298
© 2025 by he au ho s; licensee Asian Online Jou nal Publishing G oup
Jou nal o Educa ion and e-Lea ning Resea ch
Vol. 12, No. 2, 298-305, 2025
ISSN(E) 2410-9991 / ISSN(P) 2518-0169
DOI: 10.20448/jeel . 12i2.6893
© 2025 by he au ho s; licensee Asian Online Jou nal Publishing G oup
The impac o a i icial in elligence on make educa ion: Mo i a ion and echnology
accep ance in eache aining
Amaia Quin ana-O do ika1
Edo a Camino-Es u o2
U za Ga ay-Ruiz3
Ja ie Po illo-Be asaluce4
(
Co esponding Au ho )
1,3,4Depa men o Didac ics and School O ganiza ion, Uni e si y o he Basque Coun y (UPV-EHU), Leioa,
Spain.
1Email: amaia.qu[email p o ec ed]
3Email: u za.ga [email p o ec ed]
4Email: ja ie .po [email protected]
2Depa men o Educa ional Sciences, Uni e si y o he Basque Coun y (UPV-EHU), Leioa, Spain.
2Email: edo [email protected]
Abs ac
In ecen decades, he in eg a ion o eme ging echnologies, such as make educa ion and a i icial
in elligence, in o he educa ional ield has become a p ominen a ea o esea ch. The pu pose o his
a icle is o explo e whe he he in oduc ion o gene a i e a i icial in elligence in o he design
p ocess o make p ojec s by u u e eache s p oduces bene i s. Speci ically, a compa ison was made
be ween he pe cep ions o ainee eache s who expe imen ed wi h he use o gene a i e a i icial
in elligence and hose who did no use i du ing he de elopmen o eaching and lea ning designs.
A ques ionnai e was adminis e ed o a sample o 114 ainee eache s om he Uni e si y o he
Basque Coun y (UPV/EHU), who ecei ed aining in make educa ion and designed eaching and
lea ning plans based on his app oach. The esul s o he compa ison indica e a signi ican di e ence
be ween he wo g oups ac oss all analyzed dimensions, highligh ing ha he g oup wo king wi h
a i icial in elligence demons a ed g ea e mo i a ion, pa icula ly in e ms o a en ion and
accep ance o make educa ion. The indings sugges ha he design p ocess o u u e AI-based
make in e en ions should be explo ed mo e ho oughly in se e al dimensions ela ed o he
in eg a ion o eme ging echnologies.
Keywo ds: A i icial in elligence, cu iculum de elopmen , educa ional echnology, make educa ion, eache educa ion, eaching inno a ion.
Ci a ion | Quin ana-O do ika, A., Camino-Es u o, E., Ga ay-Ruiz,
U., & Po illo-Be asaluce, J. (2025). The impac o a i icial
in elligence on make educa ion: Mo i a ion and echnology
accep ance in eache aining. Jou nal o Educa ion and E-Lea ning
Resea ch, 12(2), 298–305. 10.20448/jeel . 12i2.6893
His o y:
Recei ed: 13 Ma ch 2025
Re ised: 23 June 2025
Accep ed: 30 June 2025
Published: 15 July 2025
Licensed: This wo k is licensed unde a C ea i e Commons
A ibu ion 4.0 License
Publishe : Asian Online Jou nal Publishing G oup
Funding: This s udy ecei ed unding om he Uni e si y o he Basque
Coun y (UPV/EHU), Spain (G an numbe IT1685-22) and he Eu opean
Union, Eu opean Inno a ion Council STEAMB ace (G an numbe
101132652).
Ins i u ional Re iew Boa d S a emen : The E hical Commi ee o he
Uni e si y o Basque (UPV/EHU), Spain has g an ed app o al o his s udy
on 21 No embe 2024 (Re . No. M10_2023_399MR1).
T anspa ency: The au ho s con i m ha he manusc ip is an hones ,
accu a e, and anspa en accoun o he s udy; ha no i al ea u es o he s udy
ha e been omi ed; and ha any disc epancies om he s udy as planned ha e
been explained. This s udy ollowed all e hical p ac ices du ing w i ing.
Compe ing In e es s: The au ho s decla e ha hey ha e no compe ing
in e es s.
Au ho s’ Con ibu ions: All au ho s con ibu ed equally o he
concep ualiza ion, and design o he s udy. All au ho s ha e ead and ag eed he
published e sion o he manusc ip .
Con en s
1. In oduc ion .................................................................................................................................................................................... 299
2. Me hod ............................................................................................................................................................................................. 300
3. Resul s .............................................................................................................................................................................................. 302
4. Discussion and Conclusions ......................................................................................................................................................... 303
5. Limi a ions and Fu u e Lines o Resea ch ................................................................................................................................ 304
Re e ences ................................................................................................................................................. E o ! Bookma k no de ined.
Jou nal o Educa ion and e-Lea ning Resea ch, 2025, 12(2): 298-305
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Con ibu ion o his pape o he li e a u e
The a icle examines whe he in eg a ing gene a i e a i icial in elligence in o make p ojec
design can enhance p e-se ice eache s' mo i a ion and accep ance o echnology in make
educa ion. I compa es pe cep ions be ween wo g oups hose wi hou AI expe ience and hose
wi h AI expe ience highligh ing key di e ences in hei a i udes and pe cep ions.
1. In oduc ion
Resea ch in o eme ging echnologies such as make educa ion in o mal educa ion appea s o be inc easing in
bo h p e-se ice and in-se ice eaching. A signi ican numbe o s udies ha e ocused on aining u u e eache s o
de elop compe encies in he design and planning p ocess in acco dance wi h make p inciples (Douglass & Ve ma,
2022; He edia & Fishe , 2022; Shi ely, Hi chens, & Hi chens, 2021). Recen s udies indica e ha he inco po a ion
o make educa ion in o o mal lea ning en i onmen s os e s and p omo es skills ela ed o science, echnology,
enginee ing, and ma hema ics (STEM) (Blackley, She ield, Mayna d, Koul, & Walke , 2017). In his ield, he e is
also an inc easing emphasis on he in eg a ion o a i icial in elligence (AI) because i holds signi ican po en ial o
expand he ange o s udy oppo uni ies and p ac ical expe iences a ailable o s uden s (Wu, Lee, Wang, Lin, &
Huang, 2023). The in eg a ion o AI, pa icula ly gene a i e a i icial in elligence (GenAI), wi hin he educa ional
sec o has gi en ise o a mul i ude o opinions and pe spec i es, p ima ily due o i s po en ial o ans o m he
educa ional landscape (Tlili e al., 2023). A conside able body o esea ch has been published demons a ing he
e ec i eness o using GenAI as a pedagogical ool, highligh ing how i can imp o e eaching and lea ning ou comes
(Ali, Shamsan, Hezam, & Mohammed, 2023; Eage & B un on, 2023). Bo h eme ging echnologies ha e been shown
o ha e a signi ican impac on s uden s' eelings and a i udes owa ds lea ning, esul ing in inc eased en husiasm
o i (Ou & Chen, 2024). The e a e e en s udies ha ecommend in oducing AI educa ion cou ses in o eache
aining p og ams (Sun, Tian, Sun, Fan, & Yang, 2024). Howe e , in he ield o eache aining, he e a e s ill
aspec s o be explo ed, such as he pe cep ions o p e-se ice eache s.
This a icle ad ances esea ch on how eme ging echnologies can be in eg a ed in o educa ion. I s p ima y aim
is o analyze he pe cep ions o ea ly childhood and p ima y educa ion ainee eache s ega ding make educa ion,
pa icula ly conce ning i s impac on mo i a ion and he accep ance o echnology when o he eme ging
echnologies, such as a i icial in elligence, a e in oduced. The s udy has wo main objec i es: i s , o compa e he
pe spec i es o wo g oups o ainee eache s one wo king solely wi h make educa ion and he o he in eg a ing
AI; second, o examine he ole o GenAI ools in lesson planning, analyzing how he g oup using GenAI ools
di e ed om he g oup designing hei eaching and lea ning plans wi hou AI suppo .
1.1. Make Educa ion in he Educa ional Field
The make mo emen is a phenomenon ha b ings oge he di e se agen s o o m communi ies ocused on
inke ing, c ea ing, and building unique a i ac s (Pepple & Bende , 2013). Roo ed in he do-i -you sel (DIY)
mindse , i s pedagogical ounda ions ace back o John Dewey’s emphasis on ac i e pa icipa ion in lea ning
(Ma inez & S age , 2013) and Seymou Pape ’s cons uc ionism, which highligh s he po en ial o c ea ion o e
me e knowledge ansmission (Hal e son & She idan, 2014). The cons uc ionis app oach, which was de i ed om
Piage ’s cons uc i ism, a gues ha lea ning occu s h ough he c ea ion o angible a i ac s (Pape & Ha el, 1991).
The cons uc ionism inco po a es a numbe o pedagogical app oaches such as p ojec -based lea ning, inqui y-based
lea ning, and collabo a i e lea ning (Schlegel e al., 2019). Make spaces, as physical o i ual collabo a i e lea ning
en i onmen s, se e as global and echnological lea ning spaces equipped wi h ools and ma e ials ha suppo
c ea i i y, p oblem-sol ing, and knowledge sha ing (Gan e , F ed ich, Bouncken, & K aus, 2022). Those lea ning
en i onmen s could be de ined as c ea i e and adap able lea ning en i onmen s (Soom o, Casakin, Nanjappan, &
Geo gie , 2023).
A subs an ial and p og essi e inc ease has been obse ed in he numbe o published manusc ip s ela ed o
make educa ion in ecen yea s and in ela ion o he educa ional ield. This phenomenon e lec s i s g owing
popula i y ac oss all educa ional le els, om p ima y schools o highe educa ion (Soom o e al., 2023). Howe e ,
challenges exis , pa icula ly o elemen a y eache s, who may s uggle wi h he con en , deli e y me hods, and
physical spaces o make spaces. This highligh s he need o specialized eache aining o suppo hese inno a i e
lea ning en i onmen s (Douglass & Ve ma, 2022). A b oad ange o s udy pa icipan s has been explo ed, including
in-se ice and p e-se ice eache s, as well as o he agen s in ol ed in wo kshops and aining cou ses (He edia &
Tan, 2021; Koole, Ande son, & Wilson, 2020). P og ammes a ge ing u u e educa o s aim o add ess he challenges
o inco po a ing make ac i i ies in o con en ional class ooms by p o iding collabo a i e suppo and hands-on
expe iences, he eby os e ing bo h heo e ical unde s anding and p ac ical expe ise (Douglass & Ve ma, 2022;
He edia & Fishe , 2022). O he p og ams ocus on e ec i e p o essional de elopmen h ough imme si e i ual
en i onmen s ha os e collabo a ion and communi y (Lock e al., 2020; Mo ison & Hughes, 2023) o h ough on-
si e aining, wi h a iew o be e in eg a ing make p ac ices in o cu icula and add essing challenges such as
con en d ead (He edia & Tan, 2021).
1.2. A i icial In elligence in he Educa ional Field
The use o a i icial in elligence in e e yday ools and i s consequen ede ini ion o se ices in highe educa ion
ha e been well documen ed (Popenici & Ke , 2017). The in e es in he use o AI in educa ional se ings has
expe ienced no able g ow h in ecen yea s (Celik, Dinda , Muukkonen, & Jä elä, 2022; Popenici & Ke , 2017).
Resea ch s udies ha e p edic ed a ise in he numbe o s udies ha will be ca ied ou , wi h he applica ion o AI o
educa ional en i onmen s. The e will also be an inc ease in he numbe o s udies discussing possible app oaches o
p omo ing and eaching AI skills a all educa ional le els and in all ields (Hwang, Xie, Wah, & Gaše ić, 2020).
In he con ex o eache aining, posi i e pe cep ions ha e been iden i ied ega ding he use o GenAI in
educa ional en i onmen s, indica ing oppo uni ies o i s in eg a ion in o eaching p ac ices (Lozano & Blanco
Fon ao, 2023). I possesses he po en ial o educe he wo kload o eache s, acili a ing he e icien c ea ion o cou se
ma e ials, al hough he eaching uni s may be somewha gene ic and equi e u he e inemen (Coope , 2023).
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Mo eo e , esea ch has demons a ed he bene i s o GenAI in helping s uden s iden i y sou ces o inspi a ion
o composi ion and con en e alua ion asks (Fe gus, Bo ha, & Os o a , 2023). Mo eo e , i has been obse ed ha
i has he capaci y o acili a e he gene a ion o ideas and o p o ide explana ions ha a e accep able in a a ie y o
knowledge domains. This, in u n, has he e ec o enhancing he accessibili y, e iciency, and e icacy o academic
counseling (Akiba & F aboni, 2023). Wi hin he domain o uni e si y s uden s, his ool is no pe cei ed as a h ea
o he educa ion sys em, p o ided ha he da a gene a ed is e i iable (Lozano & Blanco Fon ao, 2023). Howe e ,
GenAI also exhibi s ce ain weaknesses ha should be aken in o accoun . I has been a gued ha i may no be
sui able o no ices, as i s esponses may lack logical cohe ence and equi e p io knowledge. In some cases, he
in o ma ion p o ided may no be en i ely accu a e, and in o he s, he esponses may be con adic o y, which may
lead o ques ions ega ding he eliabili y o he in o ma ion p o ided (S ojano , 2023). The didac ic designs i c ea es
can be gene ic and equi e adap a ion by eache s (Coope , 2023). The e a e s udies ha a gue ha he u iliza ion o
AI may ha e he po en ial o diminish c i ical hinking skills and ha i s e icacy can a y ac oss di e en subjec s
(Mohamed, 2024). I is acknowledged by many eache s ha he implemen a ion o AI in he educa ional sec o may
be use ul o educing he ime spen on adminis a i e asks and acili a ing he c ea ion o engaging and
pe sonalized lea ning expe iences. Howe e , he e a e conce ns ega ding he e o equi ed o ain eache s, he
po en ial consequences o employmen due o job displacemen , he impac on c ea i i y and c i ical hinking skills,
as well as he dependency on AI's capaci y o unc ion wi hou e o (Alwaqdani, 2024). A ecen s udy has indica ed
ha eliance on AI ools alone may no be su icien o enhance mo i a ion when con on ed wi h challenging
p og amming asks (Yilmaz & Yilmaz, 2023).
Consequen ly, he e is a necessi y o p o ide aining and educa ion o he educa ion communi y, comp ising
eache s and s uden s, in o de o ensu e he esponsible and e hical use o gene a i e a i icial in elligence in
educa ional p ac ice (Zhu, Sun, Luo, Li, & Wang, 2023).
1.3. A i icial In elligence and Make Educa ion
In ecen yea s, he e has been an inc ease in he popula i y o educa ion in a i icial in elligence (Ng e al., 2023).
Ini ia i es a e being unde aken in he educa ion sec o o inc ease s uden li e acy in AI and o cul i a e
collabo a i e p oblem-sol ing skills. Ins i u ions a e in eg a ing he ield o AI in o STEM educa ion and compu e
science cu icula. The pu pose o his in eg a ion is o cul i a e AI li e acy among s uden s h ough an
in e disciplina y app oach. Addi ionally, he in eg a ion o AI in o educa ional p ac ice has been shown o inspi e
s uden s o sol e p oblems in STEAM subjec s (Sin o e al., 2016). Au ho s place an emphasis on he ac ha AI
can be used o communica e and collabo a e wi h colleagues (Ca pio Cañada, Ma eo Sanguino, Me elo Gue ós, &
Ri as San os, 2015).
Few s udies ha e explo ed make educa ion as a s a egy o AI li e acy. A ecen s udy esea ched i s in eg a ion
a di e en cogni i e le els, highligh ing imp o emen s in mo i a ion, p o essional in e es , con idence, and
collabo a ion (Ng, Su, & Chu, 2024). I has also been de e mined ha make pedagogy has he po en ial o be an
e ec i e app oach in p o iding s uden s wi h oppo uni ies o explo e play ul ools and ma e ials o meaning ul
p ac ical c ea ion. The objec i e o his app oach is o imp o e AI li e acy h ough he implemen a ion o a 'lea ning
by doing' s a egy (Hsu, Abelson, Lao, Tseng, & Lin, 2021).
In os e ing g ea e con idence in AI and in ela ion o s udies ha ha e analyzed he deg ee o accep ance o
his eme ging echnology, i is no ewo hy ha eache s' accep ance o educa ional AI ools is in luenced by pe cei ed
use ulness, pe cei ed ease o use, and pe cei ed us (Choi, Jang, & Kim, 2023). I should also be emphasized ha i
may be necessa y o educa o s o be in o med abou he AI decision-making p ocess and i s po en ial o enhance and
complemen hei skills a he han eplace hem (Naza e sky, A iely, Cuku o a, & Alexand on, 2022). Ano he piece
o esea ch ha analyzed he accep ance o AI concluded ha , in a sample o 200 eache s, hey had a posi i e a i ude
owa ds AI, inding a signi ican posi i e co ela ion be ween pe cei ed use ulness, pe cei ed ease o use, and a
posi i e a i ude owa ds AI (He zallah & Makaldy, 2025).
In ligh o he g owing in e es in eme ging echnologies such as make educa ion and a i icial in elligence, i
is e iden ha p e ious s udies ha e ye o analyze he pe cep ions o ainee eache s owa ds make educa ion in
wo di e en con ex s: one wi hou AI and he o he wi h AI. The p esen s udy he e o e poses he ollowing
esea ch ques ions:
1.4. Resea ch Ques ions
The ollowing esea ch ques ions a e p oposed.
RQ1. Do he wo g oups, he Non-AI and AI p e-se ice eache s, exhibi di e gen pe cep ions o mo i a ion
owa ds make educa ion upon comple ing he p ojec design?
RQ2. Do he wo g oups, Non-AI and AI, demons a e di e ences in echnology accep ance owa ds make
educa ion upon comple ion o he p ojec design? Which subca ego ies exhibi he mos signi ican di e ences?
2. Me hod
2.1. Pa icipan s and Design o he In e en ion
The p esen in es iga ion was conduc ed wi h a sample o 114 ainee eache s en olled in he In o ma ion and
Communica ion Technologies (ICT) subjec a he Facul y o Educa ion o he Uni e si y o he Basque Coun y.
The pa icipan s we e en olled in bachelo 's deg ee p og ams in Ea ly Childhood Educa ion and P ima y Educa ion
du ing he 2022/23-24 academic yea .
The 16-week cou se included a h ee-week segmen ocused on make educa ion, inco po a ing bo h heo e ical
and p ac ical app oaches. Theo e ical classes p o ided an o e iew o he s ages and p inciples o he make educa ion
me hodology, while p ac ical sessions we e conduc ed in a make labo a o y, a collabo a i e lea ning en i onmen
loca ed a he Facul y o Educa ion. Pa icipan s we e asked wi h designing eaching and lea ning plans ha
employed he make pedagogical app oach h ough p ojec -based lea ning, in eg a ing heo e ical knowledge wi h
p ac ical expe ience. Pa icipan s had he oppo uni y o expe imen wi h all he ools and ma e ials a ailable in he
make labo a o y. The cu iculum o ini ial eache aining p og ams emphasizes he design and execu ion o lesson
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plans, which a e undamen al componen s o he p og am. These designs we e based on he TPACK model (Mish a
& Koehle , 2006), an educa ional amewo k ha acili a es unde s anding o he ela ionship and in e sec ion
be ween echnology, pedagogy, and con en . As pa o echnological knowledge, pa icipan s we e equi ed o
include app op ia e echnological ools o applica ions, which in his case we e ools a ailable in he make
labo a o y. Pedagogical conside a ions in ol ed he design, implemen a ion, and e alua ion o eaching and lea ning
plans based on he make pedagogical app oach.
The p esen s udy employed a compa ison be ween wo dis inc g oups: he Non-AI g oup (n=59) and he AI
g oup (n=55). The Non-AI g oup o pa icipan s de eloped hei eaching and lea ning plans wi hou he use o
GenAI ools. In con as , he AI g oup in eg a ed GenAI in o he second pa o he planning p ocess o enhance
and op imize hei p e-designed eaching and lea ning plans. I is no ewo hy ha he AI g oup used GenAI a e
comple ing hei p elimina y d a s, a s a egy ha enabled he p ese a ion o hei o iginal ideas and acili a ed
c i ical e lec ion (Figu e 1).
The p omp s used wi h GenAI we e p ima ily designed o assis ainee eache s in gene a ing ideas and
op imizing p e-designed eaching and lea ning plans. The ollowing sec ion p esen s a isual ep esen a ion o he
dis inc ion be ween he wo g oups analyzed (Figu e 1).
Figu e 1. Rep esen a ion o he dis inc ion be ween he wo g oups analyzed.
2.2. Da a Collec ion and Ins umen
The da a collec ion p ocess was conduc ed a e he aining p og am, which was g ounded in he make
pedagogical app oach. This phase o da a collec ion occu ed a e he p esen a ion o pa icipan s' p oposals o hei
espec i e classma es. I was emphasized o he pa icipan s ha comple ing he ques ionnai e was olun a y and
ha hei inal g ades would no be ad e sely a ec ed by hei esponses. The ques ionnai e had wo sec ions. The
i s sec ion con ained ques ions based on he Reduced Ins uc ional Ma e ials Mo i a ion Su ey (RIMMS), while
he second sec ion included i ems abou he Technology Accep ance Model (TAM). The pe cep ions o p e-se ice
eache s we e measu ed using a Like scale om 1 o 6.
The educed e sion o he Ins uc ional Ma e ials Mo i a ion Su ey (RIMMS) was used (Loo bach, Pe e s,
Ka eman, & S eehoude , 2015), d awing on Kelle ’s ARCS model o mo i a ional design, which e alua es mo i a ion
h ough ou subscales: a en ion, ele ance, con idence, and sa is ac ion. Each subscale can be sco ed independen ly
(Kelle , 2010). The a en ion subscale includes i ems assessing he quali y o he sessions (A3), he sui abili y o he
assignmen a angemen s (A6), and he a ie y o assignmen s and illus a ions (A10). The ele ance subscale
e alua es whe he he con en and ma e ials a e linked o wha he lea ne has al eady lea ned (R1), as well as
whe he he con en and ma e ials a e wo hwhile, pu pose ul, and use ul (R6 and R9). The con idence subscale
measu es how con iden lea ne s eel abou unde s anding he con en (C5), succeeding in assessmen s (C7), and how
well he asks a e o ganized (C9). Finally, he sa is ac ion subscale assesses whe he lea ne s would like o explo e
u he (S2), hei enjoymen le el o pa icipa ing in he p ojec (S3), and he pleasu e de i ed om engaging wi h
well-designed asks (S6).
The Technology Accep ance Model (TAM) (Da is, 1989) was adap ed o assess he accep ance o he make
pedagogical app oach. In his esea ch, his model is based on i e dimensions: pe cei ed use ulness (PU), pe cei ed
ease o use (PEU), pe cei ed enjoymen (PEN), a i ude owa ds use (ACU), and in en ion o use (IU). In his model,
PU and PEU a e c i ical, in luencing use s’ o e all a i udes, enjoymen , and in en ion o adop he echnology. The
pe cei ed use ulness subscale includes ou i ems: lea ning imp o emen (PU1), acili a ion o comp ehension o
speci ic concep s (PU2), o e all use ulness (PU3), and enhancemen o lea ning (PU4). The pe cei ed ease o use
subscale comp ises h ee i ems: ease o use (PEU1), he absence o di icul ies in lea ning and handling he ool
(PEU2), and cla i y in i s use (PEU3). The pe cei ed enjoymen subscale consis s o h ee i ems: enjoymen o i s use
(PEN1), pe sonal enjoymen (PEN2), and lea ning by doing (PEN3). The a i ude owa ds use subscale, in u n,
includes wo i ems: he i s i em is o make lea ning mo e in e es ing (ACU1), and he second is ha i is a good
idea o use in class (ACU2). Finally, he in en ion o use subscale con ains wo i ems: in en ion o use in he u u e
(IU1) and in en ion o use i o lea n new opics (IU2).
2.3. Analysis o he Ins umen
The s udy u ilized bo h desc ip i e and di e en ial s a is ics, in addi ion o co ela ional analysis, o asce ain
he in e nal cha ac e is ics o each g oup. A e compiling he da a, he di e en sub-dimensions o each ins umen
we e calcula ed. Fo bo h he RIMMS and he TAM, he means o he i ems co esponding o each sub-dimension
and he o al alues o he ins umen s we e calcula ed, esul ing in he gene a ion o a e age sco es o indi iduals
in each esea ch g oup (Non-AI g oup – AI g oup). Subsequen ly, he assump ions o no mali y and homoscedas ici y
we e e i ied. I was obse ed ha all sub-dimensions p esen ed non-no mal dis ibu ions in bo h g oups, excep in
he RIMMS o he AI g oup (Kolmogo o -Smi no es , p=0.089). Howe e , no signi ican di e ences we e obse ed
in he a iabili y o any sub-dimension, wi h p- alues g ea e han 0.05 in he Le ene es , indica ing equali y o
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a iances be ween he wo g oups. The ins umen s used in his s udy ha e been demons a ed o possess inhe en
eliabili y and alidi y, as e idenced by C onbach's alpha alues anging om 0.797 o 0.967. This indica es a high
deg ee o in e nal consis ency among he sub-dimensions. The indi idual mean sco es we e e-scaled, esul ing in
he ca ego iza ion o each sco e in o one o ou dis inc ca ego ies based on nume ical anges. The Low (o Ve y
Low) ca ego y includes alues om 1 o 3, indica ing a low o e y low le el. The Medium ca ego y co e s he ange
3 o 4, ep esen ing a mode a e le el. The High ca ego y includes alues om 4 o 5, signi ying a high le el. Finally,
he Excellen ca ego y alls wi hin 5 o 6, indica ing an excellen le el.
This ypology has been used o desc ibe he cha ac e is ics o each g oup, aiming o iden i y pe cen age
di e ences ha can p o ide an ini ial o e iew o he esul s ob ained h ough he me hodology applied o each o
he wo s uden g oups. Addi ionally, he a ying p opo ions o hei ypological cha ac e is ics ha e been analyzed.
Subsequen ly, a hypo hesis es was conduc ed using he T- es o de e mine whe he he e a e s a is ically
signi ican di e ences in he means be ween he wo s udy g oups. The null hypo hesis (Ho) s a es ha he e a e no
signi ican di e ences be ween he wo g oups o s uden s in any o he sub-dimensions o in he o e all
measu emen s o he RIMMS and TAM ins umen s.
3. Resul s
3.1. Classi ica ion o Pa icipan s
The eache aining pa icipan s ha e been classi ied, in a p elimina y manne , in o di e en ca ego ies based
on hei sco es. Each o hese ca ego ies ob ains pe cen age sco es (Table 1), bo h in he o e all calcula ion o he
ins umen on he mo i a ion scale (RIMMS) and in he accep ance o echnology (TAM).
The desc ip ion o hese pe cen ages has been ca ied ou on he con as g oups, bo h on e ical columns (AI
g oup s. non-AI g oup) and ho izon al ows (ca ego ies o scale le els). A a gene al le el, in e ms o RIMMS, he
sec o o he sample (n=114) ha shows mo i a ion le els o bo h High (42.11%) and Excellen (24.56%) co esponds
o 66.67% o he o al sample, while he alues o Medium (24.56%) and Low (8.77%) accoun o 33.33%. In e ms
o he o e all pe cen age dis ibu ion o TAM, he sco es in he High (44.74%) and Excellen (28.07%) ca ego ies
ep esen 72.81% o he sample, while he p opo ions o Medium (16.67%) and Low (10.53%) a ings s and a 27.2%.
This indica es a posi i e assessmen o bo h ins umen s ac oss he sample, wi h a majo i y o pe cen ages a ing
e y posi i ely ega ding mo i a ion and accep ance o make educa ion.
Table 1. Pe cen ages dis ibu ed by ca ego y o he RIMMS and TAM ins umen s.
AI g oup (n=55)
Non-AI g oup (n=59)
To al (n=144)
n
Row %
Column %
n
Row %
Column %
n
Row %
Column %
RIMMS
Low
2
20.00
3.64
8
80.00
13.56
10
100.00
8.77
Medium
12
42.86
21.82
16
57.14
27.12
28
100.00
24.56
High
21
43.75
38.18
27
56.25
45.76
48
100.00
42.11
Excellen
20
71.43
36.36
8
28.57
13.56
28
100.00
24.56
TAM
Low
3
25.00
5.45
9
75.00
15.25
12
100.00
10.53
Medium
9
47.37
16.36
10
52.63
16.95
19
100.00
16.67
High
19
37.25
34.55
32
62.75
54.24
51
100.00
44.74
Excellen
24
75.00
43.64
8
25.00
13.56
32
100.00
28.07
Howe e , hese gene al posi i e p opo ions di e o change when he independen a iable (G oup) is
in oduced. An analysis o he le els o mo i a ion (RIMMS) shows ha , in e ms o he sums o he pe cen ages o
he High and Excellen le els, he e is a highe pe cen age concen a ion in he AI g oup (74.54%) han in he non-
AI g oup (66.67%). Wi hin he AI g oup, he e is a endency o he pe cen ages o inc ease as he le el o mo i a ion
ises, wi h a e y low p opo ion a he Low le el. Meanwhile, in he non-AI g oup, he maximum occu s a he High
le el (45.76%) bu does no main ain he same in ensi y a he Excellen le el, d opping mo e d as ically o 13.56%.
Rega ding he ho izon al dis ibu ion o mo i a ion le els, wi hin he Low le el, 80% belong o he non-AI g oup,
while 20% belong o he AI g oup. Fo he medium le el, 57.14% o he sample in ha ca ego y belong o he non-
AI g oup, while 42.86% a e in he AI g oup. A he High le el, he p opo ions a e 56.25% o he non-AI g oup and
43.75% o he AI g oup. Conce ning he Excellen le el, he e is a e e sal o he pa e n obse ed a he Low le el,
as 28.57% belong o he non-AI g oup, while 71.43% a e in he AI g oup.
The accep ance o echnology (TAM) exhibi s simila cha ac e is ics in e ms o he pe cen age dis ibu ion
ac oss g oups and ca ego ies. In e ical eading o columns o pe cen ages, he AI g oup shows a consis en upwa d
end, anging om he Low le el (5.45%), Medium (16.36%), High (34.55%), o Excellen (43.64%). Con e sely, he
non-AI g oup eaches i s highes pe cen age a he High le el (54.24%) and declines a he Excellen le el (13.56%).
No ably, bo h g oups ha e signi ican pe cen ages be ween he High and Excellen le els, wi h he AI g oup
achie ing a highe pe cen age (78.19%) han he non-AI g oup (67.8%). In he ho izon al pe cen ages ac oss ows,
he Medium and High le els o TAM di e be ween he AI g oup (47.37% and 37.25%) and he non-AI g oup (52.63%
and 62.75%), wi h he non-AI g oup holding highe p opo ions. Howe e , an in e sion occu s a he Low and
Excellen le els: he AI g oup has lowe pe cen ages a he Low le el (25%) compa ed o he non-AI g oup (75%),
while a he Excellen le el, he AI g oup accoun s o 75% o he sample, whe eas he non-AI g oup accoun s o
only 25%.
3.2. S a is ical Analysis o he Ins umen s
Rega ding he measu emen s collec ed o each o he ins umen s a a global le el and in hei sub-dimensions,
a se ies o a e ages can be obse ed wi h a endency owa ds he high ca ego y, especially among he g oup ha has
used AI when designing he eaching and lea ning plans (Table 2). In he case o he AI g oup, he RIMMS a e ages
ange om 4.39 poin s (Sa is ac ion) o 4.65 (Rele ance), wi h he o e all a e age o he ins umen (4.51) indica ing
a high a ing. As o he a e ages collec ed om he TAM ins umen , highe sco es a e obse ed han in he RIMMS
ins umen , wi h a ings anging om a minimum a e age (4.40) in he case o PEU o a e ages app oaching
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excellen , as in he case o he ACU sub-dimension (4.71). Likewise, he o e all sco e o accep ance o he TAM
model (4.60) e lec s a high a ing o his ins umen in he AI g oup.
Table 2. S a is ics o he g oups by subdimension and con as es .
(Sub) dimension
G oup
- es o equali y o means
d
Non-AI (n=59)
Mean (SD)
AI (n=55)
Mean (SD)
- es (
p
)
Mean di e ence
A en ion
3.92 (0.88)
4.50 (0.76)
<0.001***
0.58
0.68
Rele ance
4.10 (0.98)
4.65 (0.89)
0.002**
0.55
0.58
Con idence
4.05 (0.92)
4.50 (0.89)
0.008**
0.46
0.50
Sa is ac ion
3.87 (1.08)
4.39 (1.03)
0.010**
0.52
0.49
RIMMS
3.98 (0.90)
4.51 (0.81)
0.001***
0.53
0.62
PU
4.06 (0.96)
4.69 (0.89)
<0.001***
0.63
0.68
PEU
3.88 (0.86)
4.40 (0.84)
0.001***
0.52
0.61
PEN
4.17 (1.09)
4.61 (1.05)
0.029*
0.44
0.41
ACU
4.21 (1.16)
4.71 (0.98)
0.015*
0.50
0.46
IU
4.08 (1.09)
4.62 (1.11)
0.011*
0.53
0.49
TAM
4.07 (0.92)
4.60 (0.85)
0.002**
0.53
0.60
No e:
Equali y o a iances was assumed o all compa isons (p > 0.05 in Le ene's es ). *p<0.05; **p<0.01; ***p<0.001.
In con as , he sco es o he non-AI g oup ob ained lowe a ings, posi ioning hem in he medium ypology.
Rega ding he RIMMS ins umen , a e age sco es we e ob ained in some o i s sub-dimensions, such as A en ion
(3.92) o Sa is ac ion (3.87). Howe e , ce ain sub-dimensions achie ed high sco es, such as PEN (4.17) o ACU
(4.21).
In gene al, ega ding he RIMMS, he Non-AI g oup achie ed an o e all sa is ac ion sco e o 3.98, which is a
he uppe limi o he medium a ing ca ego y. This indica es a speci ic di e ence o -0.53 compa ed o he AI g oup,
which, as p e iously examined, achie ed a high a ing be ween 4 and 5 poin s (speci ically, 4.51). Addi ionally, he
Non-AI g oup, in i s e alua ion o he TAM ins umen , p o ided a e age sco es anging om a medium a ing o
3.88 in he PEU subca ego y o he highes sco e o 4.21 in he ACU subca ego y. O e all, he TAM sco e o his
g oup is high a 4.07, bu i is 0.53 poin s lowe han he o e all sco e o he AI g oup.
The compa ison o he means ob ained in bo h g oups, h ough he T- es , indica es signi ican di e ences
(p<0.05) be ween he means o bo h g oups. A common pa e n is cha ac e ized by he exis ence o signi ican
di e ences be ween he wo g oups in all he sub-dimensions, as well as in he o e all e alua ions o bo h
ins umen s. In he case o he RIMMS, he e a e highly signi ican di e ences (p<.01) be ween he mean alues o
he non-AI g oup and he AI g oup in all i s sub-dimensions and in he o e all calcula ion o he RIMMS. In he case
o TAM, hese di e ences a e also highly signi ican ; howe e , in he cases o PEN, ACU, o IU, hey do no show
such clea di e ences be ween he wo g oups analyzed. None heless, in he o e all case o TAM, he di e ences in
means be ween hem a e highly signi ican (p= 0.002). The inclusion o A i icial In elligence in he design phase o
eaching and lea ning plans has had an a e age impac , wi h e ec size alues anging om 0.41 o 0.68, wi h he
highes alues obse ed in A en ion (d=0.68) and PU (d=0.68). I has also been obse ed ha he e is a medium
e ec in he accep ance o he echnology (d=0.60), as well as a medium-sized e ec (d=0.62) in mo i a ion.
Thus, he signi ican di e ences be ween he wo g oups ha e been con i med in all he sub-dimensions o he
wo measu emen ins umen s, he eby ejec ing he null o ini ial hypo hesis. Signi ican di e ences we e obse ed
be ween he wo g oups analyzed. In p inciple, i can be s a ed ha hese di e ences may be a ibu able o he use
o AI. I so, he hypo hesis es con i ms ha he a e age sco es on bo h ins umen s a e signi ican ly highe in he
AI g oup han in he non-AI g oup.
4. Discussion and Conclusions
The as majo i y o he li e a u e o da e has analyzed make educa ion om ei he a heo e ical o p ac ical
pe spec i e. Howe e , he e is s ill a lack o s udies examining p e-se ice eache s' pe cep ions o make educa ion
when inco po a ing o he eme ging echnologies, such as A i icial In elligence. In ligh o his limi a ion, he
p esen s udy has explo ed he accep ance o make echnology and mo i a ion owa ds i in wo cases: wi h and
wi hou he use o AI.
In ela ion o he ini ial esea ch ques ion, he p ima y indings o he p esen s udy indica e ha p e-se ice
eache s who u ilized GenAI demons a ed highe le els o mo i a ion owa ds make educa ion ac oss all
subdimensions. Speci ically, he AI g oup exhibi ed signi ican ly g ea e a en ion, ele ance, con idence, and
sa is ac ion compa ed o hei non-AI coun e pa s. These esul s sugges ha AI suppo may ha e he capaci y o
enhance a ious mo i a ional ac o s, aligning wi h p e ious esea ch which ound ha AI ools posi i ely in luence
mo i a ion and may lead o imp o ed lea ning ou comes in educa ional se ings (Mohamed, Shaaban, Bak y, Guillén-
Gámez, & S zelecki, 2024).
The second esea ch ques ion a ge ed he di e ences in accep ance o echnology, and i can be concluded ha
he esul s we e simila o he p e ious pa ag aph, wi h he g oup o eache s who had wo ked wi h gene a i e AI
demons a ing a highe le el o accep ance in all he sub-dimensions ha we e analyzed. This heigh ened le el o
accep ance aligns wi h he indings o o he s udies ha ha e examined he accep ance o echnology, pa icula ly
a i icial in elligence, among p e-se ice eache s (Zhang, Schießl, Plößl, Ho mann, & Gläse -Zikuda, 2023). The
pe cei ed use ulness (PU) sub-dimension e ealed he mos signi ican di e ences, wi h he dimensions o in en ion
o use (IU) and pe cei ed ease o use (PEU) anking second and hi d, espec i ely. This obse ed le el o accep ance
may be a ibu ed o s udies ha ha e p e iously analyzed he accep ance o cha bo s by s uden s (Ragheb, Tan awi,
Fa ouk, & Ha a a, 2022) and hei impac on s uden success (Chen, Jensen, Albe , Gup a, & Lee, 2023). Howe e ,
i has also been analyzed ha when planning, he designs o scien i ic uni s c ea ed by a i icial in elligence should
be c i ically e alua ed and adap ed o hei pa icula eaching con ex s (Coope , 2023). The high in en ion o use i
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ha p e-se ice eache s ha e demons a ed in his s udy is consis en wi h he esea ch conduc ed by Lozano and
Blanco Fon ao (2023), which ound posi i e pe cep ions o p e-se ice eache s when using AI.
5. Limi a ions and Fu u e Lines o Resea ch
The p esen s udy is subjec o se e al limi a ions, he mos no able o which is he sample size, which is limi ed
o a pa icula deg ee, ha o eache aining. Fu he mo e, he con ex in which his esea ch has been ca ied ou
is limi ed. I is ecommended ha u u e esea ch eplica e his s udy in he in-se ice se ing o expand and con as
he esul s. Ano he limi a ion is ha only a pos - es was analyzed; including a p e- es migh ha e p o ided
addi ional aluable insigh s in o he esul s.
Mo eo e , he p esen s udy is es ic ed in scope o he desc ip ion o a compa a i e s udy be ween wo g oups,
wi hou any expe imen al examina ion o he unde lying causes. I is ecommended ha u u e esea ch in es iga e
he ac o s ha lead o inc eased mo i a ion o make educa ion and highe echnology accep ance when AI is
in eg a ed, and such ques ions could be e ec i ely esea ched using quali a i e esea ch me hods.
Despi e he limi a ions iden i ied in his s udy, i is expec ed o p o ide aluable insigh s in o how eache s
pe cei e he in eg a ion o AI in highe educa ion, he eby con ibu ing o a mo e comp ehensi e unde s anding o
hese eme ging echnologies.
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