10.1%.
- The figures come from two Epoch AI/Ipsos surveys of US adults on Ipsos' KnowledgePanel, fielded 3–5 March 2026 (n=2,017, 1,028 AI users) and 28–30 August 2026 (n=1,016, 574 AI users), weighted to represent US adults aged 18 and over.
- Epoch flags its own comparability problem: "The days of use question changed between waves, in two ways." March asked one overall question with three bands; August asked per-service day counts from 1 to 7 and Epoch took the maximum across services, which it says "may understate frequency relative to an overall-use question". Epoch concludes "comparisons of frequency across the two waves are approximate".
- The two waves are separate samples rather than the same people tracked over time, so the result is a population-level shift, not a measurement of individuals changing their behaviour.
- "Informational Help-Seeking on Reddit Did Not Decline After ChatGPT" (arXiv 2609.12447, submitted 11 September, announced 14 September) by Hazem Ibrahim and Yasir Zaki tracks monthly post counts in 26 Reddit informational communities against 90 size-comparable hobby communities over the same six calendar months before and after ChatGPT's launch, and repeats the analysis at 66 earlier dates as a placebo.
- The abstract states: "Our results rule out any decline in posting larger than 3.4%, far smaller than the 8% to 25% declines documented in prior work."
- The authors scored 274,411 posts and 223,775 comments with AI-text detectors and report that "AI-written posts rose only 2-3 percentage points more in informational communities than in hobby communities, short of the 5.1 points that would be needed to hide even the smallest decline previously reported for Reddit". They attribute earlier findings to community types already drifting apart before ChatGPT existed.
- Preprint, not peer reviewed. The study covers one platform over a six-month window around launch and relies on AI-text detectors whose false positives it cancels statistically rather than validates directly. The authors report their own largest estimate — an 18% fall in posts to low-stakes curiosity communities — matches that group's pre-existing trend.
- The Decoder, publishing on 13 September, reports a randomised controlled trial by Thibault Schrepel of Vrije Universiteit Amsterdam comparing three groups — no ChatGPT access, unguided AI suggestions, and structured prompt-engineering training — with 66 students in 2024 and 164 in 2025.
- The group without AI scored lowest on both the in-class assignment and the take-home exam in both years. Schrepel, who had expected unguided use to hurt performance, is quoted saying: "I was wrong."
- The trained group scored well above the other two in 2024, but by 2025 that advantage had almost closed and all three groups performed at roughly the same level. The Decoder reports Schrepel attributes this to growing familiarity with chatbots.
- Limitations stated in the article: a small sample, students already enrolled in an AI course and so likely more tech-savvy than average, and no way to verify how much AI students actually used during take-home exams. The underlying paper is on SSRN and was not accessible for this edition, so these figures come from The Decoder's report.