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CHAPTER 5
CHAPTER 5
INTEGRATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES
INTO THE DIGITAL TRANSFORMATION OF PROFESSIONAL
HIGHER EDUCATION IN TECHNICAL FIELDS
ABSTRACT
This chap e explo es cu en ends in he in eg a ion o a i icial in elligence echnologies
in o he p o essional aining o s uden s in echnical highe educa ion ins i u ions. The heo e ical
sec ion highligh s models o digi al ans o ma ion, he s uc u e o digi al compe ences, he ole o
AI in adap ing educa ional p og ams, as well as s a egic ini ia i es implemen ed a L i Poly echnic
Na ional Uni e si y unde he leade ship o ec o N. Shakho ska.
Special a en ion is gi en o an empi ical s udy based on a su ey o s uden s and ins uc o s in
echnical ields. The indings iden i y he mos an icipa ed bene i s and ba ie s o AI adop ion in he
educa ional p ocess and e eal co ela ions be ween selec ed ad an ages and he esponden s’
le el o digi al eadiness. A se ies o isualiza ions is p esen ed, including a digi al compe ence py a-
mid, ne wo k g aphs o s akeholde in e ac ion, and a map o mul iple associa ions be ween AI-d i -
en educa ional ou comes. The esul s unde sco e he need o a sys emic app oach o os e ing
AI li e acy in echnical uni e si ies, he impo ance o digi al pedagogical suppo o ins uc o s,
and he de elopmen o an e hical cul u e in he use o in elligen ools in p o essional educa ion.
KEYWORDS
A i icial in elligence in educa ion, echnical highe educa ion, digi al ans o ma ion, AI li e acy,
digi al compe ences, educa ional echnologies, p o essional aining, e hical use o AI, ins uc o s’
digi al eadiness, lea ning inno a ion.
In he 21s cen u y, p o essional highe educa ion aces he impe a i e o ans o ma ion unde
he in luence o digi al echnologies, pa icula ly a i icial in elligence (AI). Technical uni e si ies,
such as L i Poly echnic Na ional Uni e si y, a e expec ed no only o adap o new condi ions bu
also o become lagships o inno a i e change. Today, he mission o echnical educa ion ex ends
beyond he ans e o specialized knowledge o include he de elopmen o digi al compe ences
aligned wi h he demands o a dynamic labo ma ke and con empo a y digi al eali y.
DOI: 10.15587/978-617-8360-16-0.CH5
Ana olii Dmy uk, Vi aliia H y si , Maiya Babkina,
I yna Sk il, I yna Vyslobodska, Ma yna Smile skal
© The Au ho (s) o chap e , 2025. This is an Open Access chap e dis ibu ed unde he e ms o he CC BY license
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AI unc ions no only as a subjec o s udy bu also as a powe ul ool o ans o ming he
educa ional p ocess. In elligen sys ems a e al eady being applied o analyze educa ional da a,
pe sonalize lea ning, c ea e simula ions and digi al wins, au oma e knowledge assessmen , and
suppo s uden s h ough i ual u o s and cha bo s. A he same ime, he implemen a ion o
such echnologies equi es upda ed eaching me hodologies, e hical esponsibili y, and equi able ac-
cess o digi al solu ions o all pa icipan s in he educa ional ecosys em. The heo e ical unde pin-
nings o AI in ma hema ics and educa ion a e well a icula ed in he amewo ks e lec ing cu en
ad ances [1–3], emphasizing he impo ance o desc ip i e models and sys em ca ego iza ions.
This opic is pa icula ly ele an in he con ex o he s a egic de elopmen o L i Poly echnic
Na ional Uni e si y, led by Rec o P o . Na aliia Shakho ska, Doc o o Technical Sciences and a
leading expe in in elligen in o ma ion echnologies. The uni e si y consis en ly ad ances a policy
o educa ional digi al ans o ma ion and he expansion o digi al compe ences among bo h ins uc-
o s and s uden s.
The pu pose o his s udy is o p o ide a heo e ical amewo k and empi ical explo a ion o
he in eg a ion o a i icial in elligence echnologies in o he educa ional p ocess o a echnical
highe educa ion ins i u ion. Special emphasis is placed on he analysis o digi al and AI- ela ed
compe ences, he expec ed bene i s and challenges o implemen a ion, and he in e connec ions
be ween key componen s o digi al ans o ma ion – as illus a ed by he case o L i Poly echnic
Na ional Uni e si y.
The objec o he s udy is he p ocess o de eloping digi al and AI compe ences in he p o es-
sional highe educa ion sys em o echnical p o ile.
The subjec o he s udy includes he me hods, models, and ools o implemen ing AI echnolo-
gies in he educa ional p ocess o a echnical uni e si y, as well as he a i udes o key s akeholde s
owa d hei use.
Resea ch objec i es:
– o analyze he cu en s a e o AI implemen a ion in p o essional educa ion wi hin echnical
uni e si ies;
– o iden i y he le els o digi al and ai- ela ed compe ences de eloped among s uden s in
echnical ields;
– o in es iga e he expec ed bene i s and challenges associa ed wi h in eg a ing ai in o he
educa ional p ocess;
– o de elop a isual model illus a ing he in e connec ions be ween key componen s o digi al
ans o ma ion;
– o p opose a s uc u al model o in eg a ing AI in o he sys em o p o essional highe edu-
ca ion in echnical ins i u ions.
The me hodological amewo k o he s udy includes an analysis o scien i ic li e a u e and
s a egic documen s, a compa a i e e iew o educa ional p ac ices, as well as a quan i a i e
empi ical s udy based on su eys conduc ed among s uden s and ins uc o s in echnical ields.
The ob ained esul s a e p esen ed h ough g aphical isualiza ions – including py amidal models
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o digi al compe ence, ne wo k diag ams, and his og ams – which help in e p e he s uc u e o
pe cei ed bene i s and ba ie s o AI in eg a ion in educa ion.
S uc u ally, he chap e comp ises six hema ic blocks, which he chap e combines analy ical
dep h wi h p ac ical o ien a ion, illus a ing he oppo uni ies and p ospec s o expanding digi al
compe ences in mode n p o essional highe educa ion.
Mos exis ing esea ch ocuses ei he on gene al o e iews o IT/AI in highe educa ion o on
applica ions wi hin speci ic disciplines (e.g., language lea ning o medical aining). In con as ,
his s udy p esen s a sys ema ic analysis o AI implemen a ion speci ically wi hin a echnical
uni e si y, aking in o accoun in e nal educa ional policies and p ac ices a L i Poly echnic Na-
ional Uni e si y.
The p oposed o iginal h ee-le el model o digi al and AI compe ences (li e acy – p o essional
use – esea ch le el) o e s a amewo k o s uc u ing he p epa a ion o echnical s uden s o
eal-wo ld pa icipa ion in he digi al economy.
An associa i e isualiza ion me hod is applied o e eal he connec ions be ween selec ed
pe cei ed bene i s o AI, a echnique a ely used in pedagogical s udies. This app oach allows no
only o acking equency o esponses bu also o explo ing he cogni i e con ex , iden i ying
which ad an ages a e in e connec ed in esponden s’ pe cep ions.
The s udy e lec s eal ini ia i es implemen ed a L i Poly echnic Na ional Uni e si y: he
digi al ans o ma ion policy led by Rec o Na aliia Shakho ska, coope a ion wi h he IT indus-
y, pa icipa ion in Jean Monne and BUP p ojec s, and he de elopmen o digi al in as uc-
u e. This gi es he esea ch a p ac ical dimension and o e s a e e ence model o o he
echnical uni e si ies in Uk aine.
The chap e also includes he o iginal su ey ins umen , which may be eused by o he ins i-
u ions o assess hei eadiness o AI in eg a ion. Addi ionally, he p oposed in og aphics and
ne wo k-based models can se e as ools o educa ional managemen and s a egic planning.
5.1 Theo e ical ounda ions o he implemen a ion o a i icial in elligence in
oca ional educa ion
5.1.1 E olu ion o AIED pa adigms (a i icial in elligence in educa ion)
The idea o applying a i icial in elligence in educa ion has a long his o y, da ing back o he
1970s and 1980s when he i s concep s o In elligen Tu o ing Sys ems (ITS) we e de eloped.
These sys ems we e based on he assump ion ha a compu e could model indi idual s uden
needs and adap educa ional ma e ials acco dingly.
The main heo e ical app oaches in his pa adigm include:
– he in elligen u o ing pa adigm, whe e AI ac s as a men o : i moni o s p og ess, de ec s
knowledge gaps, and adjus s he lea ning ajec o y;
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– he collabo a i e lea ning coo dina ion pa adigm, which eme ged la e and ocuses on sup-
po ing s uden in e ac ions, ask dis ibu ion, and enhanced g oup lea ning h ough AI mecha-
nisms [4].
Con empo a y AI ools go beyond hese ea ly concep s. Gene a i e models such as GPT,
Claude, and Copilo no only adap lea ning con en bu ac i ely c ea e new educa ional ma e ial.
This equi es a undamen ally new unde s anding o hei pedagogical ole.
As a esul , AIED is ans o ming om a ep oduc i e en i onmen in o one o sha ed cogni i e
pa ne ship be ween humans and digi al agen s.
5.1.2 Hyb id in elligence as a concep ual amewo k o ai in eg a ion
in educa ion
The adi ional iew o AI as an au onomous sys em is g adually being eplaced by he concep
o hyb id in elligence, in which AI does no eplace he human bu enhances cogni i e capabili ies.
This app oach is based on he idea o syne gy: human in ui ion, c ea i i y, and e hical judgmen a e
combined wi h he compu a ional powe , analy ical speed, and adap abili y o AI.
M. Cuku o a no es ha human – AI hyb id in e ac ion is one o he key ends in educa ional
echnology. They emphasize ha e ec i e lea ning sys ems should unc ion as ex ended lea ning
en i onmen s in which AI ac s no jus as a knowledge media o bu as a pa ne in p oblem-sol ing,
e lec ion, and sel -di ec ed lea ning [5].
In he con ex o oca ional and echnical educa ion, hyb id in elligence is ealized h ough:
– au oma ed assessmen wi h expe co ec ion;
– in e ac i e lea ning sys ems ha model beha io and p o ide eedback;
– join p ojec de elopmen be ween s uden s and digi al agen s, e.g., du ing code c ea ion,
diag am design, o da a modeling [6].
5.1.3 E hical and inclusi e dimensions o AIED
As he in luence o AI in educa ion expands, he issue o e hical esponsibili y becomes inc eas-
ingly impo an . The in eg a ion o AI changes bo h pedagogical app oaches and he ela ionships
among eache s, s uden s, and digi al agen s. The e o e, he e is a g owing need o es ablish an
e hical amewo k o AIED use.
Acco ding o W. Holmes e al., he main isks associa ed wi h AIED include [4]:
– algo i hmic opaci y and he inabili y o explain sys em-gene a ed ecommenda ions;
– hidden bias due o skewed aining da a;
– p i acy iola ions and i esponsible collec ion o pe sonal educa ional da a;
– educed s uden au onomy, wi h a isk o u ning educa ion in o an o e ly con olled p ocess.
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In addi ion, issues o digi al equi y a e c i ically impo an . S udies conduc ed wi hin ou p ojec
con i m ha no all lea ne s ha e equal access o mode n digi al ools and high-speed in e ne ,
pa icula ly unde wa condi ions o in socioeconomically disad an aged egions.
Leading o ganiza ions such as UNESCO, IEEE, and he Eu opean Commission – ecommend
adhe ing o he ollowing p inciples in AI in eg a ion:
– anspa ency (AI sys ems should be in e p e able o use s);
– ai ness (a oiding disc imina ion o exclusion);
– accoun abili y (clea assignmen o esponsibili y o AI ac ions);
– secu i y (p o ec ion o educa ional da a);
– human-cen e edness (AI should se e as a suppo ool, no a con ol mechanism) [7, 8].
5.1.4 Theo y o socially gene a i e sys ems
A no el di ec ion in AI and educa ion esea ch is he concep o socially gene a i e sys ems,
which iews AI no as a s a ic ool bu as a co-pa icipan in he social lea ning p ocess.
M. Sha ples p oposes in e p e ing gene a i e models (such as Cha GPT, Claude AI, and Copilo )
as communica ion pa icipan s capable o suppo ing, ans o ming, o e en simula ing pedagogical
in e ac ions. Lea ning, in his con ex , becomes a iadic p ocess: eache – s uden – Ai [9].
Social gene a i i y is e lec ed in:
– AI pa icipa ion in dialogues, whe e i no only answe s bu also asks cla i ying ques ions
o p o ides coun e a gumen s;
– co-cons uc ion o knowledge, whe e s uden s “discuss” ideas wi h AI, e ine a gumen s,
and ain logical hinking;
– shaping lea ning beha io h ough AI-gene a ed ecommenda ions ha in luence ime plan-
ning o lea ning s a egies.
This heo y helps explain why pe cei ed bene i s o AI among eache s and s uden s a e in e -
ela ed. In ou esea ch, a ne wo k s uc u e o pe cei ed bene i s was iden i ied, whe e e ec s
such as pe sonaliza ion, mo i a ion, and inno a ion a e in e connec ed. This is a mani es a ion o
social gene a i i y.
Thus, ea ing AI as a social agen allows us o expand adi ional educa ional models and
align hem wi h 21s -cen u y lea ning concep s – co-c ea ion, pa ne ship, and mul idi ec ional
in e ac ion [10, 11].
5.1.5 Digi aliza ion as a d i e o p o essional compe ence de elopmen
Digi al ans o ma ion a ec s no only educa ional ools bu also he s uc u e o p o essional
compe encies o med in s uden s o echnical disciplines. The ocus is shi ing om adi ional
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knowledge and skills o in eg a ed digi al abili ies, he capaci y o adap o eme ging echnologies,
and he abili y o collabo a e e ec i ely wi hin digi al en i onmen s.
A ecen amewo k o oca ional and echnical educa ion a gues ha de eloping digi al com-
pe encies e ec i ely equi es a whole-ins i u ion app oach – engaging ins i u ional leade s, each-
e s, and lea ne s oge he in co-c ea ing he digi al lea ning en i onmen .
Sys ema ic e iews in highe educa ion poin ou ha digi al ans o ma ion demands no
only echnical luency bu also pedagogical skillse s: c i ical media li e acy, e hical awa eness, and
me hodological inno a ion a e highligh ed as essen ial capabili ies o bo h s uden s and educa o s.
A s udy on g adua es’ employabili y e eals signi ican skills gaps: employe s inc easingly e-
qui e da a li e acy, online esea ch compe ence, digi al communica ion, and basic cybe secu i y.
Acco ding o esea ch conduc ed wi hin ou p ojec (Sec ion 5.3), bo h s uden s and ins uc-
o s acknowledge ha he use o AI se ices con ibu es signi ican ly o he de elopmen o key
p o essional compe encies (Fig.5.1), including:
– analy ical hinking, de eloped h ough wo king wi h la ge da ase s, que ying AI, and in e -
p e ing esul s;
– digi al li e acy, enhanced h ough hands-on in e ac ion wi h mode n ools such as Gi Hub
Copilo , No ion AI, and simila pla o ms [6, 11];
– adap abili y and lexibili y, os e ed by na iga ing he unp edic abili y o gene a i e AI e-
sponses;
– p ojec -o ien ed hinking, suppo ed by new o ma s such as lea ning case s udies, hack-
a hons, and collabo a i e wo k en i onmen s [12, 13].
Fig. 5.1 S uc u e o he ela ionship be ween digi al skills and componen s o p o essional
compe ence
P o essional
compe ency
Digi al
li e acy
Adap abili y o
echnological
change
Technical
knowledge
C i ical
hinking
C ea i i yTeamwo k
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In he con ex o echnical and oca ional educa ion, hese compe encies a e especially impo -
an . Fu u e p o essionals a e expec ed no only o ope a e AI ools, bu also o unde s and hei
a chi ec u e, e hical limi a ions, and p ac ical ele ance o hei ield o expe ise [14, 15].
5.1.6 The li elong lea ning pa adigm in he digi al socie y
In he 21s cen u y, he concep o li elong lea ning has e ol ed om an abs ac ideal in o
a p ac ical necessi y. The apid ad ancemen o digi al echnologies, pa icula ly a i icial in elli-
gence, is eshaping he labo ma ke , al e ing he quali ica ions expec ed om p o essionals, and
sho ening he li e cycle o knowledge. In his con ex , highe educa ion ins i u ions a e no longe
limi ed o deli e ing ounda ional knowledge bu a e inc easingly esponsible o de eloping skills in
sel -di ec ed lea ning, e-skilling, and c i ical adap a ion (Fig.5.2).
Fig. 5.2 C oss-s uc u e o o mal, non- o mal, and in o mal lea ning in he digi al socie y
Fo mal
lea ning
Non- o mal
lea ning
In o mal
lea ning
• Lec u es
• P ac icals
• Labo a o ies
• Webina s
• Open online
cou ses
S uden as
an a chi ec o
hei own educa ional
ajec o y
AI
Pe sonaliza ion
New expec a ions o g adua es o echnical uni e si ies include:
– he abili y o quickly upda e p o essional knowledge;
– eadiness o mas e new digi al ools independen ly, wi hou ex e nal assis ance;
– sel -assessmen skills o acking one’s educa ional p og ess;
– in insic mo i a ion o con inuous lea ning, especially in online en i onmen s [16, 17].
A i icial in elligence plays a key ole in suppo ing li elong lea ning h ough:
– adap i e lea ning sys ems, which adjus con en and pace based on lea ne pe o mance;
– pe sonalized lea ning pa hways, aligned wi h lea ne goals and cu en compe encies;
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– AI-based assis an s ha p o ide sugges ions, gene a e explana ions, o adminis e diagnos-
ic es s (e.g., Copilo , ecommende sys ems used by pla o ms like Cou se a);
– knowledge e i ica ion ools based on in elligen es ing algo i hms [18].
This shi equi es a e hinking o bo h educa ional con en and me hodologies. Educa o s a e
now expec ed o cul i a e lea ning- o-lea n s a egies, enabling s uden s o unc ion e ec i ely in
dynamic, digi al knowledge en i onmen s.
5.1.7 T ans o ma ion o he educa ional en i onmen in he con ex o
digi al ansi ion
As digi al echnologies con inue o expand, he educa ional en i onmen o echnical uni e si-
ies is ans o ming in o a mul i-dimensional ecosys em ha combines physical, i ual, blended,
and simula ed lea ning spaces. Wi hin his e ol ing con ex , a i icial in elligence unc ions as a
modula o o educa ional lows, enabling he cus omiza ion o lea ning p ocesses o mee he indi-
idual needs o each pa icipan .
Key cha ac e is ics o he mode n educa ional en i onmen include:
– hyb id lea ning o ma s, combining o line ins uc ion, online lea ning, asynch onous modules,
and simula ion-based expe iences;
– digi al mobili y, whe e s uden s access con en ia mobile apps, cloud pla o ms, and i ual
labo a o ies;
– in eg a ion o in elligen sys ems, such as AI-powe ed scheduling ools, p og ess acking
dashboa ds, and pe sonalized ecommenda ion engines;
– con inuous eedback loops, suppo ed by lea ning managemen sys ems (LMS), cha bo s,
and educa ional analy ics pla o ms [4, 5].
Examples o in eg a ed solu ions:
– Moodle wi h AI modules – o pa icipa ion analy ics and au oma ic gene a ion o pe sonal-
ized assignmen s;
– MS Teams wi h Copilo – assis ing ins uc o s in c ea ing quizzes, answe ing s uden ques-
ions, and managing cou se ma e ials;
– Open edX wi h adap i e pa hways – deli e ing di e en ia ed ins uc ion based on lea ne
pe o mance and p e e ences.
This ans o ma ion ede ines he educa ional space om a s a ic loca ion in o a dy-namic
lea ning ecosys em, esponsi e o changes in lea ne beha io and echnological ad an-cemen s.
The Fig. 5.3 illus a es how co e elemen s o he digi al en i onmen – adminis a i e pla -
o ms, lea ning pla o ms, cloud se ices, simula o s, and AI modules – in e ac h ough a cen al
educa ional analy ics hub.
This hub collec s da a on use ac i i y, pe o mance, and lea ning dynamics o gene a e indi-
idualized educa ional scena ios.
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Fig. 5.3 Digi al lea ning en i onmen o a echnical uni e si y: componen s, in e ac ions, and unc ions
ACTIVITY
AI moduls
Adminis a i a e
pla o m
Educa ional and
lea ning pla o ms
Cloud
se ices
LEARNING
ANALYTICS
RESULTS
RECOMMENDATIONS
Digi al Lea ning En i onmen in HEI:
Componen s, In e ac ion, Func ions
INDIVIDUALIZED
LEARNING
SCENARIOS
PROGRESS
DYNAMICS
5.1.8 Adap ing he egula o y amewo k o AI in eg a ion in educa ion
The g owing in eg a ion o a i icial in elligence in o educa ional p ocesses equi es no only
echnical mode niza ion bu also he upda ing o egula o y amewo ks go e ning he ope a ion o
echnical highe educa ion ins i u ions. A bo h na ional and ins i u ional le els, he lack o clea ly
de ined policies ega ding he use o gene a i e AI, s uden da a analysis, and au oma ed assess-
men sys ems c ea es legal unce ain y and aises conce ns o e academic in eg i y.
The model (Fig. 5.4) ou lines egula o y alignmen a h ee in e connec ed le els: na ional policy
(mac o), ins i u ional go e nance (meso), and class oom p ac ices (mic o). I e lec s how op-down
and bo om-up egula o y dynamics shape e hical, anspa en , and e ec i e use o AI in educa ion.
Key a eas o egula o y adap a ion include:
– ins i u ional AI policies: o malizing guidelines o he pe mi ed use o AI ools in s uden
p ojec s, heses, labo a o y epo s, and o he academic wo k;
– e ised assessmen p ocedu es: inco po a ing open o ma s, elemen s o o al e i ica ion,
and hyb id assessmen models o ensu e au hen ici y o lea ning ou comes;
– e hical code o AI usage: equi ing p ope a ibu ion o AI-assis ed con en (simila o
academic ci a ions), and p ohibi ing he use o AI o chea ing, manipula ion, o da a ab ica ion.
S uden da a p o ec ion: aligning ins i u ional p ac ices wi h GDPR p inciples, e en o in e nal
da a pla o ms used o educa ional analy ics [19, 20].
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signi ican o ices in L i – ha e been cen al o his e o . These alliances ha e shaped cu iculum
and lab o e ings: So Se e and pee s ac i ely con ibu ed o de eloping new AI, da a science,
cybe secu i y, and IoT p og ams h ough he L i IT Clus e , wi h suppo o educa ional acks
and access o indus y-g ade ools.
E en s like he L i IT Clus e ’s “IT Fu u e Con ” wi h So Se e as gold pa ne egula ly as-
semble leading i ms (including EPAM, N-iX, A enga, In ellias) o lec u es, wo kshops, and s uden
ec ui men . Beyond educa ion, companies like So Se e ha e launched eal-wo ld AI pilo p o-
g ams such as in eg a ing gene a i e AI in o de elopmen wo k lows, boos ing p oduc i i y up o
45% c ea ing in e nship and esea ch oppo uni ies o s uden s a L i Poly echnic .
Addi ionally, join spaces like he Sma Indus y con e ence and he IoT lab, suppo ed by bo h he
IT Clus e and companies like So Se e and GlobalLogic, os e con inuous collabo a ion among aca-
demia, business, and s uden s. This engagemen enables he uni e si y o co-c ea e applied AI solu ions,
while s uden s bene i om hands-on p ojec s, indus y men o ing, and di ec pa hs o employmen .
The key s akeholde g oups – s uden s, ins uc o s, adminis a ion, IT depa men s, and ex-
e nal pa ne s (IT companies, EdTech de elope s) – and he di ec ions o hei in e ac ion du ing
digi al ans o ma ion a e p esen ed in Fig 5.9.
Fig. 5.9 Ne wo k s uc u e o s akeholde in e ac ion in he p ocess o AI in eg a ion in o
echnical highe educa ion
Go e nmen
Lea ne s
Quali y
assu ance
agencies
Employe s
NGOs
Employe
o ganiza ions
Highe
educa ional
ins i u ions
Ins uc o s se e as in e media ies be ween he adminis a ion and s uden s, while also col-
labo a ing wi h IT companies in he de elopmen o educa ional con en . S uden s in e ac no only
wi h ins uc o s, bu also indi ec ly – h ough LMS in e aces – wi h echnical se ices.
This ne wo k s uc u e highligh s ha success ul AI implemen a ion in educa ion is no me ely
a echnological shi , bu an o ganiza ional one, whe e coo dina ion among all educa ional en i on-
men pa icipan s plays a c ucial ole.
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5.3.1 In eg a ion o AI in o academic cou ses
The de elopmen o digi al and AI- ela ed compe ences is suppo ed h ough a se o dedica ed
cou ses in oduced in o he academic cu iculum, such as:
1. A i icial In elligence, Machine Lea ning, and In elligen Da a Analysis – o e ed o s uden s
in p og ams like Compu e Science (122), So wa e Enginee ing (121), Applied Ma hema ics (113),
and In o ma ion Sys ems (126).
2. Founda ions o AI and Digi al T ans o ma ion – a ailable as an elec i e o s uden s om
non- echnical ields.
3. Decision Suppo Sys ems, Py hon P og amming, and Neu al Ne wo ks and Compu a ional
In elligence – included in mas e ’s p og ams.
Some cou ses a e co-designed in collabo a ion wi h leading IT companies such as So Se e
and EPAM [34]. This pa ne ship acili a es he inclusion o indus y-o ien ed con en and enables
s uden s o wo k wi h eal-li e case s udies.
5.3.2 Digi al lea ning en i onmen s enhanced by AI ools
L i Poly echnic Na ional Uni e si y ac i ely employs blended lea ning pla o ms, pa icula ly
Moodle, which is in eg a ed wi h analy ics modules and p edic i e algo i hms [35]. In pilo se ings,
he uni e si y has in oduced:
– au oma ed code assessmen sys ems;
– pa e n ecogni ion in s uden esponses;
– gene a ion o pe sonalized assignmen s using gene a i e AI.
Tools such as Cha GPT, Gi Hub Copilo , and No ion AI a e inc easingly used by s uden s o
assis in s udy p epa a ion, solu ion modeling, w i ing e lec ions, and p epa ing p esen a ions.
Pe sonalized lea ning sys ems using neu al ne wo ks ha e demons a ed success in adap ing
indi idual s udy plans o echnical s uden s [36]. Fu he mo e, he use o sen imen analysis and
neu al-ne wo k quali y-managemen ools in educa ion and heal hca e has been alida ed in simila
educa ional se ings [37–39].
Acco ding o he su ey esul s [Fig. 5.10], he mos widely ecognized bene i associa ed
wi h he use o AI in p o essional educa ion is pe sonalized lea ning (84%). This indica es a s ong
demand o indi idual lea ning pa hs suppo ed by in elligen sys ems.
O he signi ican ac o s include imp o ed quali y o educa ion (75%) and he de elopmen o
digi al skills (66%).
Less han hal o he esponden s iden i ied assessmen op imiza ion (39%) as a key bene i ,
which may e lec a lack o awa eness abou he echnical capabili ies o AI in knowledge e alua ion.
In esponse, ins uc o s ha e de eloped new assessmen o ma s – including analy ical epo s,
mini-p ojec s, and case s udies – aimed a os e ing c i ical hinking and p omo ing deepe lea ning.
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Fig. 5.10 Su ey esul s on he pe cei ed bene i s o using a i icial in elligence in
p o essional educa ion
100 %
90 %
80 %
70 %
60 %
50 %
40 %
40 %
30 %
20 %
10 %
0 %
Pe sonalized
lea ning
Inc eased
lea ning
De elopmen
cou ces
Access o
esou ces
Adap a i e
lea ning
Au oma ic
g ading
O he
84 %
75 %
66 %
53 %
47 %
39 %
9 %
The Fig. 5.11 illus a es how many AI- ela ed bene i s each esponden selec ed. Mos e-
sponden s iden i ied i e o se en key ad an ages, indica ing a b oad pe cep ion o AI’s alue wi hin
he s uden communi y. This dis ibu ion e lec s a high le el o awa eness among pa icipan s
ega ding he di e se po en ial o AI in educa ion, pa icula ly in a eas such as adap i e lea ning,
AI e hics, and big da a analy ics.
Fig. 5.11 Dis ibu ion o he numbe o AI bene i s selec ed by esponden s
40
35
30
25
20
15
10
10
5
05 4 3
Numbe o bene i s selec ed
Pe cen age o esponden s
2 1
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5.3.3 S uden ini ia i es and esea ch p ojec s
L i Poly echnic Na ional Uni e si y ac i ely suppo s you h-led esea ch ini ia i es ela ed o
a i icial in elligence. Uni e si y-o ganized AI hacka hons, s a up compe i ions, and c oss- acul y
inno a ion hubs engage s uden s in de eloping p ojec s in a eas such as:
– sma ci y echnologies;
– ene gy e iciency;
– digi al assis an s;
– educa ional pla o ms wi h adap i e lea ning ea u es.
Some s uden heses and mas e ’s p ojec s al eady implemen ML algo i hms using ools such
as Tenso Flow, sciki -lea n, and OpenCV, demons a ing he g adual in eg a ion o AI in o s uden s’
p o essional skill se s du ing hei s udies [40].
A he same ime, pedagogical esea ch a L i Poly echnic Na ional Uni e si y highligh s he
ole o educa ional coaching and in e disciplina y lea ning in enhancing s uden mo i a ion and cog-
ni i e engagemen . S udies show ha ac i a ing s uden s’ lea ning po en ial h ough coaching
me hods and in eg a ing o eign language ins uc ion wi hin p o essional educa ion con ibu es o
highe au onomy and eadiness o digi al lea ning en i onmen s.
5.3.4 In e na ional p ojec s and collabo a ions
L i Poly echnic Na ional Uni e si y ac i ely pa icipa es in a ange o in e na ional educa ional
ini ia i es ha suppo he in eg a ion o a i icial in elligence and digi al ans o ma ion in o highe
educa ion. Among hem a e he E asmus+ Jean Monne p ojec s ocused on he digi aliza ion o
go e nance and educa ion in Uk aine, as well as he Bal ic Uni e si y P og amme, which p omo es
sus ainable de elopmen modeling using analy ical and AI-based ools.
Addi ionally, he uni e si y is in ol ed in specialized E asmus+ Key Ac ion 2 (KA2) conso ia, such as:
– “E ec i eness o Medicine E-lea ning Dis ance Cou ses”, an in e na ional collabo a i e p ojec
co-led by P o . N. Shakho ska in pa ne ship wi h he Uni e si y Lumiè e Lyon 2 (F ance), he Poly-
echnic Uni e si y o Valencia (Spain), Linnaeus Uni e si y (Sweden), and o he s. This p ojec ocuses
on he applica ion o AI-based adap i e lea ning sys ems in medical educa ion and digi al pedagogy;
– “iCa e4Nex ” emphasizes inclusi e digi al lea ning, accessibili y, and digi al suppo mech-
anisms o s uden s wi h disabili ies and e e ans. AI is used in his con ex o de elop in elligen
u o ing and suppo sys ems ha adap o use s’ cogni i e and emo ional s a es;
– “SmallAIM (AI in Medicine)”, coo dina ed unde he Eu izone ini ia i e, explo es he appli-
ca ion o explainable AI models in medical diagnos ics and e-lea ning sys ems, in eg a ing e hical
conside a ions and anspa ency.
These in e na ional collabo a ions no only aise awa eness abou he po en ial o a i icial
in elligence among s uden s and acul y bu also enable he ans e o inno a i e ins uc ional
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app oaches in o he Uk ainian educa ional con ex . Th ough such p ojec s, L i Poly echnic Na ional
Uni e si y con ibu es o he o ma ion o a sha ed Eu opean educa ional space based on digi al
inclusion, sus ainabili y, and da a-d i en pedagogy.
5.3.5 Indus y-suppo ed educa ional p og ams: he case o L i IT clus e
In esponse o he g owing demand o indus y- ele an compe encies, L i Poly echnic Na ion-
al Uni e si y has pa ne ed wi h he L i IT Clus e o mode nize i s bachelo ’s deg ee p og ams.
This collabo a ion esul ed in he c ea ion and implemen a ion o cu ing-edge cu icula ac oss
mul iple disciplines, e lec ing he la es ends in a i icial in elligence, digi al sys ems, and
da a analy ics.
The upda ed p og ams include:
– Robo ics (G6 In o ma ion-Measu ing Technologies) – a ge ing applica ions in medicine, de-
ense, and space;
– In e ne o Things (F3 Compu e Sciences, Sys ems Enginee ing) – aining specialis s o
design sma , in e ne -connec ed sys ems;
– Cybe secu i y (F5 Cybe secu i y and In o ma ion P o ec ion) – p epa ing expe s o p o ec
digi al in as uc u e;
– A i icial In elligence (F3 Compu e Sciences, AI Sys ems) – ocusing on de eloping AI-based
echnologies and applica ions;
– De Ops & Da a Enginee ing (F6 In o ma ion Sys ems and Technologies) – eaching s uden s
how o manage complex digi al ecosys ems;
– Business Analysis & Da a Science (F4 Sys em Analysis) – equipping u u e p o essionals wi h
analy ical and decision-making skills;
– IT Sales Managemen (F4 Sys em Analysis, IT P oduc Managemen ) – aining s uden s in
p oduc managemen and ma ke s a egies;
– UI/UX Design (G20 Publishing and Polyg aphy) – me ging echnology wi h aes he ics o
c ea e use -cen e ed in e aces.
These p og ams a e de eloped wi h he ac i e pa icipa ion o IT p o essionals and egula ly
upda ed o e lec he needs o he digi al labo ma ke . Thanks o his ini ia i e, s uden s gain ac-
cess no only o up- o-da e heo e ical knowledge bu also o eal-wo ld p ac ices and in e nships
wi h pa ne companies.
Conclusion o Sec ion 5.3
The implemen a ion o a i icial in elligence a L i Poly echnic Na ional Uni e si y exempli ies
a s a egic and comp ehensi e app oach o educa ional inno a ion. Th ough he in eg a ion o
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AI- ela ed cou ses, he use o in elligen digi al lea ning en i onmen s, and ac i e engagemen in
in e na ional p ojec s, he uni e si y has es ablished i sel as a leade in os e ing AI compe encies
among bo h s uden s and acul y.
Impo an ly, hese ini ia i es go beyond echnology adop ion – hey eshape he pedagogical
cul u e, s imula e in e disciplina y collabo a ion, and align educa ional ou comes wi h he demands
o he digi al economy. The ongoing ins i u ional commi men o AI-d i en ans o ma ion e lec s
no only cu en global ends bu also a p oac i e ision o he u u e o echnical educa ion.
5.4 Challenges and e hical aspec s o using AI in he educa ional p ocess
Despi e he nume ous bene i s ha a i icial in elligence echnologies b ing o p o essional
highe educa ion, hei implemen a ion is accompanied by a ange o challenges: echnical, pedagog-
ical, and e hical. In echnical uni e si ies, whe e AI is used no only as a lea ning ool bu also as a
componen o p o essional p ac ice, he issue o esponsible AI use becomes pa icula ly impo an .
5.4.1 Academic in eg i y in he age o gene a i e AI
One o he mos deba ed challenges is he use o gene a i e AI models (such as Cha GPT,
Claude, and Gi Hub Copilo ) by s uden s o p oduce ex s, answe s, code, o epo s. In he ab-
sence o clea ly de ined policies on AI usage in highe educa ion, se e al isks a ise:
– academic plagia ism;
– loss o independen c i ical hinking skills;
– au oma ion o asks wi hou eal unde s anding o he con en .
In esponse o hese isks, ins uc o s a L i Poly echnic a e de eloping new assessmen o ma s:
analy ical asks wi h pe sonalized elemen s, open-ended discussions, and mini-p ojec s ha equi e
s uden s o jus i y hei hough p ocesses. The e is also ongoing deba e a ound accep able AI usage,
aiming o dis inguish be ween esponsible assis ance and inapp op ia e subs i u ion o human wo k.
5.4.2 Su ey esul s on ba ie s o AI adop ion
To iden i y ba ie s o he e ec i e in eg a ion o AI in o p o essional educa ion, a su ey was
conduc ed among s uden s and acul y o echnical disciplines. The esul s a e p esen ed in Fig. 5.12.
The mos signi ican ba ie , acco ding o esponden s, is he lack o app op ia e use
skills (65%), highligh ing he u gen need o sys ema ic aining o bo h s uden s and ins uc o s.
A conside able pe cen age also emphasized e hical isks (53%) and echnical in as uc u e lim-
i a ions (49%).
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O he ba ie s, such as lack o unding (45%) and legal cons ain s (18%), poin o he impo ance
o ex e nal suppo and egula o y amewo ks o he in eg a ion o digi al inno a ions in educa ion.
Fig. 5.12 Su ey esul s on ba ie s o he use o a i icial in elligence in he educa ional p ocess
Lack o
ele an
skills
Technical
limi a ions
70
60
50
40
30
20
10
10
0
65 %
53 % 49 % 45 %
18 %
Insu icien
unding
Legisla i e
ba ie s
E hical
issues
Pe cen age o esponden s
5.4.3 Pe cep ion o p oblem complexi y: How many ba ie s do esponden s iden i y?
The Fig. 5.13 illus a es how many ba ie s each esponden ma ked as signi ican . This allows
us o assess whe he he p oblem o AI in eg a ion in educa ion is pe cei ed na owly o b oadly
by s akeholde s.
Fig. 5.13 Dis ibu ion o he numbe o AI- ela ed ba ie s iden i ied by esponden s
Numbe o esponden s, %
Numbe o obs acles selec ed
5
4
3
2
1
60
50
40
30
20
10
0
0
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The da a sugges a nea -no mal dis ibu ion: mos esponden s selec ed h ee key ba ie s,
indica ing a balanced and comp ehensi e pe cep ion o he issue. A small po ion iden i ied only one
o as many as i e ba ie s, e lec ing a ying le els o awa eness o pe sonal expe ience wi h AI
in eg a ion in educa ion. This dis ibu ion unde sco es he need o a di e en ia ed app oach o
add essing hese challenges – om basic aining o ins i u ion-wide suppo policies.
5.4.4 Digi al access inequali y
No all lea ne s ha e equal access o mode n digi al ools o s able in e ne connec ions –
pa icula ly unde wa ime condi ions o in blended/ emo e lea ning se ings. This aises conce ns
ha he in eg a ion o AI echnologies may deepen educa ional inequali y.
In his con ex , i is c ucial o uni e si ies o p o ide:
– open-access esou ces and local se e s wi h AI capabili ies (wi hin in e nal in as uc u e);
– baseline digi al li e acy aining o all s uden s ega dless o majo ;
– onboa ding sessions on using AI ools (e.g., No ion AI, Copilo , OpenCV, RapidMine ) a he
beginning o he academic yea .
5.4.5 Ins uc o aining and suppo
The success ul in eg a ion o AI equi es no only echnical upg ades bu also a shi in he ole
o ins uc o s. No all educa o s possess su icien expe ience wi h digi al ools, which can lead o:
– anxie y abou new echnologies;
– challenges in managing he lea ning p ocess;
– esis ance o change due o lack o suppo o inc eased wo kload.
L i Poly echnic g adually implemen s p o essional de elopmen p og ams in EdTech, digi al
pedagogy, and AI ools. These include aining wo kshops, summe schools, and in ol emen o
acul y in c oss-depa men al digi aliza ion p ojec s.
5.4.6 E hical use o AI in educa ion
The e is g owing global a en ion o AI e hics. Key p inciples ha educa ional ins i u ions
should adhe e o include:
– algo i hmic anspa ency (unde s anding how AI sys ems make decisions);
– non-disc imina ion (elimina ing bias in da a o models);
– da a p o ec ion (especially when analyzing s uden pe o mance o handling pe sonal da a);
– espec o human au onomy (AI as an assis i e ool, no a eplacemen o human inpu ).
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Uni e si ies adop ing AI should de elop hei own e hical guidelines o digi al ools, clea ly
de ining bounda ies, accoun abili y, con iden iali y, and openness.
Conclusion o Sec ion 5.4
The in eg a ion o a i icial in elligence in o he educa ional p ocess o echnical uni e si ies
b ings bo h ans o ma i e oppo uni ies and c i ical challenges. While AI can signi ican ly enhance
lea ning pe sonaliza ion, con en gene a ion, and da a-d i en decision-making, i s implemen a ion
mus be app oached wi h cau ion and esponsibili y. The indings indica e ha insu icien use
skills, e hical conce ns, and in as uc u e limi a ions emain key ba ie s o e ec i e AI adop-
ion. Mo eo e , unequal digi al access, lack o ins uc o p epa edness, and he po en ial e osion
o academic in eg i y due o misuse o gene a i e AI ools highligh he need o ins i u ional s a -
egies ha combine echnical, pedagogical, and e hical sa egua ds. Uni e si ies mus he e o e no
only in es in digi al in as uc u e and p o essional de elopmen bu also es ablish anspa en
and inclusi e policies o guide he esponsible use o AI. By add essing hese challenges h ough a
holis ic and equi y- ocused app oach, highe educa ion ins i u ions can ensu e ha he in eg a ion
o AI s eng hens a he han unde mines he quali y and in eg i y o academic p ocesses.
5.5 A model o implemen ing AI in p o essional educa ion a a echnical uni e si y
Success ul digi al ans o ma ion o he educa ional p ocess a echnical highe educa ion
ins i u ions equi es no a agmen ed adop ion o indi idual digi al ools, bu a sys ema ic model
o in eg a ing a i icial in elligence (AI) in o all s ages o p o essional aining. Such a model should
be based on an in e disciplina y app oach, p ac ical o ien a ion, adhe ence o e hical p inciples, and
he de elopmen o bo h s uden and acul y digi al compe encies.
5.5.1 Implemen a ion le els
The model can be ep esen ed as a h ee-le el s uc u e:
a) Le el 1 – AI li e acy: de elopmen o basic knowledge abou he p inciples o AI, machine
lea ning, algo i hms, and hei socie al impac . This le el should be accessible o all s uden s,
ega dless o hei ield o s udy.
Implemen a ion me hods: in eg a ed lec u es, online cou ses, and semina s;
b) Le el 2 – p o essional applica ion o AI: using AI as a ool wi hin he amewo k o a speci ic
discipline: o example, o ecas ing in economics, digi al wins in mechanical enginee ing, da a
analysis in he ene gy sec o , code gene a ion and e i ica ion in IT.
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Implemen a ion: h ough specialized cou ses, labo a o y wo k, and p ac ical aining;
c) Le el 3 – esea ch and inno a ion Le el: engaging s uden s in in e disciplina y esea ch
p ojec s, hacka hons, and hesis p ojec s using AI echnologies. Ac i e collabo a ion wi h IT com-
panies, pa ici pa ion in in e na ional educa ional ini ia i es, and submi ing s a up ideas o inno-
a ion compe i ions.
5.5.2 Key componen s o he model
Ins i u ional Policy:
– de ining a clea s a egy o digi al ans o ma ion;
– de eloping an e hical code o he use o AI in educa ion;
– suppo ing educa ional ini ia i es a he ec o a e le el.
Educa ional P og ams and S anda ds:
– upda ing academic p og ams o include AI-o ien ed componen s;
– designing in e disciplina y cou ses;
– implemen ing mic o-quali ica ions and ce i ica ion modules (e.g., AI o Enginee s, AI o Teache s).
Facul y De elopmen :
– o e ing p o essional de elopmen cou ses in AI/EdTech;
– acili a ing expe ience exchange among depa men s and acul ies;
– p o iding men o ship o junio acul y in wo king wi h digi al ools.
In as uc u e:
– access o open esou ces (Google Colab, Hugging Face, Kaggle);
– a ailabili y o AI labo a o ies and GPU-suppo ed se e s;
– equipping class ooms o hyb id and simula ion-based lea ning.
In eg a ion wi h he Labo Ma ke :
– collabo a ion wi h IT companies in p og am de elopmen ;
– s uden in e nships in AI- ocused eams;
– o ganiza ion o wo kshops, gues lec u es, and ce i ica ions in ol ing indus y p o essionals.
5.5.3 Visualiza ion o he bene i s o AI in eg a ion
The mul iple in e connec ions be ween he bene i s o AI use in educa ion a e shown in Fig. 5.14.
The nodes wi h he highes numbe o associa i e connec ions a e pe sonalized lea ning, ac-
cess o esou ces, and p epa a ion o he digi al labo ma ke . This indica es ha hese compo-
nen s o m he co e pe cep ion o AI e ec i eness in p o essional educa ion. The O he node is
linked by only a single edge, e lec ing i s limi ed signi icance. The hickness o he connec ing lines
ep esen s he equency wi h which esponden s selec ed he connec ed bene i s simul aneously.
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