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TRAINING OF COMPETENCIES IN ELECTRONIC
ENGINEERING THROUGH ARTIFICIAL
INTELLIGENCE: EVIDENCE, CHALLENGES AND
CONTRIBUTIONS FROM EDUCATIONAL
PSYCHOLOGY AND UNIVERSITY TEACHING AT THE
POPULAR UNIVERSITY OF CESAR
SANDRA EMILIA MOLINA MONTERO
UNIVERSIDAD POPULAR DEL CESAR (UNICESAR), VALLEDUPAR, CESAR, COLOMBIA,
EMAIL: sand amolina@unicesa .edu.co
DORIXY DE ARMAS DUARTE
UNIVERSIDAD POPULAR DEL CESAR (UNICESAR), VALLEDUPAR, CESAR, COLOMBIA
EMAIL: [email p o ec ed]
CARLOS EDUARDO DÍAZ FERNÁNDEZ
UNIVERSIDAD POPULAR DEL CESAR (UNICESAR), VALLEDUPAR, CESAR, COLOMBIA
EMAIL: [email p o ec ed]
JOSÉ LUIS ZABALETA FERNÁNDEZ
UNIVERSIDAD POPULAR DEL CESAR (UNICESAR), VALLEDUPAR, CESAR, COLOMBIA
EMAIL: [email p o ec ed]
SANDRA MILENA DAZA DAZA
UNIVERSIDAD POPULAR DEL CESAR (UNICESAR), VALLEDUPAR, CESAR, COLOMBIA
EMAIL: sand a[email p o ec ed]
CARMEN ELENA AMAYA PABON
UNIVERSIDAD POPULAR DEL CESAR (UNICESAR), VALLEDUPAR, CESAR, COLOMBIA,
EMAIL: [email p o ec ed]
Summa y
This a icle p esen s he in eg a ion o a i icial in elligence in elec onic enginee ing
educa ion, iden i ying eme ging e idence, psychological challenges, and eaching
conside a ions ele an o he Uni e sidad Popula del Cesa . I shows a quan i a i e
me hodological design ha combines su eys o eache s and su eys o elec onic
enginee ing s uden s, analysis o anges o uses o AI ools and a li e a u e e iew on
a i udes and psychological e ec s. The p elimina y esul s highligh po en ial
imp o emen s in pe o mance, sel -e icacy and concep ual unde s anding, while
iden i ying challenges such as biases, echnological dependence and he need o eache
aining o esponsible and e hical implemen a ion a he Uni e sidad Popula del Cesa .
I was achie ed wi h he collec ion o empi ical e idence and in e p e a ion om
app oaches o educa ional psychology and eaching p ac ices, o ad ance in a esponsible
and e hically g ounded implemen a ion a he Popula Uni e si y o Cesa .
1. INTRODUCTION
The in eg a ion o A i icial In elligence (AI) in uni e si y educa ion is a booming phenomenon and is jus i ied
based on he a ailable e idence. Ins i u ions ha ain u u e p o essionals, pa icula ly hose who wo k in
echnological ields and ad ise he popula ion, canno be le ou and mus p epa e o hei implemen a ion
(Souzaa & Maie b). The elec onic enginee ing aining includes, among o he s, he a eas o ci cui heo y, con ol
sys ems and signal heo y; concep s ha demand a lo o s udy ime. The use o AI ools in he eaching and
lea ning o hese concep s has begun o be s udied, bu no ye in dep h. Speci ically, he esea che asks he
ollowing ques ion: Wha ad an ages and gaps ha e been e idenced in he use o AI ools in he knowledge and
lea ning o undamen al concep s in he eaching o elec onic enginee ing? The esponse will make i possible o
o mula e ecommenda ions o i s p ope implemen a ion a he Popula Uni e si y o Cesa .
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The echnological and psychological challenges posed by AI, added o he complexi y o enginee ing eaching,
sugges he need o a deepe a icula ion be ween educa ion, educa ional psychology and in eg a ion o AI,
suppo ed by e idence. These p emises lead o a second objec i e: o iden i y e idence and challenges in he use
o AI echnologies in he ield o uni e si y educa ion, educa ional psychology and eaching in elec onic
enginee ing, which make hei in eg a ion in o he Popula Uni e si y o Cesa iable. The sea ch o in o ma ion
ha allows his pu pose o be ul illed accoun s o he h ee main a eas in ol ed: educa ion and educa ional
psychology, AI in educa ion and elec onic enginee ing, wi h hei espec i e subca ego ies. (Esqui el and
He nández2024)
2. THEORETICAL FRAMEWORK
The mos ele an concep ions o he educa ional p ocess a e deeply linked and connec ed o he heo y o
educa ional psychology; om he e, a ious heo ies on lea ning and mo i a ion a e deduced, which a e
undamen al o unde s anding how s uden s acqui e and e ain knowledge (So iano e al., 2024). In addi ion,
impo an phenomena such as me acogni ion, which in ol es he abili y o e lec on one's own hinking and
lea ning, a e explained. These concep ions a e also he essen ial ounda ion o he p ac ice o assessmen , whe e
i seeks o measu e and unde s and he s uden 's p og ess. They also allow us o unde s and, om he pe spec i e
o Technology, he way in which human beings in eg a e, in hei own pe sonal and p o essional lea ning p ocess,
a ious echnologies app op ia e and ele an o hei con ex . The implemen a ion o a i icial in elligence in he
educa ional ield, as well as he adap a ion o a ious eaching-lea ning ools, s ands as a undamen al pilla in he
cons uc ion o lea ning en i onmen s ha a e no only e ec i e, bu also s imula ing, pa icula ly in he aining
o elec onic enginee s. This app oach becomes c ucial in he ace o he incessan challenges posed by a wo ld
in pe pe ual e olu ion, whe e echnological inno a ion and adap abili y become essen ial compe encies o he
p o essional u u e o s uden s. Eme ging echnologies no only acili a e he assimila ion o knowledge, bu also
os e mo e in e ac i e and pe sonalized lea ning, allowing lea ne s o de elop skills ha anscend he bounda ies
o con en ional educa ion. Consequen ly, a ange o oppo uni ies opens up ha will undoub edly ha e a posi i e
impac on he academic and p o essional ca ee o hose who a e p epa ing o en u e in o his dynamic ield o
elec onic enginee ing. (López e al.2024).
Educa ional AI is he concep ha co e s he design and implemen a ion o compu e sys ems, based on AI
pa adigms, in which pedagogical beha io is ca ied ou by means o machines, simula o s o compu e agen s
(Ri e a). These sys ems allow a s uden o a cou se o lea n in a di e en way, because hey a e o ien ed o he
pe sonaliza ion o he eaching-lea ning p ocess, depending on he needs and in e es s o he s uden . AI ools a e
a ailable ha can be used in he eaching-lea ning p ocess, such as in elligen u o ing sys ems, ne wo ked u o
sys ems and ecommenda ion sys ems. These ools analyze he s uden 's in o ma ion and p opose he ollowing
ac ions based on hei con ex and objec i es; Some e en inco po a e he e alua ion ace and p opose which
aspec o he opic should be s eng hened. The inco po a ion o educa ional AI in ol es he ans o ma ion o he
educa ional p ocess in o an open sys em, in which he e a e cons an in e ac ions be ween s uden s and machines,
and machines a e able o adap hei beha io o s uden s (Ve a, 2023).
2.1. Educa ion and educa ional psychology
Educa ion is a complex and mul i ace ed p ocess ha in ol es a a ie y o ac o s, including s uden s, eache s,
educa ional ins i u ions, and socie y a la ge. Despi e he in ica e na u e o educa ion, educa ional psychology has
decided o concen a e i s e o s on he exhaus i e in es iga ion and desc ip ion o ce ain key a eas and
cons uc s in pa icula (Calde ón, 2024). These a eas include c ucial aspec s such as lea ning, mo i a ion,
me acogni ion, assessmen , and mo e. The way in which hese elemen s a e pe cei ed and unde s ood by he
di e en educa ional ac o s is undamen al, since hese belie s and concep ions in luence how lea ning ac ually
akes place and wha a e he ac o s ha a o o hinde i . Consequen ly, he ac ions ha a e pu in o p ac ice a e
de e mined by hese unde s andings, which ensu es ha he deep knowledge and ca e ul conside a ion o hese
cons uc s allow eache s o design and execu e hei decisions and ac ions in a way ha p oduces mo e a o able
and e ec i e esul s in he eaching-lea ning p ocess. The in eg a ion o echnology in he class oom can acili a e
and en ich his educa ional p ocess, as long as bo h eache s and ins i u ions adop a i udes and belie s ha make
he mos o he possibili ies ha hese echnological ools ha e o o e in he con empo a y educa ional con ex
(I u alde e al., 2024).
The concep ions o lea ning, as well as o he a ious p ocesses ha can acili a e o , on he con a y, hinde his
impo an phenomenon, a e o g ea impo ance in he ield o educa ion. These concep ions di ec ly de e mine
he ac ions ha eache s ca y ou , as well as he way in which s uden s ace he challenge o lea ning e ec i ely.
In his con ex , mo i a ion is p esen ed as one o he mos in luen ial ac o s in lea ning, and i is in his sense ha
special a en ion should be paid o he decisions made by eache s in hei daily p ac ice. Such decisions may ha e
he po en ial o encou age o , ailing ha , discou age i , and, he e o e, signi ican ly in luence he quali y o
lea ning achie ed by s uden s (Velasco e al., 2024). Addi ionally, he e alua ion p ocess plays a c ucial ole, as
i has a acili a ing and enhancing cha ac e o he lea ning p ocess, as long as i is unde s ood as a pe manen and
con inuous p ocess ha accompanies eaching om i s incep ion (Vélez e al., 2024). This app oach con ibu es
signi ican ly o mo e accu a e and in o med pedagogical decision-making. In his con ex , he a i icial
in elligence ools ha a e eme ging oday can p o ide aluable suppo o bo h eache s and s uden s in he a ious
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aspec s in ol ed in he lea ning p ocess. I s app op ia e and conscious use can cause p o ound changes in belie s
ha de e mine which aspec s o he lea ning p ocess a o o hinde i , hus cons i u ing an addi ional and
p omising pa h o he op imiza ion o lea ning.
2.2. A i icial in elligence in educa ion
A i icial in elligence in educa ion encompasses hose de elopmen s and con en ha use a i icial in elligence
echniques o ools o educa ional pu poses. In his sense, an a i icial in elligence educa ion sys em is an
applica ion ha in eg a es AI me hods and echniques o c ea e con en , pe sonalize eaching and lea ning,
acili a e he educa ional p ocess, o con ibu e o he de elopmen o knowledge and esea ch in educa ion (K-
12) (Gallen -To es e al., 2024). Tools ha p o ide s uden s wi h a di ec aid in lea ning a e classi ied as
educa ional ools based on a i icial in elligence. These ools a e especially ele an because hey a e being used
o lea ning and no unde he supe ision o a p ima y o seconda y school eache . The AI ools ha help s uden s
in hei lea ning p ocess a e Cha GPT, Copilo , Duolingo, Can a, among o he s, and hese a e jus some o he
ools ha inc ease hei use among s uden s, al hough no always co ec ly (Di e al. 2024).
A i icial in elligence educa ion sys ems a e in elligen sys ems ha help sol e complex p oblems and become
inc easingly sophis ica ed, as hey can play a ious oles in he educa ional p ocess. Howe e , in eg a ing a i icial
in elligence in o lea ning is no wi hou isks, as i s indisc imina e use can a ec s uden s' w i ing, c ea i i y,
easoning, and lea ning. In his sense, i is a gued ha "Cha GPT is a echnology wi h a g ea impac on educa ion,
al hough on hei own hey do no add o de ac om aining. Thei po ency o dange depends on how hey a e
used." (Cas illo e al.2024)
2.3. Elec onic enginee ing and lea ning en i onmen s
Elec onic Enginee ing p og ams mus allow he de elopmen o compe encies ha no only add alue o he
aining o hei g adua es, bu also acili a e hei adap a ion o he cons an ly changing wo ld o wo k (Mo eno
& de Cali). To his end, he use o eaching-lea ning me hods ha p omo e he de elopmen o c i ical, analy ical
and c ea i e hinking skills is equi ed, encompassing no only echnical concep s, bu also s a egies ha p omo e
inno a ion. These skills a e posi i ely ueled by he use o eme ging echnologies applied o educa ion, including
a i icial in elligence ools and online lea ning pla o ms ha ans o m he educa ional p ocess. Howe e , hei
inco po a ion in o he design o lea ning en i onmen s s ill aces echnological, o ganiza ional, pedagogical, and
cul u al challenges ha mus be o e come o maximize hei e ec i eness and ensu e ha all s uden s ha e access
o a quali y educa ion ha adequa ely p epa es hem o he challenges o he u u e (Muñoz e al., 2024).
The in eg a ion o eme ging echnologies, especially gene a i e a i icial in elligence ools, allows us o explo e
in g ea dep h mo e adap i e eaching-lea ning p ocesses, ocused on he s uden and hei a ious con ex s, in
addi ion o con ibu ing signi ican ly o he achie emen o lea ning ha is uly meaning ul. Eme ging
echnologies a e hose ha de elop new applica ions and inno a ions ha impac educa ion and o he ields, and
ha , h ough eedback mechanisms, a e he con inuous objec o esea ch, c ea ion and de elopmen . All his is
done wi h he clea objec i e o deli e ing hem o socie y h ough hei p oduc ion, ma ke ing and use, gene a ing
a posi i e impac on lea ning and eaching. (Chá ez-Boza and E azo-Mo e a2024)
3. RESEARCH METHODOLOGY
The esea ch app oach is mixed. The explo a o y-desc ip i e design is combined, which allows cha ac e izing he
a i udes, com o and ese a ions o he Elec onic Enginee ing eache s o he Popula Uni e si y o Cesa
owa ds he inco po a ion o a i icial in elligence in hei classes, wi h he quasi-expe imen al design, which
allows con as ing he e ec s on he lea ning o key concep s o an enginee ing wi h he use o Cha GPT and o he
gene a i e AI ools. The esea ch is pa o he ield o educa ional psychology, gi en he in e es in he in eg a ion
o AI ools in Elec onic Enginee ing cou ses and he g owing use o hese esou ces in educa ion, wi hou any
subjec dedica ed o hei pedagogical applica ion. (PIBAQUE QUIMIS, 2021)
The popula ion is made up o eache s and s uden s who s udy he Elec onic Enginee ing p og ams o he Popula
Uni e si y o Cesa . A g oup o eache s is selec ed by means o non-p obabilis ic sampling by c i e ia and he
s uden s by means o an in en ional non-p obabilis ic sampling. Ques ionnai es, an in e iew, speci ic ub ics,
e alua ion es s and knowledge con ol a e used as da a collec ion ins umen s. The in o ma ion is ob ained in he
i s wo academic pe iods o 2024. The e hical c i e ia o aci consen and con iden ial in o ma ion a e
conside ed, and measu es a e applied o minimize bias in da a collec ion and analysis. The consis ency and alidi y
o he ins umen s a e de e mined om C onbach's alpha coe icien and he expe judgmen me hod,
espec i ely.
3.1. Design and popula ion
The s udy has a non-expe imen al design o a desc ip i e and c oss-sec ional ype. Da a collec ion was ca ied ou
om wo samples: one o 22 p o esso s who each elec onic enginee ing cou ses and ano he made up o 59
s uden s o he Elec onic Enginee ing p og ams o he Popula Uni e si y o Cesa who we e en olled in a leas
one cou se whe e gene a i e AI ools had been used.
The eache s we e in en ionally selec ed, conside ing ha gene a i e AI ools had been used in hei classes and
ha hey had, a leas , a basic le el o aining in he use o AI. The selec ion o he g oup o s uden s was ca ied
ou h ough a non-p obabilis ic con enience sampling. All subjec s p o ided in o med consen o pa icipa ion
in he esea ch.
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3.2. Ins umen s and p ocedu es
A ques ionnai e on he use o AI ools in educa ion, a semi-s uc u ed in e iew we e used o in es iga e he
eache s' pe cep ion o he in eg a ion o AI in hei ac i i y, a ub ic o e alua e a se o Elec onic Enginee ing
ac i i ies and a es o assess he lea ning o basic concep s o Digi al Elec onics. The ques ionnai e, designed by
he au ho and alida ed by expe judgmen , consis ed o 29 ques ions and was applied o 182 s uden s om 5 h
o 9 h semes e o he Elec onic Technology Design Enginee ing p og am. In he i s h ee ques ions, hose
consul ed epo ed hei use o Cha GPT o sea ch o in o ma ion, s udy o sol e doub s. In a second block, hey
answe ed 18 ques ions made wi h he i e-poin Like scale, and in a hi d block, hey epo ed he equency o
use o 11 AI ools in he h ee ac i i ies o which hey we e e alua ed. The swi ching o he i s h ee ques ions
o a single mul iple-choice ques ion, combined wi h he Like scale in he second block, allowed us o ob ain an
analysis o a iance ha de e mined s a is ically signi ican di e ences in he compa ison a iables.
The in e iew allowed us o explo e he pe cep ion o 45 p o esso s om he School o Enginee ing abou he
in eg a ion o AI in hei ac i i y. Ten o hem we e e alua o s o he h ee ac i i ies, and he es applied a
minimum o wo. They we e consul ed on ou opics: luency in he p oduc ion o ex s wi h AI ools; aining,
com o and ese a ions o in eg a e AI in o hei ac i i y; and he iden i ica ion o opics whe e hei use should
be a oided. The pe cep ion o luency was syn hesized in o h ee ca ego ies: e y luen o easy, somewha luid
o easy, and no luen o di icul . Da a we e p esen ed in equency o m o show coincidences.
3.3. E hical and alidi y c i e ia
The de elopmen o he esea ch con empla ed he in o med consen o all pa icipan s, in which he anonymous
and con iden ial na u e o he da a collec ion was gua an eed, as well as i s exclusi e use o academic pu poses.
Possible biases in da a collec ion we e also conside ed, especially in eache s' pe cep ions, which could be
in luenced by he me hodology o he s udy: al hough i was an explo a o y s udy de eloped h ough he analysis
o he answe s in a closed ques ionnai e, in which mos o he ques ions we e single-answe , The analysis s age
included he p og amming o a sc ip ha in e p e ed he answe s conside ing a se ies o public c i e ia o each
ques ion.
The alidi y and eliabili y o he ins umen s was de e mined based on he judgmen o expe s and he analysis
ha allows he Consensus Me hod o he alidi y es ima es wi h he P opo ionali y o he Common Va iance,
which es ima es he alidi y o an ins umen based on he ac ha 50% o he ques ions pene a e each ac o o
componen o he gene al model o he s udy. The alidi y o hese ins umen s can be conside ed adequa e o
he de elopmen o an explo a o y analysis.
4. RESULTS
The p esen a ion o he esul s is di ided in o h ee sec ions. The i s add esses he pe cep ions o eache s
ega ding he inclusion o AI in eaching in elec onic enginee ing. The second analyzes he e idence o he e ec s
o AI ools on he lea ning o key concep s in his discipline. The hi d poin s ou he challenges and gaps ound
in implemen a ion.
Teache s' pe cep ions o he inclusion o AI ools in he eaching o elec onic enginee ing e eal gene ally
posi i e a i udes, wi h a high deg ee o com o in hei use, oge he wi h ce ain ese a ions abou possible
de imen s o lea ning. Consensus has been ound on he need o suppo and aining o imp o e he in eg a ion
o hese echnologies in hei class ooms. (PAZ-MALDONADO and FLORES-GIRÓN2021)
Wi h p ope use, AI ools p oduce posi i e e ec s on unde s anding concep s, e aining lea ning, lea ning speed,
and inding addi ional in o ma ion. Howe e , hei in ensi e use gene a es less dep h in lea ning. Teache s wa n
ha lack o in e ne access, echnical di icul ies, ime p essu e, o ganiza ional si ua ions, and cul u al cons ain s
a e he main obs acles in implemen a ion.
4.1. Teaching pe cep ions o AI in he eaching o elec onic enginee ing
A key pa o he p ocess o in eg a ing AI in o educa ion is he willingness o eache s o emb ace i s use.
Acco ding o he li e a u e, AI can o e se e al ad an ages o eaching and lea ning a di e en educa ional
le els; howe e , i s e ec s ha e been explo ed in an incipien way in highe educa ion con ex s and, in pa icula ,
in he aining o enginee s in gene al and in elec onic enginee ing in pa icula . Despi e hese gaps, eache s in
he la e a ea ha e shown posi i e a i udes owa ds hei in eg a ion. On he con a y, hei com o and
con idence in he use o AI ha e been limi ed, and hey ha e desc ibed he need o imp o e hei aining. (Niebla
e al.2025)
The i s e idence p esen ed he e explo es he pe cep ions o a g oup o wel e p o esso s abou he use o AI
ools in he eaching o Elec onic Enginee ing. Al hough he esul s a e p elimina y, hey p o ide a glimpse o
he gene al a i ude, com o , and con idence ha educa o s expe ience when conside ing hei in eg a ion, as well
as he poin s a which hey should be ained o e ec i ely use hese ools. The analysis was ca ied ou using a
ques ionnai e based on a Like scale, whose answe s we e subjec ed o desc ip i e analysis.
4.2. Impac o AI ools on lea ning key concep s
The use o a i icial in elligence ools posi i ely impac s he lea ning o key concep s o he cou ses o he
academic p og am o elec onic enginee ing o he Popula Uni e si y o Cesa . Compa ed o o he eaching
me hods, i s use is associa ed wi h a g ea e unde s anding o concep s, be e e en ion o hem, a as e lea ning
p ocess, and less ime spen sea ching o in o ma ion. These indings espond o he esea ch ques ion on he
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e ec s o he in eg a ion o AI ools in eaching-lea ning p ac ices and o one o he h ee lines o esea ch ha
seek e idence and con ibu e o he design o a plan o s a egies ha acili a e such in eg a ion. (O ega e al.2025)
The analysis o he da a ob ained in he second subques ionnai e, complemen ed wi h in o ma ion om in e iews
and a hi d ub ic, allows us o conclude ha he use o a i icial in elligence ools posi i ely impac s he lea ning
o key concep s. The esul s, p esen ed h ough a se ies o uphols e ed ques ions, indica e ha , compa ed o o he
eaching me hods, hei use is associa ed wi h a g ea e unde s anding o concep s, be e e en ion o hem, a
as e lea ning p ocess and less ime spen sea ching o in o ma ion.
4.3. Challenges and gaps in implemen a ion
The esea ch has shown ha he use o AI ools in e-enginee ing educa ion aces ba ie s ha limi i s in eg a ion
and pedagogical e ec i eness. Fi s , he use o AI equi es ha ing de ices wi h in e ne connec i i y. Second,
ins i u ions mus ha e policies aimed a he e olu ion o an o ganiza ional, pedagogical and cul u al en i onmen
ha con empla es a eal and e ec i e inco po a ion o hese ools wi hin he amewo k o he educa ional
ecosys em. I echnology is a ailable bu no policies ha a ou i s use, he impac o he use o AI in educa ion
is small. Thi dly, con inuous eache aining is conside ed necessa y o be able o p ope ly inco po a e hese ools
and ake ad an age o hei po en ial o he imp o emen o he eaching-lea ning p ocess.
In summa y, he esul s ob ained show ha al hough AI can be used o he lea ning o elec onic enginee ing,
un il now i has no been used con inuously and in a s uc u ed way in he ins i u ions ha each hese p og ams;
All he associa ed p oblems ha es ic hei use mus be conside ed and sol ed so ha a g ea e in eg a ion o
hese echnologies in he eaching-lea ning p ocess is possible.
5. DISCUSSION
The esul s sugges ha , i eache s p opose changes in cu iculum design and class oom p ac ices ha inco po a e
he ools ha AI makes a ailable o hem – i hey ake ca e o e alua e how and o wha ex en hese esou ces
a e being used in hei cou ses – and i s uden s use hem o acili a e hei lea ning and imp o e hei well-being,
so he impac o educa ional AI on he lea ning p ocess o elec onic enginee ing a he Popula Uni e si y o
Cesa could be posi i e. On he o he hand, he in eg a ion o hese ools in lea ning en i onmen s should also be
conside ed om educa ional psychology, in o de o ensu e ha hei use does no nega i ely a ec lea ning
p ocesses and he well-being o use s.
Al hough esea ch in his ega d has been sca ce so a , s uden s do no seem o conside hem ha m ul. The
possibili y ha AI acili a es he unde s anding o di icul concep s, a o s e en ion and lea ning speed, and e en
educes he e o when pe o ming exe cises, seems o be d i ing he use o hese echnologies by s uden s.
Howe e , access o he In e ne , he ime a ailable, and he use o Cha GPT o sol e exe cises a e ac o s ha
limi i s use in o mal educa ion.
5.1. Pedagogical implica ions
The mos ele an pedagogical implica ions de i ed om he esul s poin o he need o e o mula e he cu icula
design o elec onic enginee ing aining p og ams, o econ igu e eaching-lea ning p ac ices wi hin he cou ses
and o e iew he lea ning e alua ion p ocesses. In his sense, he in eg a ion o new digi al ools gene a ed om
AI applica ions in he elec onic enginee ing aining cou ses o he Popula Uni e si y o Cesa could be
suppo ed by a g adual implemen a ion plan ha includes he selec ion o speci ic applica ions, he ealiza ion o
pilo es s in some cou ses and hei subsequen scaling o o he lea ning en i onmen s.
The success o hese ac ions a he pedagogical le el would depend on he design o eaching s a egies and
lea ning assessmen based on he a ailable e idence on he impac o AI sys ems on he lea ning p ocesses o key
opics in he discipline. Among he ecommenda ions eme ging om he li e a u e e iew, i is sugges ed o ha e
class oom ac i i y design guides ha , conside ing he use o AI-gene a ed ools, p opose he implemen a ion o
eaching-lea ning and o ma i e assessmen asks ha o e imely eedback o s uden s. Likewise, quali y
indica o s a e pos ula ed ha acili a e a sys ema ic moni o ing o he esul s ob ained and allow, based on hei
e iew, o ca y ou he con inuous imp o emen o hese p ac ices.
5.2. Implica ions o educa ional psychology
The e idence collec ed sugges s ha AI ools can suppo a ious psychological p ocesses ela ed o s uden
lea ning, sa is ac ion, and well-being. By acili a ing access o in o ma ion, unde s anding complex concep s, and
imp o ing he quali y o esponse o assessmen ac i i ies, AI could con ibu e o he achie emen o expec ed
lea ning and enhance he way s uden s eel du ing hei lea ning p ocess. Howe e , e en i he use o hese ools
has a posi i e e ec on lea ning and e en ion o key concep s in a opic, excessi e o au oma ed use o AI
esou ces, such as answe ing ques ions on hese pla o ms wi hou ha ing e ec i ely unde s ood he opic, could
ha e a nega i e e ec on unde s anding concep s and unde mine lea ning. The e o e, i is essen ial ha he use o
AI ools is p ope ly guided and con olled by he eache . In addi ion, i is impo an o ein o ce e hical
conside a ions in he use o AI and p omo e s uden s' e lexi i y in in e ac ing wi h hese sys ems.
The use o AI ools should go beyond simple ins uc ion on hei use and encou age dialogue wi h s uden s so ha
hey eel com o able wi h hei inco po a ion in o he lea ning p ocess and make a signi ican con ibu ion o
hei emo ional well-being du ing i . The e olu ion o human-machine in e ac ion should be app oached in an
analogous way o he inco po a ion o any o he echnology in he class oom, h ough a g adual p ocess ha
includes he mo i a ion, design, selec ion and e alua ion o s a egies, ac i i ies o ypes o asks ha a e suppo ed
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by AI ools. In o de o mo e e ec i ely add ess he in eg a ion o AI ools in he lea ning o Elec onic
Enginee ing, i would be desi able o ha e an ins i u ional p og am ha con empla es he c ea ion o echnological
in as uc u e, eache aining and he gene a ion o an en i onmen o us and explo a ion ha con empla es
bo h he con ibu ions and he isks o he use o AI in hese p ocesses.
5.3. Ins i u ional conside a ions a he Popula Uni e si y o Cesa
The in eg a ion o a i icial in elligence in elec onic enginee ing educa ion poses challenges ha anscend
eaching and lea ning p ocesses, and equi e he gaze o ins i u ional go e nance. A he Popula Uni e si y o
Cesa , his sugges s he need o e iew echnological policy, o s eng hen in as uc u e, o pay a en ion o he
aining and well-being o eache s, and o p omo e s uden accompanimen . While cu en AI ools a e a om
pe ec and can eplace eache s, hey cons i u e a powe ul ex ension o he human b ain ha has he po en ial o
ans o m no only he lea ning p ocess, bu he en i e way s uden s and eache s a e.
The es ablishmen o a go e nance model ha con empla es he adop ion, use and e olu ion o hese ools mus
be an ins i u ional p io i y. Technology policy should include he sa e and esponsible use o a i icial in elligence,
and in as uc u e decisions should ensu e access o he cloud and he necessa y p ocessing esou ces. Teache
aining and suppo ac ions mus add ess he changes ha echnology p oposes o he design, eaching, e alua ion
and u o ing p ocesses. The well-being o eache s, who a e usually o e bu dened and wi h li le ime o hei
pe sonal li es, mus also be p io i ized. Las bu no leas , he in eg a ion o a i icial in elligence ools in he
eaching-lea ning p ocess equi es a cul u al change ha places he s uden a he cen e o he lea ning p ocess.
6. Recommenda ions
P ac ical ac ions a e p oposed o he p o esso s o he elec onic enginee ing ca ee o he Popula Uni e si y o
Cesa o in eg a e a i icial in elligence in he eaching-lea ning p ocess. They es ablish a plan o pilo ing and
scaling up he in eg a ion o AI ools in ca ee eaching, a se o guidelines o he design o eaching-lea ning
ac i i ies wi h AI echnologies, and a se ies o indica o s aimed a e alua ing he quali y o he in eg a ion o AI
ools in he lea ning p ocess and e i ying hei eal impac on s uden lea ning.
A s agge ed in eg a ion plan and a se o guides a e p oposed ha ake up e idence on he use o AI ools in he
acquisi ion o undamen al concep s in he a ea o elec onic ci cui s and hei ele ance o he es o he ca ee .
The in eg a ion includes e e y hing om he selec ion and pilo ing o a speci ic ool o he in eg a ion o
gene a i e AI ools in he design o lea ning ac i i ies and in he e alua ion o lea ning. The ac i i ies a e
ca ego ized and s uc u ed acco ding o h ee conside a ions: he ype o ool used, whe he he use o he ool is
manda o y in he ac i i y, and he ype o in e ac ion be ween he s uden and he ool.
6.1. S a egies o in eg a ing AI in o elec onic enginee ing cou ses
To achie e sound pedagogical esul s, an implemen al and e idence-based app oach is sugges ed. In he i s
ins ance, i is ad isable o de elop an in eg a ion plan o speci y i s applicabili y, ex ension and scalabili y. This
includes: (i) he selec ion o ools based on es ablished h esholds and p ocesses; (ii) an essay limi ed o a g oup
o s uden s, wi h ca e ul obse a ion o he e ec s gene a ed; and (iii) he subsequen adop ion and scaling in
o he cou ses, wi h emphasis on he e alua ion o quali y indica o s, lea ne ou comes and added alue.
Subsequen ly, e idence-based class oom p ac ices can be es ablished, which include: eaching guides, ac i i ies
designed wi h he suppo o AI ools, and o ma i e assessmen s ha include ad ance eedback.
To conclude, quali y indica o s a e p oposed o he in eg a ion o AI ools in Elec onic Enginee ing cou ses.
Fi s , i is necessa y o conside he ype o ool and i s ac ual o po en ial ole in he s uden s' lea ning p ocess.
Secondly, i is sugges ed ha ub ics be d awn up o check he e ec i e applica ion o echnologies. Thi d, he
lea ning ou comes o s uden s who pe o m ac i i ies suppo ed by AI ools should be moni o ed. Finally, i is
ad isable o pe iodically e iew he implemen a ion based on he esul s ob ained.
6.2. E idence-based eaching p ac ices
To acili a e he in eg a ion o a i icial in elligence ools in elec onic enginee ing cou ses, e idence-based
eaching p ac ices a e p oposed. Guides should be de eloped o guide eache s in he eaching o key concep s, as
well as a p oposal o he design o ac i i ies ha con empla e hei in eg a ion in o lea ning en i onmen s. I is
also sugges ed o apply a o ma i e assessmen model ha allows moni o ing he p og ess o s uden s and a
eedback s a egy ha suppo s hei lea ning.
In eg a ing AI ools in o elec onic enginee ing cou ses ha use la ge language models p o ides s uden s wi h
signi ican ad an ages. Howe e , in o de o hese ools o eally con ibu e o hei lea ning p ocess, i is
impo an ha eaching and assessmen ac i i ies a e adjus ed. Teache s end o ha e ese a ions abou he
implemen a ion o hese ools, and aining and in as uc u e gaps a e pe cei ed in he ins i u ions. The e o e, a
se o e idence-based eaching p ac ices is p oposed o help eache s e ec i ely in eg a e hese ools in o hei
cou ses.
6.3. Quali y indica o s and assessmen o lea ning
The in eg a ion o A i icial In elligence ools in elec onics educa ion mus be accompanied by a se o indica o s
ha allow hei quali y and impac o be e alua ed. Such indica o s should ange om he selec ion o app op ia e
ools o he cou se o he assessmen o lea ning. This assessmen should include me ics and in o ma ion
associa ed wi h he ools, a ub ic o de e mine how much i suppo s s uden s in mee ing cou se objec i es, as
well as a ollow-up o s uden ou comes in ela ion o he use o AI ools and a o mal e iew o implemen a ion.
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A igo ous selec ion o he ools o be used in he cou se may in ol e he analysis o he ecommenda ions o he
main echnological and educa ional media. Addi ionally, and as is done wi h o he echnologies, i is possible o
c ea e a se o aspec s ha a ool mus mee o be used in he class oom. A he same ime, each cou se ha adop s
any o hese ools mus ha e a se o e idence ha allows de e mining whe he i s use in he lea ning en i onmen
enhances, limi s o does no ha e an e ec on s uden lea ning, hus con igu ing an impac analysis p ocess.
7. Limi a ions and u u e lines o esea ch
The esul s p esen ed ha e se e al limi a ions. Fi s , he popula ion ha se ed as he basis o he main e idence
is made up o a ela i ely small sample o s uden s, so hey may no be ep esen a i e o he en i e popula ion o
he Popula Uni e si y o Cesa (UPC). Howe e , he o e exploi a ion ha has been made o AI ools in educa ion,
he nega i e a i ude ha a g oup o enginee ing s uden s ha e exp essed owa ds hei use, and he eali y ha he
UPC aces when implemen ing changes in he use o echnology, suppo he in e es o he p oblem posed.
Secondly, he ac ha only a ew p o esso s o he Facul y o Elec onic Enginee ing ha e been consul ed implies
ha i is possible ha he pe cep ions o mos UPC p o esso s abou educa ional AI a e di e en .
The esea ch has add essed only a ew lines o in eg a ion o AI in he lea ning o elec onic enginee ing. Fu u e
esea ch ques ions should he e o e b oaden he explo a ion. Fo he design o ac i i ies wi h AI, o example, one
could in es iga e how o include he gene a ion o images o hei use as a sea ch assis an (simila o Google),
and analyze whe he hei inco po a ion in o class ac i i ies imp o es s uden s' pe o mance and use o ime, as
has been ound in o he con ex s. Ano he in e es ing line is he in eg a ion o AI in heo e ical lea ning models
and he use o AI in well-being p ocesses and human-machine in e ac ion.
8. CONCLUSIONS
The in eg a ion o a i icial in elligence in e-enginee ing educa ion equi es a change o men ali y in eaching and
assessmen , so ha AI is seen as an ally and no as a h ea . A he le el o educa ional psychology, suppo is
needed o he achie emen o signi ican lea ning, as well as o he well-being o s uden s and he esponsible
use o AI ools, in an en i onmen ha a o s human in e ac ion and no i s isola ion. F om he uni e si y, policies
mus be es ablished, esou ces mus be alloca ed, co po a ions mus be p o ided wi h e ec i e go e nance and
eache s mus be adequa ely ained. A co ec use o AI in he lea ning o elec onic enginee ing a ec s no only
he comp ehension, e en ion, speed o app op ia ion o concep s and he consump ion o esou ces, bu also
mo i a ion and he de elopmen o au onomy.
Resea ch has ound ha he use o a i icial in elligence ools can in luence he unde s anding and e en ion o
concep s and help accele a e lea ning, ha eache s' com o and con idence inc ease wi h he equency o using
AI applica ions in hei subjec s and ha , al hough in gene al hei a i ude is posi i e, The e a e ese a ions
abou human-machine in e ac ion and e hics, and mo e aining is equi ed o p ope use. Howe e , gaps and
challenges ha limi i s in eg a ion a e also de ec ed: in he ins i u ion's in as uc u e ha p e en s i s use due o
echnological ailu es and in he eaching o ganiza ion ha does no conside he use o a i icial in elligence in
i s design o in ins i u ional go e nance; in he eaching-lea ning app oach ha does no seek adap a ion o he
indi idual cha ac e is ics o each s uden o encou age in e ac ion; and in he cul u e ha sees AI mo e as a isk
han an oppo uni y.
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