OECDDigitalEducationOutlook2026ExploringEffectiveUsesof

Generative

AIinEducationcrausiovhnenlOECDDigital

EducationOutlook2026EXPLORINGEFFECTIVEUSESOFGENERATIVEAIINEDUCATION》OECDBETTERPOLlcIES

FOR

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one.Pleasecite

thispublication

as:OECD(2026),OECDDigital

EducationOutlook

2026:Exploring

EffectiveUsesof

Generative

AI

inEducation,OECDPublishing,Paris,

/10.1787/062a7394-en.EditoriaITheOECDDigitalEducation

OutlookistheOECD’sflagshippublicationpresentingour

latest

analysis

ofemergingdigital

technologiesineducation.This

2026

edition

synthesises

evidence

and

expert

insightsto

show

how

generativeAI

hasthe

potentialtotransform

the

quality

and

effectiveness

of

learning,

as

well

as

the

productivity

of

education

systems,

provided

its

associated

risks

are

carefully

managed.

Its

applications

include

enhancing

student

learning,

supporting

teachers’

performance

while

preserving

professional

autonomy,

and

strengthening

education

systems,

as

well

as

institutional

and

research

capacities.For

students,

generative

AI

can

scale

personalised

learning

through

intelligent

tutoring

systems,

including

in

low-

infrastructure

settings.

GenerativeAI

can

also

support

knowledge

acquisition

by

enabling

collaborative

learning

and

enhancingcreativity.However,evidenceshows

thatoverrelianceongenerative

AI

tools

thatprovidedirectanswers

canreduce

students’

active

engagement,improvingtaskperformancewithoutcorrespondinglearninggains.Whenusedas

a

shortcut

ratherthan

a

learningtool,generativeAIcandisplacecognitiveeffortandweakenthe

skillsthat

underpin

deep

learning.Forexample,afieldexperimentinTürkiyefoundthatwhileaccesstoGPT-4improvedshort-termperformance–by48%

withthestandard

interface,and

by

127%with

atutoring

version

designed

to

support

learning

–

students

performed

17%worseonceaccesswasremoved,showingthatgenerativeAIcanunderminelearningunlessexplicitly

designedto

supportskillacquisition.For

teachers,

generative

AI

can

improve

both

productivity

and

teaching

quality.

Evidence

cited

in

the

report

shows

a

31%

reduction

in

time

spent

on

lesson

and

resource

planning

by

secondary

science

teachers

in

England,

and

a

9-percentage-point

increase

instudent

pass

rateswhen

low-experiencetutors

usedAIsupport,withsmaller

gains

for

moreexperiencedtutors.Accordingtothe

OECD’s2024Teachingand

LearningInternational

Survey,

37%

ofteachers

already

use

generativeAI

for

work-related

tasks-such

as

learning

about

or

summarising

topics

and

supporting

lesson

planning

-with

substantial

variationacrosscountries.Atthe

sametime,

concerns

persistthat

overreliance

onAI

could

undermine

teacher

autonomy

and

professionalism,

raiseethical

risks,and,when

usedextensivelyfortaskssuch

as

marking,feedback

or

lesson

planning,

erodeteachers’

professional

skills.Thereportcallsforashifttowardseducationalgenerative

AIsystemsdesignedwithteachers,enabling

them

tomonitor

students’interactionswithgenerativeAIandactivelyshapeitsuse

in

learning.At

the

systemlevel,

generative

AI

canimprove

the

efficiency

of

education

systems

and

schoolmanagementby

automatingandsupportingadministrativeandanalyticalprocesses.Itcanhelpdevelop

standardised

assessmentitems,

reviewcurricularalignmentbyanalysingactualversusexpectedstudentworkload,enhancestudyandcareerguidance,

and

support

the

classification

of

educational

resources

to

namejust

a

few.

Generative

AI

can

also

have

potentially

transformativeimplicationsforeducation

research,asinotherfields.To

realise

this

potential,

policymakers

will

need

to

mitigate

and

manage

associated

risks

-

such

as

those

related

toaccess,data

privacy,ethicsand

bias–throughsound

policyframeworksandeffective

governance.?OECD2026OECDDigitalEducationOutlook20263MathiasCormannSecretary-General,OECD4OECD

Digital

Education

Outlook

2026?OECD2026EditorialThe

OECDsupports

policymakers

in

making

effectiveand

responsible

use

ofgenerativeAI

in

education.This

includes

promoting

approaches

that

place

humanjudgement,

feedback

and

oversight

at

the

centre

ofAI

use;

strengthening

teachers’

capacity

toengage

with

AIconfidently

andeffectively;

andprovidingclear,practical

guidance

on

the

appropriate

use

of

generative

AI

in

education.

The

OECD

can

also

foster

international

co-operation

and

the

exchange

of

good

practices,enablingpeerlearningacross

jurisdictions,so

thatgenerative

AIdeliverson

its

fullpotential

forbetterlearning

andmoreeffectiveeducation

systems.AcknowIedgmentsThispublicationisanoutputof

theprojecton

“SmartDataandDigital

TechnologyinEducation:

AI,learninganalyticsand

beyond”of

theOECDCentreforEducationalResearchandInnovation(CERI)withintheOECDDirectoratefor

Education

andSkills(EDU).

LedbyStéphanVincent-Lancrin(DeputyHeadofDivisionand

Senior

Education

Economist,

OECD),the

project

team

is

(or

was)

comprised

ofQuentinVidal

(Analyst,

OECD),who

made

key

contributionsto

the

project;

Yixi

Wang

(former

Secondee,

now

CNAES,

China),whoworked

on

an

extensive

literature

reviewthat

informedthe

report

and

team’s

knowledge;Jennifer

O’Brien

(Assistant,

OECD),

who

managed

the

final

publication

process,

and;

Federico

Bolognesi(formerAssistant,OECD)who

providedexcellent

projectassistance

before

movingto

new

responsibilities.The

book

was

edited

by

Vincent-Lancrin.

The

overview

(chapter

1)

was

authored

by

Stéphan

Vincent-Lancrin

and

Quentin

Vidal.

The

fourinterviews(chapters5,6,10and12)

wereconductedand

transcribedbyStéphan

Vincent-Lancrin

and

QuentinVidal.TThe

other

chapterswereauthored

by

Dragan

Ga?evi?and

LixiangYan(chapter

2);

Yuheng

Li

and

Xiangen

Hu(chapter3);SebastianStrau?

and

Nikol

Rummel

(chapter

4);

Mutlu

Cukurova

(chapter

7);

ParaskeviTopali,

Alejandro

Ortega-Arranz

and

Inge

Molenaar

(chapter

8);

Ryan

Baker,Xiner

Liu,

Mamta

Shah,

Maciej

Pankiewicz,Yoon

Jeon

Kim,Yunseo

Lee

and

Chelsea

Porter

(chapter

9);

Zachary

Pardos

and

Conrad

Borchers

(chapter

11);

Dominique

Guellec

andStéphan

Vincent-Lancrin

(chapter13).Many

thanks

for

very

thoughtfulcontributions

andproviding

feedback

on

others’chapters!

Many

thanks

as

well

to

Seiji

Isotani

(chapter

5),

Ronald

Beghetto

(chapter

6),

Dorottya

Demszky

(chapter

10),andAlinavon

Davier(chapter

12)fortheirveryinformativeinterviews.Withinthe

OECDSecretariat,AndreasSchleicher,

Directorfor

Educationand

Skills

andAdvisor

to

the

OECD

Secretary-

General

on

Education

Policy,

provided

invaluablefeedback

and

iswarmlythankedfor

his

continuous

encouragement

throughout

theprocess.EdmundMisson

(Head

of

the

Innovation

andMeasuringProgressDivision,including

CERI)is

also

warmlyacknowledged

forhiscontinuoussupportandcomments

on

the

draftreport.

CassieHague

(Analyst),HyerimKim

(Analyst)andAnjelicaGiordano(Associate

researcher)arewarmly

thanked

for

their

reviews

ofthe

chapters,feedback,

suggestionsandconstantwillingnesstohelpthroughouttheproductionof

thereport.TheEDUcommunicationsteam

led

byJoanne

Caddy

is

gratefully

acknowledged

for

their

support

and

delivery

under

severe

time

pressure:

Duncan

Crawfordcopy-editedthe

ExecutivesummaryandOverviewof

thebook;

DellaShinand

Sophie

Limoges

didthe

layout;

EdaCabbarmanagedthepublicationprocesswiththeOECDcentralpublicationsteam.ColleaguesintheIMEPdivision

andthe

Director’sofficearealsogratefullyacknowledgedfortheircomments,suggestionsorfriendlyencouragement.The

CERI

governing

board

is

thanked

for

very

helpful

comments

and

feedback

on

the

initial

and

interim

ideas

for

the

book,whichthey

collectively

chose,

andfortheirfeedback

on

the

final

manuscript.

Special

thanks

to

the

country

coordinatorsof

theprojectfortheirfeedbackbutalsoengagement,enthusiasmandsharingofinformationduringthe

project

meetings.

Koreaand

Englandarealsothankfullyacknowledgedfortheirfinancialsupporttothe

project.TheGerhard

RichterAtelierarewarmlythankedforgranting

permissiontouse

Richter’s“Lesende”asthecover

image

ofthis

book,

representingthe

uncertainty

induced

by

generativeAIwith

a

comforting

tribute

tovalued

human

skills

suchas

reading.The

book

is

dedicated

to

the

late

Tia

Loukkola,

former

Head

of

CERI

(and

IMEP),

a

lovely

colleague

and

friend

who

brighteneddiscussionsonthe

possibleimpactsofgenerativeAIwith

her

mischievoussmile.?OECD2026OECDDigitalEducationOutlook20265TabIeof

contentsEDITORIAL.........................................................

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.11CHAPTER

1.

EXPLORINGEFFECTIVEUSESOFGENERATIVE

ARTIFICIALINTELLIGENCEINEDUCATION:AN

OVERVIEW

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13What

isthe

general

uptake

ofGenAI?

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14When

does

GenAI

improve

learning

outcomes?.

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.20What

do

educational

GenAI

tools

look

like?.

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.26HowcouldGenAIenhancetheeffectivenessofeducationsystemsandinstitutions?...

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.28Concluding

remarks

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.30Annex1.A.ExamplesofcountrystrategiesandframeworksongenerativeAIineducation

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37CHAPTER2.

GENERATIVE

AIFORHUMANSKILLDEVELOPMENT

AND

ASSESSMENT:IMPLICATIONSFOREXISTINGPRACTICESANDNEWHORIZONS

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.39Introduction

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.39Existing

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41Challengingassumptionsandenvisioning

new

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.50Conclusion

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.55PART1ENHANCINGSTUDENTLEARNINGWITHGENERATIVE

AI...................

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.65CHAPTER3.

LEARNING

WITHDIALOGUE-BASED

AITUTORS:IMPLEMENTINGTHESOCRATICMETHODWITHGENERATIVEAI.

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.66Introduction

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.66GenerativeAI

meetstraditional,AI-powered

pedagogicalagents...

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67Enhanced

agent

roles

and

capabilities

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.68Pedagogical

design

and

interaction

frameworks.

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70Working

in

practice:the

SPL

demonstration

system

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73Framework

for

efficacy

study.

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.77Challenges,

ethics

and

practical

implications.

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.78Future

directions

and

research

roadmap.

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.80Conclusion

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.83Annex3.A.TechnicalaspectsofeducationalGenAIagents.

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.876OECDDigitalEducationOutlook2026?OECD2026TableofContentsCHAPTER

4.

FOSTERINGCOLLABORATIVELEARNING

ANDPROMOTINGCOLLABORATIONSKILLS:WHATGENERATIVEAICOULDCONTRIBUTE.

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