Trend · rank 162 of 250

Foundational learning deficits

Many pupils still leave school without secure literacy and numeracy, lowering national productivity and limiting how far later training can meet more complex skill requirements.

Human CapitalPeople, health & workwork skillsinequality

Second pass: Carried forward from the first pass, where it was Learning loss & education quality. Held by all three panel models.

Sensitivity and network position

Rank
162of 250
Sensitivity
5.0rank 209
Breadth
0of 20 scenarios High or above
PageRank
36.7rank 113
Eigenvector
3.6
Betweenness
7.8
Links
11trends at Moderate similarity or above
Composite
16.7

Attention over three years

How much this trend is read about on Wikipedia and searched for on Google, week by week, set against the typical trend in the list so that what moves every trend at once is taken out. 100 is this trend's own three-year average. The score measures public attention, which is not the same as the strength of the force: a trend can deepen unnoticed, and a news event can lift attention for a fortnight.

Now
116
Over a year
+21%Rising
Over three years
+7%
Peak
2526 Sept 2026
Sources
Bothagreement 0.37
Coverage
14languages · 56 nations

Trailing 4-week average. 100 is this trend’s three-year average.

By language

Wikipedia reading in each language edition where the trend’s pages are read enough to measure (14 of 28). Share is of all the readers counted.

LanguageShareNowOver a yearThree years
English44%113+4%
Spanish13%154+89%
German11%160+92%
French7%119+18%
Russian6%108+28%
Italian4%145+70%
Japanese3%129+15%
Portuguese3%118+6%
Chinese2%109−7%
Polish2%150+55%
Arabic1%116+25%
Swedish1%134+56%
Indonesian1%133+17%
Dutch1%149+59%

By country

Google search interest over the three years, as a share of each country’s searching. 100 is the nation where interest is highest. The search terms are English, which favours countries that search in English.

India

China

Nepal

Pakistan

Singapore

United States

South Korea

Philippines

Canada

United Kingdom

Sri Lanka

United Arab Emirates

100

98

98

93

93

82

78

71

50

47

46

44

The 12 highest of 56 nations with a reading.

By region and G20 member

Attention to this trend inside each place, from Google searches made in its countries and Wikipedia reading in the languages mostly read there, averaged where both exist. Each line compares the trend with its own past in that place; the lines do not compare places.

Regions

PlaceRead fromNow90 days1 year3 yearsThree years
North AmericaGoogle in 2 countries + Wikipedia in English103+1%+4%+3%
EuropeGoogle in 8 countries + Wikipedia in German, French, Italian, Polish, Dutch, Swedish139+39%+57%+15%
Indo-PacificGoogle in 10 countries + Wikipedia in Japanese, Chinese, Indonesian118−5%+18%+29%
South AsiaGoogle in 3 countries72−9%−29%−46%
Gulf and Middle EastGoogle in 6 countries + Wikipedia in Arabic96+25%+2%−0%
AfricaGoogle in 2 countries75+3%−13%−35%
Latin America and CaribbeanGoogle in 6 countries + Wikipedia in Spanish, Portuguese122+26%+15%+26%
Russia and EurasiaGoogle in 2 countries + Wikipedia in Russian97+41%−11%−25%

G20 members

PlaceRead fromNow90 days1 year3 yearsThree years
ArgentinaGoogle82−11%−32%−3%
AustraliaGoogle105−25%+14%−7%
BrazilGoogle + Wikipedia in Portuguese94+7%−18%−4%
CanadaGoogle81−30%+22%−22%
FranceGoogle + Wikipedia in French112−6%+13%+11%
GermanyGoogle + Wikipedia in German153+14%+97%+40%
IndiaGoogle66−3%−40%−51%
IndonesiaGoogle + Wikipedia in Indonesian152−10%+74%+136%
ItalyGoogle + Wikipedia in Italian93+35%+39%−20%
JapanGoogle + Wikipedia in Japanese113−12%+7%+20%
South KoreaGoogle110−30%+11%+38%
MexicoGoogle120+35%−4%+25%
RussiaWikipedia in Russian108+30%+28%−16%
Saudi ArabiaGoogle69+39%−37%−53%
South AfricaGoogle108−6%−4%+75%
TürkiyeGoogle67−7%−11%−6%
United KingdomGoogle108+11%+35%−15%
United StatesGoogle + Wikipedia in English105+4%+2%+6%

Relation to each scenario

A trend relates to a scenario when it changes how likely the scenario is, how hard it lands, or is itself sharply changed by it. Each score is the median of three scores given separately by the members of the model panel.

RelationScenarioFamily
3 · ModerateAn AI deployment step causes abrupt labour displacement S08Technology and infrastructure
2 · LowA public service platform reaches inclusive operation S18 ↗Technology and infrastructure
1 · Very lowA validated farming package raises water-efficient output S16 ↗Energy, climate and resources
0 · Not relatedCritical trade divides into rival blocs S01Economic and trade
0 · Not relatedA global funding seizure reaches sovereign balance sheets S02Economic and trade
0 · Not relatedA regional war closes a globally important sea route S03Geopolitical and security
0 · Not relatedHybrid coercion ends in an infrastructure blackout S04Geopolitical and security
0 · Not relatedA disputed transfer of power breaks effective government S05Geopolitical and security
0 · Not relatedTwo breadbasket failures trigger a food availability crisis S06Energy, climate and resources
0 · Not relatedA major disaster disables the national economic core S07Energy, climate and resources
0 · Not relatedA cryptographic breakthrough invalidates digital trust S09Technology and infrastructure
0 · Not relatedA novel respiratory pathogen exceeds health surge capacity S10Health
0 · Not relatedA creditor agreement releases fiscal capacity S11 ↗Economic and trade
0 · Not relatedA cross-regional market access compact redirects investment S12 ↗Economic and trade
0 · Not relatedA great-power agreement lowers economic security barriers S13 ↗Geopolitical and security
0 · Not relatedA verified Middle East settlement restores access S14 ↗Geopolitical and security
0 · Not relatedCheap firm clean power becomes available at scale S15 ↗Energy, climate and resources
0 · Not relatedAI-directed research produces a reproducible discovery step S17 ↗Technology and infrastructure
0 · Not relatedAffordable preventive treatments reduce chronic illness S19 ↗Health
0 · Not relatedA live outbreak proves rapid distributed pandemic defence S20 ↗Health

Similarity = 0.3 × shared tags (Jaccard) + 0.3 × likeness of scenario profiles (cosine) + 0.3 × likeness of names and descriptions (TF-IDF cosine) + 0.1 if in the same capability domain, cut into seven levels at 0.12, 0.22, 0.33, 0.45, 0.58, 0.72. Shown at Moderate and above.

SimilarityTrendThemeRank
5 · Very highEntrenched opportunity inequalityPeople, health & work9
5 · Very highGraduate skill mismatchPeople, health & work178
5 · Very highGender participation divergencePeople, health & work192
4 · HighAI & knowledge-work automationPeople, health & work53
4 · HighInformal employment persistencePeople, health & work84
4 · HighLifelong learning & digital reskillingPeople, health & work132
4 · HighYouth population expansionPeople, health & work146
4 · HighYouth employment exclusionPeople, health & work177
3 · ModerateTechnical & occupational skill shortagesPeople, health & work19
3 · ModerateTechnology diffusion & productivity gapsPeople, health & work24
3 · ModerateAlgorithmic accountability & biasState capacity & governance135