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FORMATION OF ARTIFICIAL INTELLIGENCE COMPETENCIES IN
FUTURE EDUCATORS IN THE CONTEXT OF DIGITAL EDUCATION
Muna a Sul ono a
Cen e o Resea ch on he De elopmen o Highe Educa ion unde he Minis y o
Highe Educa ion, Science and Inno a ion o he Republic o Uzbekis an
[email p o ec ed]
Abs ac : The in eg a ion o a i icial in elligence (AI) in o educa ional sys ems
ep esen s a undamen al shi in how educa o s mus be p epa ed o wen y- i s -
cen u y eaching. The g owing p esence o AI echnologies in class ooms demands
ha u u e educa o s acqui e compe encies ha blend echnological unde s anding
wi h sound pedagogical p ac ice. This a icle ou lines he concep ual ounda ions,
challenges, and s a egies o de eloping AI compe encies among u u e educa o s
wi hin digi al educa ion con ex s. I highligh s key dimensions o compe ence
o ma ion and o e s a s uc u ed amewo k o assessing he app op ia eness o AI
in eg a ion ac oss di e se educa ional se ings.
Keywo ds: AI, AIED, echnologies in eaching, digi al educa ion, digi al
compe encies.
AI compe encies o educa o s encompass he knowledge, skills, and a i udes
necessa y o e ec i ely in eg a e a i icial in elligence echnologies in o eaching and
lea ning p ocesses. These compe encies b idge echnological unde s anding wi h
pedagogical expe ise o enhance educa ional ou comes (Muengsan & Cha wa ana,
2024). AI compe encies e e o he abili y o unde s and, e alua e, and e ec i ely
u ilize a i icial in elligence echnologies in educa ional con ex s. This includes
comp ehension o AI p inciples, p ac ical applica ion skills, and he capaci y o make
in o med decisions abou AI in eg a ion in eaching p ac ices (AlMahdawi, 2024).
A sys ema ic app oach o e alua ing he sui abili y o AI in eg a ion equi es
comp ehensi e assessmen c i e ia ha conside con ex ual ac o s, pedagogical
alignmen , and echnical easibili y. Key con ex ual dimensions include he
educa ional le el— anging om age-app op ia e ools o p ima y educa ion o
ad anced applica ions in highe educa ion and p o essional de elopmen —and he
deg ee o ins i u ional eadiness, such as in as uc u e capaci y, acul y digi al
compe ence, adminis a i e suppo , and budge alloca ion.
Pedagogical alignmen demands ha AI applica ions co espond o cu iculum
s anda ds, enhance highe -o de cogni i e skills, and suppo di e en ia ed ins uc ion.
They should in eg a e smoo hly wi h es ablished eaching me hods, including
cons uc i is app oaches, collabo a i e and p ojec -based lea ning, pe sonalized
lea ning, and au hen ic assessmen p ac ices. Technical easibili y in ol es ensu ing
adequa e ha dwa e and ne wo k esou ces, obus da a secu i y, compa ibili y wi h
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exis ing lea ning managemen sys ems, and manageable implemen a ion complexi y.
T aining equi emen s, accessibili y o use in e aces, and scalabili y ac oss class sizes
a e also c i ical.
E hical and social conside a ions o m a pa allel axis o e alua ion. P i acy and
da a p o ec ion equi e compliance wi h educa ional egula ions such as FERPA and
GDPR, anspa en AI decision-making p ocesses, and clea consen mechanisms.
Equi y and accessibili y demand a en ion o he digi al di ide, inclusi e design o
s uden s wi h disabili ies, cul u al sensi i i y in AI algo i hm design, and equal access
o AI-enhanced lea ning oppo uni ies.
Assessmen me hodologies should combine quan i a i e me ics—such as
imp o emen s in lea ning ou comes, engagemen a es, e iciency indica o s, and cos -
bene i analyses—wi h quali a i e app oaches, including s akeholde eedback, case
s udies, ocus g oup discussions, and class oom obse a ions. Decision-making
amewo ks bene i om mul i-c i e ia decision analysis, weigh ed sco ing sys ems,
isk assessmen s, and pilo es ing o i e a i e e inemen . Implemen a ion oadmaps
can hen be de eloped wi h phased ollou s, a ge ed p o essional de elopmen , and
con inuous moni o ing o adap o e ol ing educa ional needs.
AI compe encies o m an essen ial componen o digi al li e acy in mode n
educa ion. Digi al li e acy encompasses in e ela ed skills, including media,
echnology, in o ma ion, isual, communica ion, and social li e acies (Muengsan &
Cha wa ana, 2024). AI awa eness and compe ency ex end hese ounda ional digi al
skills and ep esen a c i ical capaci y o educa o s p epa ing s uden s o a da a-d i en
socie y (Bende , 2024).
Co e Componen s o AI Compe encies o Fu u e Educa o s
The de elopmen o a i icial in elligence (AI) compe encies equi es a
mul i ace ed app oach ha uni es echnical knowledge, pedagogical expe ise, and
e hical awa eness o p epa e educa o s o e ec i e and esponsible use o AI in
educa ional con ex s. Fu u e educa o s mus acqui e a solid unde s anding o AI
p inciples, e minology, and p ac ical applica ions, including knowledge o gene a i e
AI capabili ies ha p oduce ex , images, sound, and syn he ic da a (Muengsan &
Cha wa ana, 2024). App ecia ing how AI p ocessing sys ems pa allel human
cogni i e unc ions helps eache s ecognize how hese echnologies can complemen
human eaching and lea ning. Comp ehensi e amilia i y wi h key concep s such as
machine lea ning, na u al language p ocessing, and gene a i e AI is essen ial, ye
esea ch e eals pe sis en gaps in p o essional unde s anding o AI e minology and
unde lying p inciples, unde sco ing he need o a ge ed aining (Rainey e al., 2021).
Gene a i e AI in pa icula o e s powe ul oppo uni ies o educa ion by enabling he
c ea ion o ealis ic i ual assis an s, pe sonalized lea ning se ices, and digi al a
ha can ans o m ins uc ional p ac ices and en ich lea ning expe iences (Muengsan
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& Cha wa ana, 2024). Equally c i ical is he capaci y o in eg a e hese echnologies
meaning ully in o eaching p ac ices while main aining sound pedagogy. Educa o s
mus be able o design and manage AI-enhanced digi al lea ning en i onmen s ha
os e s uden engagemen and measu able achie emen (Vilppola e al., 2022) and o
implemen AI-suppo ed assessmen me hods such as o ma i e e alua ion ools and
digi al es ing sys ems. E idence om game-based pla o ms such as Kahoo !
demons a es how AI-enhanced ools can s eng hen academic pe o mance,
mo i a ion, and lea ne engagemen (Mdlalose e al., 2021). Alongside echnical and
pedagogical expe ise, he cul i a ion o s ong e hical and c i ical hinking
compe encies is essen ial. Responsible use o AI equi es awa eness o issues o
plagia ism, equi y, access, and da a p i acy; educa o s mus ensu e ha AI-d i en
p ac ices mee es ablished e hical and p i acy s anda ds (Bende , 2024). Finally, u u e
educa o s need he abili y o c i ically e alua e AI ools and applica ions, assessing
hei app op ia eness, e ec i eness, and po en ial impac on s uden lea ning, while
ecognizing bo h he oppo uni ies and limi a ions inhe en in AI echnologies.
Cu en S a e o AI Compe ency De elopmen
Resea ch consis en ly highligh s a subs an ial gap be ween he g owing need o
a i icial in elligence (AI) compe encies and he cu en le el o p epa a ion among
educa o s. Unde s anding hese sho comings is c ucial o designing e ec i e aining
p og ams and long- e m suppo sys ems. S udies show ha educa o s gene ally
display only medium le els o AI awa eness, wi h acul y membe s sco ing 3.05 on a
i e-poin scale, indica ing conside able oom o imp o emen in bo h knowledge and
unde s anding o AI echnologies (AlMahdawi, 2024). Simila pa e ns o
unde p epa edness appea in o he p o essional domains. Fo example, esea ch among
heal hca e p o essionals e eals widesp ead de iciencies in knowledge o AI
p inciples, unde s anding o e minology, and con idence in using AI echnologies;
many epo eeling inadequa ely ained o implemen AI in p ac ice se ings (Rainey
e al., 2021). These indings sugges ha educa o s a e likely o ace compa able
challenges in in eg a ing AI in o eaching and lea ning.
P o essional de elopmen oppo uni ies cu en ly a ailable o educa o s ha e
no kep pace wi h hese needs. Many eache s exp ess dissa is ac ion wi h exis ing
p og ams, desc ibing hem as insu icien ly p ac ical and poo ly aligned wi h he
speci ic compe encies equi ed o AI in eg a ion (, 2024). To add ess hese
sho comings, s a egies o AI compe ency de elopmen mus be comp ehensi e and
mul i ace ed, combining heo e ical ins uc ion wi h oppo uni ies o au hen ic
p ac ice.
P omising app oaches include game-based lea ning pla o ms in eg a ed wi h
gene a i e AI, which c ea e challenging and engaging en i onmen s ha os e
p ac ical explo a ion and skill acquisi ion (Muengsan & Cha wa ana, 2024). Such
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pla o ms ypically inco po a e ou essen ial elemen s: inpu ac o s—such as
s uden s, eache s, objec i es, ules, lea ning media, and assessmen ; a game-based
lea ning p ocess enhanced by gene a i e AI; measu able ou pu s, including lea ning
achie emen and digi al li e acy skills; and a eedback mechanism o guide i e a i e
imp o emen (Muengsan & Cha wa ana, 2024). The lea ning p ocess i sel can ollow
i e key s eps—explo ing knowledge, explana ion, playing games, p esen ing ideas,
and engaging in discussions and conclusions—while applying i e guiding p inciples:
p ac ice, lea ning by doing, lea ning om mis akes, goal-o ien ed lea ning, and he
a icula ion o lea ning poin s (Muengsan & Cha wa ana, 2024).
Wo k-based lea ning models also hold signi ican p omise by enabling
immedia e expe imen a ion wi h AI ools and he applica ion o new ideas in au hen ic
educa ional con ex s (Vilppola e al., 2022). In addi ion, pe sonalized aining and
sus ained suppo a e essen ial o ensu e las ing compe ency de elopmen . Meaning ul
p og ess ypically eme ges h ough con inuous in e ac ion wi h men o s o ad iso s
who balance con idence building wi h skill enhancemen , p o iding ongoing eedback
and a ge ed assis ance (O iz-Laso e al., 2023).
Implemen a ion Challenges and Solu ions
De eloping AI compe encies in u u e educa o s aces nume ous challenges ha
demand sys ema ic app oaches and a ge ed in e en ions. A he ins i u ional le el,
common ba ie s include limi ed inancial esou ces, inadequa e echnological
in as uc u e, and esis ance o echnological change. Wi hou comp ehensi e suppo
and ca e ul s a egic planning, such cons ain s hinde he e ec i e in eg a ion o
a i icial in elligence in o eache p epa a ion p og ams. Time and esou ce limi a ions
also pose di icul ies: many educa o s epo pe sis en eelings o has e and a lack o
ime o p o essional de elopmen , while aining p og ams ha ail o acknowledge
exis ing skills can discou age p og ess and educe mo i a ion (Vilppola e al., 2022).
In addi ion, esea ch highligh s co ela ions be ween AI con idence and demog aphic
a iables such as gende , age, and highes quali ica ion, indica ing he need o
di e en ia ed app oaches ha add ess di e se lea ne needs and ensu e equi able
oppo uni ies o skill de elopmen (Rainey e al., 2021).
The u u e o AI compe ency de elopmen ca ies p o ound implica ions o
educa ional ans o ma ion and he p epa a ion o digi ally li e a e ci izens. In eg a ing
AI compe encies as a co e componen o eache educa ion p og ams is essen ial a he
han ea ing hem as op ional en ichmen . Unde g adua e cu icula mus include
hands-on expe iences wi h AI applica ions and digi al echnologies o ensu e ha
u u e educa o s gain p ac ical expe ise (Hashish & Alnajja , 2024). Achie ing his
ision equi es obus echnological in as uc u e suppo ed by high-pe o mance
compu ing esou ces—such as GPU-enabled wo ks a ions and cloud compu ing
se ices—along wi h i ual and augmen ed eali y equipmen , in e ac i e
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whi eboa ds, and eliable high-speed in e ne connec ions. Secu e da a s o age,
edundan backup sys ems, and s ong wi eless ne wo ks a e equally c i ical o
sus ained access o AI ools. Access o so wa e and pla o ms, including p o essional
AI de elopmen amewo ks such as Tenso Flow and PyTo ch, no-code o low-code
AI en i onmen s, AI-in eg a ed lea ning managemen sys ems, and simula ion
so wa e o educa ional scena ios, p o ides he necessa y ounda ion o applied
lea ning. Specialized educa ional so wa e—such as AI-powe ed u o ing sys ems,
da a isualiza ion ools, and assessmen pla o ms wi h AI analy ics— u he suppo s
au hen ic class oom p ac ice.
Building AI compe encies also depends on human expe ise and ca e ully
s uc u ed p o essional de elopmen . Uni e si ies mus cul i a e AI-li e a e acul y
h ough ad anced deg ee p og ams, ongoing p o essional de elopmen , sabba ical
oppo uni ies in indus y, and collabo a i e eaching models ha pai AI expe s wi h
educa ion specialis s. In ensi e summe ins i u es, egula wo kshops on eme ging AI
echnologies, and pee men o ing ne wo ks s eng hen acul y skills, while access o
online ce i ica ion p og ams ensu es con inuous g ow h. Dedica ed echnical suppo
eams—including IT specialis s, ins uc ional designe s, and da a analys s—a e i al
o assis ing bo h acul y and s uden s in he implemen a ion o AI p ojec s. S uden -
ocused se ices such as AI u o ing cen e s, pee men o ing p og ams, ca ee
counseling, and academic ad ising ein o ce indi idual p og ess and con idence.
Cu iculum design mus p o ide s uc u ed lea ning pa hways ha begin wi h
ounda ional AI li e acy cou ses and p og ess owa d specialized acks aligned wi h
subjec a eas and g ade le els. Caps one p ojec s ha in eg a e AI in o eaching
p ac ice, mic o-c eden ialing o speci ic compe encies, and igo ous assessmen
ools—including s anda dized e alua ions, pee e iews, and po olio sys ems—
enable sys ema ic measu emen o AI compe ency de elopmen . Comp ehensi e
educa ional esou ces, om in e ac i e online modules and case s udy lib a ies o open
educa ional esou ces (OER) and p ac ical lesson plan empla es, suppo sus ained
lea ning and applica ion.
S a egic pa ne ships and ex e nal collabo a ions s eng hen he capaci y o
eache educa ion p og ams o de elop AI compe encies. Pa ne ships wi h echnology
companies can p o ide access o ools, aining oppo uni ies, in e nships, gues
lec u es, and join esea ch p ojec s, while educa ional discoun s, be a es ing
oppo uni ies, and cloud compu ing c edi s expand echnological access. Uni e si y
ne wo ks and p o essional o ganiza ions acili a e acul y exchanges, collabo a i e
esea ch g an s, and he dissemina ion o bes p ac ices h ough con e ences and
p o essional socie ies. Long- e m success also elies on inancial and adminis a i e
commi men . Ins i u ions mus alloca e su icien unding o in as uc u e, acul y
hi ing, so wa e licensing, and he eno a ion o lea ning spaces while main aining
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ongoing budge s o aining p og ams, echnical main enance, and s uden suppo
se ices. Fede al and s a e g an s, p i a e ounda ion unding, and alumni o dono
con ibu ions can help sus ain hese ini ia i es.
The ela ionship be ween li elong lea ning endencies and digi al compe encies
highligh s ano he impo an dimension o AI compe ency de elopmen . While s udies
show a signi ican co ela ion be ween hese cons uc s (Keskin, 2023), assuming
di ec causali y is me hodologically and heo e ically p oblema ic. Co ela ion does
no es ablish causal di ec ion, and hi d a iables—such as ins i u ional suppo o
socio-economic ac o s—may in luence bo h li elong lea ning and digi al compe ence.
C oss-sec ional esea ch designs canno es ablish empo al p ecedence, while
longi udinal s udies ace challenges such as pa icipan a i ion and apidly changing
echnology landscapes. Fu he mo e, bo h li elong lea ning and digi al compe ence a e
mul idimensional cons uc s ha a y ac oss cul u al con ex s and indi idual
expe iences. Recognizing his complexi y, eache educa ion p og ams should a oid
o e simpli ied causal assump ions and ins ead adop concep ual amewo ks ha iew
he ela ionship as ecip ocal and media ed. Recip ocal models acknowledge eedback
loops in which li elong lea ning and digi al compe ence ein o ce each o he o e ime,
while media ed app oaches emphasize he ole o sel -e icacy, social suppo , and
oppo uni y s uc u es in shaping hese ela ionships.
Ul ima ely, AI compe encies ha e he po en ial o ans o m educa ional p ac ice
by enabling new o ms o pe sonalized lea ning, inno a i e assessmen me hods, and
ad anced pedagogical app oaches. To ealize his ans o ma ion, educa o s mus
acqui e a comp ehensi e se o compe encies ha encompass echnical li e acy,
pedagogical in eg a ion, e hical awa eness, and p ac ical implemen a ion skills (Ma zal
& Vi a elli, 2024). Technical AI li e acy includes an unde s anding o machine
lea ning algo i hms, na u al language p ocessing, compu e ision, da a analy ics, and
he abili y o e alua e he e ec i eness and limi a ions o AI ools. Pedagogical
in eg a ion equi es designing adap i e lea ning en i onmen s, c ea ing cu icula ha
espond o indi idual s uden needs, and employing AI-d i en di e en ia ed
ins uc ion. Educa o s mus also use AI o o ma i e assessmen and eal- ime
eedback while main aining human o e sigh . E hical implemen a ion demands ha
eache s ecognize and mi iga e algo i hmic bias, ensu e p i acy and da a p o ec ion,
and ad oca e o inclusi e AI p ac ices ha espec cul u al and socio-economic
di e si y. Finally, educa o s mus cul i a e collabo a i e and c i ical e alua ion skills
o balance au oma ion wi h human judgmen , sha e bes p ac ices wi hin p o essional
lea ning communi ies, and adap AI s a egies in esponse o eme ging echnologies
and s uden eedback.
By add essing hese challenges wi h a s a egic, well- esou ced, and e hically
g ounded app oach, eache educa ion p og ams can p epa e u u e educa o s o
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ha ness he ans o ma i e po en ial o a i icial in elligence while sa egua ding
educa ional quali y and equi y.
In he con ex o Uzbekis an, he de elopmen o AI compe encies in u u e
educa o s is closely ied o he coun y’s ongoing na ional digi al ans o ma ion
s a egies and educa ional e o ms. The go e nmen ’s “Digi al Uzbekis an 2030”
p og am has es ablished ambi ious goals o in eg a ing ad anced echnologies in o
key sec o s, including educa ion, he eby c ea ing bo h oppo uni ies and challenges
o eache p epa a ion ins i u ions. While signi ican in es men s ha e been made in
expanding b oadband in as uc u e and mode nizing highe educa ion acili ies,
dispa i ies emain be ween u ban and u al egions in e ms o echnological access
and skilled pe sonnel. Teache educa ion p og ams a e inc easingly encou aged o
inco po a e AI- ela ed con en , ye he e is s ill a p essing need o sys ema ic acul y
de elopmen , indus y pa ne ships, and cu iculum inno a ion o align wi h
in e na ional s anda ds. Fu he mo e, ensu ing ha AI in eg a ion espec s cul u al and
linguis ic di e si y—pa icula ly he use o Uzbek and o he local languages in AI-
powe ed educa ional ools—will be c i ical o equi able adop ion. By le e aging s a e
suppo , in e na ional collabo a ions, and a ge ed p o essional de elopmen
ini ia i es, Uzbekis an can accele a e he o ma ion o a new gene a ion o educa o s
equipped wi h he echnical, pedagogical, and e hical AI compe encies equi ed o
sus ain he na ion’s digi al educa ion agenda and o p epa e s uden s o pa icipa ion
in he global knowledge economy.
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