Trend · rank 132 of 250

Lifelong learning & digital reskilling

Shorter skill lifecycles and the spread of AI tools are increasing the need for learning throughout working life, making accessible retraining, digital competence and credible assessment central to national workforce adaptability.

Human CapitalPeople, health & workwork skillsai

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

Sensitivity and network position

Rank
132of 250
Sensitivity
10.8rank 90
Breadth
1of 20 scenarios High or above
PageRank
36.6rank 115
Eigenvector
2.0
Betweenness
1.5
Links
13trends at Moderate similarity or above
Composite
19.7

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
92
Over a year
+1%Steady
Over three years
−25%
Peak
13031 Dec 2023
Sources
Bothagreement 0.45
Coverage
15languages · 61 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 (15 of 28). Share is of all the readers counted.

LanguageShareNowOver a yearThree years
English57%114+25%
German7%84−10%
Spanish6%86−6%
French5%102−6%
Italian5%55−11%
Korean4%67−13%
Russian4%65−20%
Chinese3%86−9%
Japanese3%92−8%
Portuguese2%93−4%
Indonesian1%139+37%
Arabic1%115+60%
Polish1%76−12%
Ukrainian1%119+26%
Malay0%56−39%

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.

Botswana

Zimbabwe

South Africa

Kenya

Ethiopia

Zambia

Australia

Ireland

Singapore

Uganda

Ghana

Sri Lanka

100

54

51

44

43

41

37

33

31

29

27

27

The 12 highest of 61 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 English107+22%−0%+1%
EuropeGoogle in 7 countries + Wikipedia in German, French, Italian, Polish79−15%−13%−29%
ChinaGoogle in 1 country396+17%+749%+642%
Indo-PacificGoogle in 11 countries + Wikipedia in Japanese, Chinese, Korean, Indonesian, Malay90−5%−16%−16%
South AsiaGoogle in 4 countries81+3%−21%−27%
Gulf and Middle EastGoogle in 1 country + Wikipedia in Arabic102+16%+99%−17%
AfricaGoogle in 5 countries71+13%−20%−46%
Latin America and CaribbeanGoogle in 2 countries + Wikipedia in Spanish, Portuguese99−23%+8%+16%
Russia and EurasiaWikipedia in Russian, Ukrainian79−2%−7%−40%

G20 members

PlaceRead fromNow90 days1 year3 yearsThree years
AustraliaGoogle163+13%+30%+84%
BrazilGoogle + Wikipedia in Portuguese810%−17%−20%
CanadaGoogle91+27%−20%−21%
ChinaGoogle396+17%+749%+642%
FranceGoogle + Wikipedia in French98+20%−14%−3%
GermanyGoogle + Wikipedia in German74−20%−24%−37%
IndiaGoogle75+3%−30%−31%
IndonesiaGoogle + Wikipedia in Indonesian99+93%−33%−41%
ItalyGoogle + Wikipedia in Italian53−5%−13%−59%
JapanGoogle + Wikipedia in Japanese100+2%−5%+6%
South KoreaGoogle + Wikipedia in Korean70−28%−28%−30%
MexicoGoogle162−44%+109%+390%
RussiaWikipedia in Russian65−16%−21%−47%
South AfricaGoogle75+3%−15%−39%
TürkiyeGoogle90−1%+190%−24%
United KingdomGoogle68−18%−45%−40%
United StatesGoogle + Wikipedia in English108+23%−0%+5%

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
4 · HighAn AI deployment step causes abrupt labour displacement S08Technology and infrastructure
2 · LowA cross-regional market access compact redirects investment S12 ↗Economic and trade
2 · LowCheap firm clean power becomes available at scale S15 ↗Energy, climate and resources
2 · LowAI-directed research produces a reproducible discovery step S17 ↗Technology 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 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 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
4 · HighAI & knowledge-work automationPeople, health & work53
4 · HighFoundational learning deficitsPeople, health & work162
3 · ModerateEntrenched opportunity inequalityPeople, health & work9
3 · ModerateTechnical & occupational skill shortagesPeople, health & work19
3 · ModerateAutomated AI researchAI, compute & quantum83
3 · ModerateBusiness dynamism & start-up formationPeople, health & work85
3 · ModerateGeneral-purpose AI capabilityAI, compute & quantum95
3 · ModerateYouth population expansionPeople, health & work146
3 · ModerateYouth employment exclusionPeople, health & work177
3 · ModerateGraduate skill mismatchPeople, health & work178
3 · ModerateGender participation divergencePeople, health & work192
3 · ModerateSkilled & research talent migrationPeople, health & work208
3 · ModerateGeneral-purpose & humanoid roboticsAI, compute & quantum225