Chap e
In eg a ing Ex ended Reali y and
A i icial In elligence in Educa ion:
T aining, Challenges, and
Re lec ions om P e- and
In-Se ice Teache s
S a os Pi sikalis and Ilona-Ele e yja Lasica
Abs ac
This pape p esen s he design, implemen a ion, and e alua ion o
a p o essional de elopmen p og amme conduc ed in G eece (Rhodes Island)
du ing 2024–2025, ocusing on he in eg a ion o ex ended eali y (XR) and
a i icial in elligence (AI) in educa ion. A o al o 101 eache s (53 p e-se ice, 48
in-se ice) pa icipa ed in i e aining sessions ha combined heo e ical inpu ,
hands-on explo a ion, and scena io-based ins uc ional design. G ounded in
Digi al Compe ence o Educa o s (DigCompEdu), Technological Pedagogical
Con en Knowledge (TPACK), and he Cogni i e A ec i e Model o Imme si e
Lea ning (CAMIL), he p og amme posi ioned eache s as designe s o imme si e
and AI-enhanced lea ning ac i i ies a he han passi e ool use s. Mixed-
me hods e alua ion, including ques ionnai es, ocus g oups, a e ac analysis,
and obse a ion, e ealed s ong in en ions o in eg a e XR/AI, heigh ened
sel -e icacy, and ecogni ion o mo i a ional gains o s uden s. Key suppo s
iden i ied included access o eady- o-use lesson scena ios, men o ing, de ice
a ailabili y, IT assis ance, and leade ship backing. Ba ie s cen ed on ime
cons ain s, in as uc u e, and cu icula igidi y. The indings unde sco e he
impo ance o ex ending exis ing compe ence amewo ks o add ess imme si e
pedagogy and e hical AI use, while si ua ing XR/AI in eg a ion wi hin he
eali ies o ins i u ional and na ional con ex s. The pape concludes wi h
ecommenda ions o sus ainable adop ion, emphasising in as uc u e
in es men , leade ship engagemen , e hical sa egua ds, and con inuous
communi ies o p ac ice.
Keywo ds: adul educa ion, eache p o essional de elopmen , ex ended eali y,
a i icial in elligence, i ual eali y, augmen ed eali y
1
1. In oduc ion
The educa ional landscape is changing as . Technologies like ex ended
eali y (XR) – including i ual eali y (VR), augmen ed eali y (AR), and mixed
eali y (MR) – and a i icial in elligence (AI) a e no longe jus expe imen al;
hey ha e become s anda d class oom ools [1]. XR o en s ands ou o i s se o
a o dances ha ma e o lea ning: p esence, in e ac i i y, adap abili y, and
scalabili y. Toge he , hese elemen s c ea e lea ning expe iences ha go beyond
wha adi ional class ooms can ypically o e [2]. Wi hin he b oade ise o
me a e se-s yle lea ning spaces, XR and AI can be combined o c ea e pe sis en ,
sha ed en i onmen s whe e eache s and s uden s wo k oge he , p ac ice
di icul asks, and c ea e esou ces om di e en loca ions, all ela ed o
cu iculum goals. Typical examples include he sa e p ac ice o high- isk
p ocedu es, i ual ield ips, embodied explo a ion o abs ac ideas, and spa ial
p oblem-sol ing wi h pee s who a e no in he same oom [1–6]. In pa allel, AI
suppo s pe sonalised p ac ice, ins uc ion ha adap s o he lea ne , and
con en gene a ion, which can imp o e daily eaching i used wisely [7, 8]. In
sho , XR and AI can be combined o design inqui y-based, hands-on, con ex -
ich ac i i ies ac oss di e en subjec s, which can lead o be e knowledge,
mo i a ion, and pe o mance [9]. In eache aining, XR also p o ides a sa e
space o p ac ice managing a class oom o ha ing di icul con e sa ions, while
AI can gi e clea p omp s and analyses ha encou age e lec ion [10].
Go e nmen policies a he na ional and Eu opean le els include bo h a eas in
hei plans o digi al and media skills; he EU’s Digi al Educa ion Ac ion Plan
men ions ha hey a e impo an o bo h ini ial and con inuing eache
educa ion [11, 12].
In eali y, p ac ice lags behind wha is possible. Tools a i e be o e eache s ha e
enough ime o s uc u ed suppo o in eg a e hem [13]. Es ablished amewo ks,
such as Digi al Compe ence o Educa o s (DigCompEdu) [11], ha e imp o ed
con idence wi h gene al digi al ools, bu hey ha e no been e ised o ully include
he de ails o imme si e, mul i-senso y se ings o he p ac ical issues o AI-d i en
da a analysis and cus omized eaching [4]. S udies also poin ou a g oup o needs
ha a ise when schools y o use XR/AI: spa ial ins uc ional app oaches, imme si e
assessmen , p esence managemen , e hics and p i acy conce ns in a a a -media ed
spaces, and ways o manage a class oom and s uden well-being [5, 6, 13]. Many
eache s eel com o able wi h mains eam echnology bu epo limi ed amilia i y
when he con ex shi s o imme si e media o AI-suppo ed eaching [10]. This is
mo e isible whe e de ice access, suppo om school leade s, and clea policy
guidance a y ac oss egions.
Gi en his si ua ion, his chap e discusses a sys ema ic e alua ion o a eache
p o essional de elopmen (TPD) p og amme deli e ed in 2024–2025. The
p og amme an ac oss i e sessions wi h 101 pa icipan s (53 p e-se ice; 48 in-
se ice) and ocused on ac i i ies using XR and AI. Ins ead o ocusing on basic
echnical skills, he p og am emphasised scena io-based, p ac ice-cen ed design,
d awing on DigCompEdu [11], he Technological Pedagogical Con en Knowledge
(TPACK) model [14], and he Cogni i e A ec i e Model o Imme si e Lea ning
(CAMIL) [15]. Hands-on explo a ion, e lec ion, and collabo a i e design we e key
componen s, in ended o e eal bo h he bene i s and limi a ions as pe cei ed by
di e en g oups o eache s.
Me a e se, Me aIn elligence and In ini e Wo lds wi h AI
2
This chap e has wo main con ibu ions. Fi s , i gi es a oadmap o using XR/
AI in eache educa ion in a way ha is sus ainable and inclusi e, based on da a. I
shows how a s uc u ed XR/AI TPD a ec s in en ions and con idence and iden i ies
he eache skills ha a e mos a ec ed: p o essional engagemen , imme si e
scena io design, assessmen , and lea ne empowe men . Second, i iden i ies he
pedagogical, echnical, and ins i u ional suppo s ha eache s say hey need o keep
using XR and AI egula ly in he G eek con ex , ansla ing hose insigh s in o
p ac ical plans o eache p epa a ion, ins i u ional policy, and echnology design.
The chap e uses a mixed-me hods design – ques ionnai es, ocus g oups,
pa icipan s’ a e ac s, and e lec i e obse a ion – o add ess wo esea ch
ques ions:
(RQ1) How does aking pa in a s uc u ed XR/AI p o essional de elopmen p og am
a ec eache s’ in en ions and con idence in using hese echnologies in hei eaching?
(RQ2) Wha pedagogical, echnical, and ins i u ional suppo s do eache s iden i y as
necessa y o implemen ing XR and AI in a sus ained way?
2. Theo e ical amewo k
This sec ion e iews how XR and AI a e used ac oss school, oca ional, and
highe educa ion, and wha ends o help o hinde adop ion. I desc ibes key
ideas, ypical applica ions in eache educa ion, and TPD models ha mo e beyond
‘ ools aining’ o e ec i e ins uc ion. I hen alks abou co e compe ence
amewo ks – DigCompEdu, TPACK, and CAMIL – and no es how hese a e being
ex ended o imme si e con ex s. I ends wi h a amewo k ha guided he aining
design and la e analysis.
2.1 XR and AI in educa ion and TPD: Cu en s a e and po en ial
XR is now used ac oss all educa ional le els. Th ee mechanisms appea epea edly
in e alua ions [1, 3]. Fi s , p esence and agency: lea ne s ac wi hin a cohe en 3D
scene a he han wa ching om he ou side. This can suppo a en ion and
mo i a ion when cogni i e load is managed. Second, embodimen and spa iali y:
abs ac s uc u es (like ana omical, a chi ec u al, and geog aphic) become
manipulable in space, helping lea ne s connec mul iple ep esen a ions. Thi d, sa e
ehea sal: p ocedu es and eaching me hods can be p ac ised wi hou eal-wo ld isk
and hen e iewed in deb ie [15, 16]. Re iews and p ojec syn heses epo s eady
gains in in ol emen and concep ual unde s anding when expe iences a e
pu pose ully designed and pai ed wi h ac i i ies be o e and a e he expe ience
[16–19]. In eache educa ion, common uses include class oom-managemen
simula ions, 360° ideo cases o p ac icing sensi i e con e sa ions, ole-playing
pa en – eache mee ings, mic o- eaching and obse a ion in simula ed class ooms,
p ac icing accommoda ions in special educa ion, and socio-emo ional awa eness
scena ios. Each o hese is usually pai ed wi h a s uc u ed in oduc ion and guided
e iew so ha ideas can be ans e ed back o class oom p ac ice [13, 16–18].
AI is used wi h XR and in s andalone ways. In imme si e ac i i ies, AI can o e
p omp s ha adap o he lea ne , pe o m da a analysis, con e speech o ex , and
p o ide ansla ion o inc ease access, as well as enable easy con en c ea ion [8, 10].
Sepa a ely, AI suppo s lesson and assessmen design (d a ing objec i es, ub ics,
In eg a ing Ex ended Reali y and A i icial In elligence in Educa ion…
DOI: h p://dx.doi.o g/10.5772/in echopen.1012606
3
and ques ion banks), con e sa ional u o ing, au oma ed eedback on w i ing o
code, and analy ics in eg a ed wi h lea ning pla o ms [7, 8]. Mos guidance
ecommends using AI whe e i suppo s ac i i ies and eedback while keeping
pedagogy and cu iculum in mind [8].
A he policy le el, EU and na ional ini ia i es inc easingly ecognise XR and
AI in eache ‑de elopmen agendas and a e aking p ac ical s eps o in eg a e
hem in o aining [16]. The Council o he EU has u ged membe coun ies o
embed ‘digi al pedagogy’ – including imme si e XR and AI – wi hin ini ial
eache educa ion and o suppo in‑se ice upskilling [20]. Eu opean s a egy
documen s simila ly no e ha AI, VR/AR, and ela ed echnologies a e eshaping
educa o s’ oles and expec eache s o co‑design e ec i e lea ning expe iences
[21]. P ojec s ac oss Eu ope e lec his. In G eece, he E asmus + PAX p ojec
p oposed a pedagogical model o XR in eache educa ion wi h modules on
li e acies, imme si e scena io design, and assessmen [18, 22]. Me aCi icEdu
implemen ed imme si e ci ic educa ion scena ios suppo ed by AI‑d i en design
ools such as CADMOS and Cha GPT, o e ing blended aining o u u e
ins uc ional designe s [9, 10, 23]. The sha ed aim is o align ini ial and
con inuing p epa a ion wi h echnological inno a ion so eache s can adop XR/
AI wi h con idence and pu pose.
TPD p og ammes o XR and AI end o ha e mo e impac when hey ame
educa o s as designe s o lea ning, no only as ool use s [10, 13]. Resea ch on
eache s as designe s shows ha p o essional g ow h inc eases when educa o s
i e a i ely plan, enac , and e lec on echnology-enhanced ac i i ies wi h au hen ic
cu iculum goals in mind, suppo ed by design ep esen a ions ha make
pedagogical in en isible [24–26]. Sho concep ual inpu s combined wi h hands-on
design and expe imen a ion – e.g., mic o- eaching in simula ions o apid
p o o yping o imme si e scena ios – help build con idence and p ac ical judgmen
[27]. These p og ams commonly ha e he same s uc u e: a sho in oduc ion o
explain goals and oles, a guided expe ience wi h XR/AI (o en in pai s o small
g oups, o a ing h ough eache , lea ne , and obse e oles), and a deb ie using
a e ac s (sc eensho s, sho clips, no es) o link expe ience o assessmen ,
accessibili y, and class oom managemen [9, 10, 13, 18]. This s uc u e enables
pa icipan s o connec in e ac ion pa e ns – like aking u ns in spaces wi h many
use s o gi ing p omp s in an AI u o – o inclusion and cu iculum goals, while
de eloping a common language o eedback.
Wo king oge he be ween de elope s and educa o s is also impo an . A ecen
Eu opean p ojec [17] uses a s epwise Cap u e → Plan → Realise → Apply p ocess o
mo e cu iculum needs o implemen a ions ha a e eady o he class oom. Cap u e
explains he lea ning p oblem and con ex ; Plan speci ies didac ic low, oles,
assessmen e idence, and accessibili y checkpoin s; Realise selec s o builds he XR/
AI expe ience; and Apply ocuses on class oom ials and changes. Following his has
led de elope eams o add ea u es eache s ask o (pause/ esume, ime s wi hin
he expe ience, da a analysis ha can be expo ed), which imp o es usabili y and
in eg a ion [19]. Ins uc ional design guidance speci ic o imme si e media – such as
ask size, na iga ion hin s, and e iew examples – suppo s c ea ing designs ha a e
scalable and use ul o eaching [2, 28].
Wi hin TPD, AI is inc easingly applied beyond i s ole in class oom scena ios.
Teache -design eams mainly use AI o d a lea ning objec i es, c ea e ub ics,
gene a e ques ion banks and scena io desc ip ions, o syn hesise pee eedback in o
Me a e se, Me aIn elligence and In ini e Wo lds wi h AI
4
conc e e nex s eps [7, 8]. In ou iew, his accele a es he p ocess wi hou displacing
pedagogical judgmen : eache s s ill se in en , e i y accu acy, and adap ou pu s o
lea ne p o iles and equi y aims [29]. Fo collabo a i e XR asks, eme ging heo y
sugges s ha clea ole sc ip s, sha ed ask iews, and e lec ion p omp s make
a di e ence – elemen s ha can be ehea sed in TPD and la e eused in class oom
p ac ice [15].
Finally, p ac ical suppo ools help enac men . Scena io ca ds mapped o
DigCompEdu domains, se up and sa e y checklis s o di e en de ices, and eady-
o-use e lec ion p o ocols o bo h eache s and s uden s c ea e con inui y ac oss
g oups and con ex s [9, 18]. New XR-speci ic compe ence desc ip ions gi e quick
p omp s – wha o explain, obse e, and documen o assessmen – ha a e use ul o
no ices and expe ienced educa o s alike [2, 4].
2.2 Digi al compe ence amewo ks
The Eu opean F amewo k o he DigCompEdu se s ou 22 educa o
compe ences ac oss six a eas: p o essional engagemen , digi al esou ces,
eaching and lea ning, assessmen , empowe ing lea ne s, and acili a ing
lea ne s’ digi al compe ence. I also desc ibes p og ession ac oss six p o iciency
le els (A1–C2). In p ac ice, i helps wi h goal-se ing, sel -assessmen , and
p og amme planning and e alua ion [11]. Imme si e, mul i-use en i onmen s
add equi emen s beyond he o iginal desc ip o s. Teache g oups wo king wi h
XR epo common needs, such as managing synch onous 3D ac i i ies (clea
oles, u n- aking ou ines, and i ual sa e y zones), managing p esence and
agency while pacing cogni i e load, ga he ing e idence om in e ac ion da a
(e.g., logs, sc eensho s) o assessmen , and planning e iew examples ha
suppo e lec ion. Accessibili y, physical sa e y, and da a p o ec ion also need
explici a en ion in sha ed i ual spaces [16–18]. In esponse, ecen ini ia i es
sugges XR-o ien ed ex ensions o he DigCompEdu – o en in he o m o ca d
se s and planning aids ha align each domain wi h eal class oom p ac ices and
p omp s o b ie ing, enac men , and deb ie – so eache s can wo k wi hin
a amilia amewo k while add essing XR-speci ic decisions [2, 4, 16–18].
TPACK adds o DigCompEdu by asking how con en , pedagogy, and echnology
come oge he in a pa icula lesson design, a he han ea ing hese domains
sepa a ely. In XR/AI con ex s, his is o en en iched wi h cons uc s om
imme si e-lea ning esea ch, especially he CAMIL, which explains how p esence,
agency, and cogni i e/a ec i e load shape lea ning and o e s p ac ical guidance on
gi ing signals, managing speed, and e iewing [14, 15]. A g owing s eam o wo k
ansla es his in o ’en iched TPACK’ ideas o imme si e lessons: align XR asks
wi h explici ou comes and quali ica ion amewo ks, speci y na iga ional hin s and
in e ac ion g anula i y, and plan o assessmen examples ha can be cap u ed
du ing o a e he expe ience [2, 28]. Whe e AI is also pa o he design p ocess,
complemen a y guidance helps eams decide whe e AI suppo s planning (e.g.,
d a ing objec i es, c ea ing ub ics), eedback, and analy ics while main aining
educa o s’ au onomy [29].
Used oge he , DigCompEdu (and i s XR- ocused ex ensions), TPACK, CAMIL,
and ela ed design guidance p o ide a solid basis o lis ing compe ence goals,
making p incipled design choices, and p epa ing assessmen and e lec ion p ocesses
ha i XR/AI-enhanced eaching [2, 4, 11, 14–16, 18, 28, 29].
In eg a ing Ex ended Reali y and A i icial In elligence in Educa ion…
DOI: h p://dx.doi.o g/10.5772/in echopen.1012606
5
2.3 Challenges and conside a ions
Findings om e iews and ieldwo k end o ag ee on a small se o condi ions
ha shape adop ion and con inued use. Figu e 1 summa ises hese condi ions.
P ac ically, p og ammes pay a en ion o de ice a ailabili y and sha ing,
class oom space and sa e y (clea play a eas, cable managemen , supe ision),
ne wo k eliabili y, and ou ines o se up and main enance (checklis s,
cha ging, upda es). Ins i u ions ha speci y who p epa es he equipmen , how
long he se up akes, and whe e he de ices a e s o ed end o mo e om pilo s o
egula use mo e smoo hly [5, 16–19]. Pedagogically, e ec i e implemen a ions
s a om clea lea ning ou comes and b ing XR/AI in o a lesson only whe e i
adds alue compa ed o exis ing me hods. Design wo k speci ies b ie –
expe ience–deb ie sequences, sca olds, and e idence o lea ning o be cap u ed
du ing o a e he ac i i y. P o ision o accessible al e na i es is made –
desk op iews, cap ions, na a ion, o al e na i e inpu s – so all lea ne s can
pa icipa e [2, 6, 24–26].
A an o ganisa ional le el, he e is a need o cla i y a ound pe mission, p i acy,
and da a lows in mul i-use pla o ms, ime abling ha i s de ice logis ics, and
Figu e 1.
Condi ions ha shape XR/AI in eg a ion in eaching (pa ially c ea ed wi h AI ools Cha GPT/DALL-E).
Me a e se, Me aIn elligence and In ini e Wo lds wi h AI
6
local suppo pa hways (who o con ac when some hing ails). These conce ns align
wi h Eu opean guidance on digi al educa ion and he in eg a ion o eme ging
echnologies in eache p epa a ion and con inuing p o essional de elopmen
[12, 19–21].
Paying a en ion o he lea ne expe ience is also impo an . Sho onboa ding
sequences help i s - ime use s; sessions a e paced wi h b ie b eaks o educe
mo ion sickness; cap ions and na a ion suppo unde s anding; and al e na i e
inpu s a e a ailable whe e needed. These measu es a e associa ed wi h s onge
engagemen and mo e equi able pa icipa ion in e alua ions o imme si e
ac i i ies [2, 5, 6, 16]. Finally, se e al enable s ecu : leade ship suppo ( ime
alloca ions, de ice pools, mic o-c eden ials), communi ies o p ac ice (pee
men o ing, co- eaching, scena io sha ing), and swi ching be ween de elope s
and educa o s o imp o e ea u es based on class oom eedback. Ready- o-use
e lec ion ools – o ins ance, s uc u ed ocus-g oup p omp s – o e eliable
ways o e iew expe ience, lea ning, and ans e o e e yday eaching [16–19,
24–26, 30]. Con ex s ill ma e s – policy, school cul u e, g oup size, p og amme
aims. App oaches ha adap o local condi ions while keeping a consis en
pedagogical s uc u e (b ie –expe ience–deb ie ) end o be mo e sus ainable
[16–18].
3. Me hodology
This s udy examined he ou comes o a s uc u ed TPD p og amme using
a mixed-me hods design. The app oach in eg a ed a pos - aining ques ionnai e,
esea che s’ obse a ion no es, examples o pa icipan s’ a e ac s, and sho
semi-s uc u ed ocus g oup discussions. The goal was o unde s and no only
whe he he p og amme changed sel - epo ed compe ence and in en ions, bu
also how eache s unde s ood he pedagogical, echnical, and ins i u ional
condi ions o sus ained use. Quan i a i e i ems p o ided b ead h; quali a i e
ma e ials added dep h.
A o al o 101 eache s, bo h p e-se ice and in-se ice, ook pa ac oss i e
independen sessions (Oc obe 2024–Ap il 2025). The design was in ended o c ea e
e idence abou indi idual skill/con idence and abou he con ex ual ac o s ha help
o hinde he in eg a ion o imme si e and AI-enhanced pedagogies.
3.1 Resea ch design
In his esea ch, we adop ed a con e gen pa allel mixed-me hods design
[31, 32]. Quan i a i e and quali a i e da a we e collec ed in pa allel, analysed
sepa a ely, and hen b ough oge he o in e p e a ion. The ocus was on h ee
ou comes:
●sel ‑assessed compe ences,
●in en ions o in eg a e XR/AI, and
●pe cei ed enable s and ba ie s ac oss pedagogical, echnical, and ins i u ional
dimensions.
In eg a ing Ex ended Reali y and A i icial In elligence in Educa ion…
DOI: h p://dx.doi.o g/10.5772/in echopen.1012606
7
The main da a sou ce was a pos -session ques ionnai e. This was complemen ed
by (a) s uc u ed obse a ion no es eco ded by he esea che s (who also se ed as
aine s), (b) hema ic analysis o indica i e pa icipan a e ac s (lesson plans,
imme si e scena ios, AI-gene a ed esou ces), and (c) sho semi-s uc u ed ocus
g oups a he end o selec ed sessions. The combina ion allowed iangula ion and
aligns wi h ecommenda ions o e alua ing TPD enhanced wi h echnology [4, 11,
14–16, 31, 32].
While he mixed-me hods app oach p o ided a de ailed and mul i-laye ed
accoun o pa icipan s’ expe iences, he s udy is cons ained by se e al ac o s.
Da a we e limi ed o immedia e pos - aining measu es and e lec ions, which
a e in o ma i e bu ime-bound; hey do no cap u e longe - e m ans e in o
class oom p ac ice. The sample was geog aphically concen a ed (Rhodes) and
composed o a speci ic mix o in-se ice and p e-se ice eache s wi h s ong
academic backg ounds. The indings should be ead as showing ends wi hin his
con ex a he han being b oadly ep esen a i e.
3.2 Pa icipan s
In o al, 101 eache s (74 emale and 27 male) pa icipa ed ac oss i e aining
sessions held be ween Oc obe 2024 and Ap il 2025 on he island o Rhodes, G eece.
Ages anged om 20 o 54 yea s; p e-se ice eache s we e mainly be ween 20 and
24, while in-se ice eache s co e ed a wide ange. Rec ui men u ilised
ins i u ional mailing lis s, pa ne ne wo ks, and in i a ions du ing dissemina ion
e en s. Pa icipa ion was olun a y and no compensa ed.
The in-se ice eache s (n = 48) came om di e en ields – ma hema ics,
science, IT, social s udies, languages, eligious educa ion, and his o y. All held a
leas a Mas e ’s deg ee, while some we e cu en s uden s o g adua es o he MSc
in New Fo ms o Educa ion and Lea ning (NFEL) a he Uni e si y o he
Aegean. Thei p o essional expe ience anged om ea ly-ca ee (<5 yea s) o
highly expe ienced eache s (>20 yea s). The p e-se ice eache s (n = 53) we e
bachelo -le el s uden s en olled in he Depa men o P eschool Educa ion
Sciences & Educa ional Design (TEPAES) a he Uni e si y o he Aegean. Mos
we e in hei inal o second- o-las yea o s udy. They knew he basics o
educa ional echnology bu had limi ed hands-on expe ience wi h XR/AI in
eaching. Table 1 p esen s he dis ibu ion o pa icipan s by session and
demog aphic composi ion.
T aining
session
To al
pa icipan s
In-se ice
eache s
P e-se ice
eache s Female Male A e age
age
1 10 10 0 7 3 42,3
2 9 9 0 6 3 41,7
3 34 16 18 25 9 33,6
4 23 13 10 16 7 31,7
5 25 0 25 20 5 21,2
To al 101 48 53
Table 1.
Pa icipan s’ dis ibu ion by aining session.
Me a e se, Me aIn elligence and In ini e Wo lds wi h AI
8
The s udy popula ion was geog aphically simila (Rhodes) bu di e en in ca ee
s ages, subjec backg ounds, and quali ica ions – which was help ul o compa ing
pe spec i es be ween in-se ice and p e-se ice eache s.
3.3 P o essional de elopmen p og amme s uc u e
The XR/AI TPD p og amme was deli e ed as i e independen sessions, each
las ing abou h ee hou s, s uc u ed a ound a b ie –expe ience–deb ie cycle
common in imme si e lea ning con ex s [15, 18, 27]. Sho inpu s we e blended wi h
hands-on explo a ion, collabo a i e design, and guided e lec ion. Co e hema ic
a eas included: (a) basics o XR/AI in educa ion (bene i s, limi s, e hics), (b)
imme si e scena io design h ough apid p o o yping, (c) AI-suppo ed planning
and assessmen (objec i es, ub ics, e lec i e p omp s), (d) managing a class oom
in XR en i onmen s wi h many use s, and (e) assessmen me hods app op ia e o
imme si e lea ning.
The cu iculum was based on DigCompEdu [11], TPACK [14], and he CAMIL
[15]. Table 2 maps amewo ks o XR-speci ic compe ences and example
ac i i ies. Teache s we e posi ioned no simply as echnology use s bu as
designe s o lea ning expe iences [24–26]. DigCompEdu in o med he
compe ence a ge s (e.g., digi al con en c ea ion, pedagogical use, lea ne
empowe men ). Following he XR-speci ic ex ensions p oposed by PAX [4], we
F amewo k/
Dimension
XR-en iched compe ences Example aining ac i i ies
DigCompEdu – digi al
con en c ea ion
Design imme si e esou ces wi h spa ial
p esence and embodied in e ac ion aligned
o objec i es.
G oup design o 360° science ield
ips; AI-assis ed 3D objec s o
his o y.
DigCompEdu – pedago-
gical use
Adap imme si e ools o cu icula goals;
manage XR o many use s; add
accessibili y.
Accessible XR language modules
wi h ansla ion and adjus able
in e ac ion modes.
DigCompEdu – lea ne
empowe men
Fos e agency using oles, explo a o y
asks, and adap i e eedback in XR.
Pee -led imme si e deba es wi h AI-
gene a ed e lec ion p omp s.
TPACK – echnological
knowledge
Selec /use XR/AI ools wi h a en ion o
speci ic bene i s.
Headse se up; XR app choice; AI
esou ce gene a ion.
TPACK – pedagogical
knowledge
Blend ac i e lea ning s a egies wi h XR/AI
bene i s.
P ojec -based XR asks in ma h and
social s udies.
TPACK – con en
knowledge
Ensu e subjec accu acy and connec ion
wi hin XR-enhanced con en .
XR his o y ou s wi h accu a e ime-
lines, AI na a ion, and con ex ual
a e ac s.
CAMIL – cogni i e
p ocessing
Manage cogni i e load wi h sho ,
sca olded asks, and deb ie .
Sho VR lab simula ions; guided
deb ie on esul s.
CAMIL – emo ional
engagemen
Build emo ional connec ion h ough s o y
and pe spec i e.
VR ole-plays on cul u al empa hy
and inclusion.
CAMIL – mo i a ional
alignmen
Balance challenge and skill; connec o
inne goals.
Gami ied XR challenges connec ed o
he cu iculum wi h pe sonalised
eedback.
Table 2.
Mapping amewo ks o XR-speci ic compe ences and example ac i i ies.
In eg a ing Ex ended Reali y and A i icial In elligence in Educa ion…
DOI: h p://dx.doi.o g/10.5772/in echopen.1012606
9
imme si e, AI-d i en pedagogies and p o ec s uden s’ well-being (e.g., managing
cogni i e load and p i acy conside a ions).
Compa ing ou indings wi h he b oade li e a u e e eals se e al simila
ends. Consis en wi h p io s udies [15–18], we ound ha eache s ecognize
he po en ial o XR/AI o en iching ins uc ion bu emain cau ious in adop ion.
Many eache s in ou p og am saw he bene i o hese echnologies o engage
s uden s, which aligns wi h e idence ha XR can boos mo i a ion and
pa icipa ion [25, 26]. Howe e , some pa icipan s emain hesi an o use XR/AI
wi hou subs an ial suppo [15, 17]. Key ba ie s men ioned – such as a lack o
con idence in using new ools, limi ed ime o lesson edesign, and lack o eady-
made digi al con en – ha e been widely obse ed in he li e a u e [15–17].
A u he pa e n, also seen in p io wo k [15], was he gap be ween expec a ions
and eali y: en husiasm some imes u ned o us a ion when de ices did no
wo k o sessions we e dis up ed. Such compa a i e insigh s highligh ha he
di usion o XR in educa ion emains g adual [24, 25] and highly dependen on
eache eadiness and con ex . A he same ime, ou esul s add o he g owing
e idence o posi i e ou comes when in eg a ion does occu . Teache s epo ed
ha s uden s we e mo e cu ious and in ol ed du ing XR lessons, e lec ing he
mo i a ional bene i s ound in ecen s udies [25, 26]. Thus, ou s udy bo h
suppo s and ex ends exis ing knowledge. I poin s ou known acili a o s and
obs acles bu also shows how hese dynamics play ou in a speci ic na ional
sys em.
This con ex is pa icula ly c ucial in he case o G eece, whe e sys emic and
e hical conside a ions s ongly a ec he space o inno a ion. The G eek educa ion
sys em ea u es a cen alized cu iculum and s ic egula ions ha shape how and
whe he XR/AI ools can be used in schools. Ou eache s no ed ha in lexible syllabi
and high-s akes exams lea e li le oom o ying new hings, a challenge in line wi h
p io s udies o educa ional inno a ion [13, 16]. Mo eo e , in as uc u al
cons ain s in G eek schools eme ged as a signi ican heme. Many schools
(especially public ones) lack eliable b oadband o Wi–Fi, and policies o en es ic
mobile de ices in class (unless special pe mission is gi en), c ea ing a pa adox whe e
eache s ained in XR ha e limi ed means o implemen i [13]. This highligh s how
dispa i ies in access and es ic i e egula ions can limi he impac o e en well-
designed TPD ini ia i es – a challenge also no ed in o he esea ch on digi al
inno a ion in schools [13, 24]. E hical and equi y implica ions also come up.
Teache s ocused on he impo ance o equal oppo uni ies: i only some schools wi h
suppo can deploy XR/AI, i could widen dispa i ies. They also exp essed conce n
abou ensu ing s uden sa e y and p i acy when using AI-d i en educa ional
so wa e, e lec ing he b oade e hical deba es a ound AI in educa ion (e.g.,
a oiding bias in algo i hms and p o ec ing s uden da a). In G eece, as in o he EU
con ex s, any adop ion o AI in class ooms mus ollow eme ging e hical guidelines
and GDPR egula ions, adding ano he laye o conside a ion o TPD p og ams. Ou
discussion highligh s ha success ul in eg a ion o XR/AI is no jus a echnical o
pedagogical issue, bu a con ex ual one. I needs o ollow local policy, in as uc u al
suppo , and obus e hical ules.
Finally, i is impo an o acknowledge he limi a ions o his s udy. The s udy
was ela i ely small in bo h scale and du a ion, which limi s he gene alisabili y o
he indings [24]. Simila pilo s udies in eg a ing AR/VR in eache aining ha e
cau ioned ha esul s may no easily ans e o all se ings o scales wi hou u he
Me a e se, Me aIn elligence and In ini e Wo lds wi h AI
16
alida ion [24, 25]. The g oup o pa icipa ing eache s was sel -selec ed and
mo i a ed, which may ha e in oduced a posi i e bias, also obse ed in simila TPD
s udies [15, 18]. Addi ionally, he e alua ion o impac was sho - e m, ocusing on
immedia e changes in eache p ac ice and s uden engagemen ollowing he TPD.
Ou esul s p o ide an ini ial snapsho – o example, eache s did implemen XR/AI
ac i i ies and obse ed en husias ic s uden esponses – bu we canno con i m i
hese changes las o e ime. P io esea ch has emphasized he need o longi udinal
s udies o de e mine whe he ini ial gains a e sus ained once he no el y o
echnology ades [13, 25]. Technical and ins i u ional cons ain s also shaped he
implemen a ion: de ice sho ages, so wa e issues, and school policies limi ing
mobile AR all a ec ed he wo k, as seen in o he esou ce-cons ained con ex s [26].
Recognising hese limi a ions posi ions ou s udy wi hin he wide landscape o
explo a o y esea ch. Fu u e wo k should he e o e ex end he app oach o la ge
g oups, o e longe pe iods, and ac oss mo e a ied school se ings o assess
ans e abili y [24–26]. Despi e he limi a ions, he indings p o ide p ac ical insigh
in o wha is needed – om policy suppo o echnical in as uc u e – o e ec i ely
b ing XR and AI in o he TPD.
6. Conclusion
This esea ch began o explo e wo key ques ions ega ding he in eg a ion o
XR and AI in TPD. Add essing RQ1, he esul s show se e al ac o s a ec ing
eache s’ willingness o adop XR/AI. The mos signi ican we e ex e nal suppo s
and in e nal eadiness; adop ion was mo e likely when echnologies aligned wi h
cu icula aims, leade ship-o e ed suppo , and in as uc u e, such as de ices and
connec i i y, was eliable. Equally impo an we e pe sonal ac o s such as he
eache s’ own digi al con idence and mo i a ion o inno a e. The s udy poin s ou
ha XR/AI in eg a ion is acili a ed by a suppo i e en i onmen and by aining
ha builds eache s’ sel -e icacy in using hese ools.
In esponse o RQ2, he s udy p o ides conc e e insigh s and s a egies. We
no iced ha success ul in eg a ion goes beyond in oducing new ools; i in ol es
me ging XR/AI in o pedagogically meaning ul ac i i ies. Teache s in ou p og am
lea ned o design imme si e lesson plans ha we e igh ly linked o hei cu iculum
goals. They epo ed ha hese app oaches no only engaged s uden s h ough
in e ac i i y and ich isualiza ion bu also encou aged s uden -cen e ed lea ning, as
shown by inc eased s uden cu iosi y and pa icipa ion du ing he XR/AI-suppo ed
lessons. Howe e , he in eg a ion wo ked bes when eache s adap ed he
echnology o hei con ex : s a ing wi h small, manageable implemen a ions (such
as a single AR-enhanced p ojec ), aligning hem wi h exis ing lesson objec i es, and
g adually scaling up as con idence g ew. This poin s o XR/AI adop ion as an
i e a i e p ocess: y, e lec , and e ine. Mo eo e , ou esul s emphasize ha TPD
p og ams should model hese s a egies, p o iding eache s wi h hands-on
expe ience in XR/AI, oppo uni ies o collabo a e and sha e expe iences, and
guidance on oubleshoo ing echnical o class oom managemen challenges. By
doing so, TPD can ansla e he p omise o XR/AI in o ac ual class oom p ac ice.
O e all, he s udy demons a es ha , wi h he igh suppo and p epa a ion,
eache s can in eg a e XR and AI in ways ha en ich lea ning expe iences,
add essing bo h esea ch ques ions. The challenge and he oppo uni y lie in building
In eg a ing Ex ended Reali y and A i icial In elligence in Educa ion…
DOI: h p://dx.doi.o g/10.5772/in echopen.1012606
17
he ecosys ems – pedagogical, echnical, and ins i u ional – ha enable ha
in eg a ion o ake oo and g ow. Below, we ansla e hese insigh s in o ac ionable
ecommenda ions o s akeholde s looking o build on his wo k and ad ance XR/AI
in eg a ion in educa ion:
●Align eache aining wi h digi al compe ence amewo ks: Design and imple-
men TPD p og ams ha map on o known amewo ks like DigCompEdu,
ex ending hem o include XR/AI-speci ic skills. This alignmen will ensu e ha
p o essional de elopmen no only builds p ac ical class oom echniques bu
also mee s b oade educa ional s anda ds o eache s' digi al compe encies.
●In es in in as uc u e and echnical suppo : P io i ise unding and esou ces
o equip schools wi h he necessa y echnology (high-speed in e ne , AR/VR
de ices, and upda ed so wa e) and on-si e echnical suppo . Reliable in a-
s uc u e is ounda ional o eache s o con iden ly in eg a e XR/AI ools, and
such in es men e lec s a b oade educa ional commi men o digi al inno a-
ion and equi y.
●Fos e a suppo i e school cul u e and leade ship engagemen : Encou age school
leade s o ac i ely suppo expe imen a ion wi h XR and AI in eaching. This
can include p o iding eache s wi h dedica ed ime o collabo a i e planning,
educing he ea o ailu e by celeb a ing inno a i e a emp s, and in eg a ing
XR/AI goals in o he school’s de elopmen plans. S ong adminis a i e backing
c ea es an en i onmen whe e eache s eel sa e and alued when adop ing new
me hodologies, in line wi h wide calls o ans o ma i e school leade ship in
digi al educa ion.
●In eg a e e hics and da a p i acy in o XR/AI ini ia i es: Ensu e ha any in o-
duc ion o AI-d i en ools o da a-in ensi e XR applica ions in educa ion comes
wi h clea guidelines and aining on e hical use. Teache s should be p epa ed o
add ess issues such as s uden da a p i acy, in o med consen o using AI
ecommenda ions, and ecognizing po en ial biases in AI con en . By embed-
ding hese discussions in TPD, educa ional au ho i ies can align he mic o-le el
implemen a ion wi h mac o-le el p io i ies, like he EU’s e hical guidelines o
AI in educa ion, hus p omo ing esponsible inno a ion.
●P o ide con inuous men o ship and communi ies o p ac ice: Mo e beyond
one-o wo kshops by es ablishing ongoing suppo mechanisms – such as
men o ing by expe ienced XR/AI educa o s o p o essional lea ning commu-
ni ies (PLCs) – whe e eache s can sha e expe iences, oubleshoo challenges,
and collec i ely de elop bes p ac ices. This sus ained suppo mi o s e ec i e
PD models in o he domains and helps main ain momen um, ensu ing ha
ini ial gains in eache capaci y lead o long- e m changes in p ac ice.
●Plan o long- e m and scalable e alua ion: Inco po a e e alua ion plans ha
moni o he impac o XR/AI in eg a ion o e ex ended pe iods and ac oss
mul iple schools. By collec ing da a on bo h sho - e m successes and longe -
e m ou comes (e.g., how eaching s a egies and s uden pe o mance e ol e
a e one yea ), s akeholde s can make in o med decisions abou scaling up he
Me a e se, Me aIn elligence and In ini e Wo lds wi h AI
18
ini ia i es. Such a commi men o e idence-based scaling esona es wi h
b oade educa ional p io i ies o accoun abili y and con inuous imp o emen in
inno a ion adop ion.
Acknowledgemen s
This wo k was pa ially suppo ed by he Eu opean Commission unde he
p ojec PAX—Pedagogical Alliance o XR-Technology in (Teache ) Educa ion—
ERASMUS-EDU-2023-PI-ALL-INNO, P ojec No. 101139827. We also ex end ou
hanks o all collabo a o s and pa ne s in ol ed.
Au ho de ails
S a os Pi sikalis
1
and Ilona-Ele e yja Lasica
2
*
1 Uni e si y o he Aegean, Depa men o P eschool Educa ion Sciences &
Educa ional Design, Rhodes, G eece
2 Uni e si y o he Aegean, Rhodes, G eece
*Add ess all co espondence o: [email p o ec ed]
© 2025 The Au ho (s). Licensee In echOpen. This chap e is dis ibu ed unde he
e ms o he C ea i e Commons A ibu ion License (h p://c ea i ecommons.o g/
licenses/by/4.0/), which pe mi s un es ic ed use, dis ibu ion, and ep oduc ion in
any medium, p o ided he o iginal wo k is p ope ly ci ed.
In eg a ing Ex ended Reali y and A i icial In elligence in Educa ion…
DOI: h p://dx.doi.o g/10.5772/in echopen.1012606
19
Re e ences
[1] Alnag a A, Ismail RC, Id us SZ,
Al aqi RM. A e iew o ex ended eali y
(XR) echnologies in he u u e o
human educa ion: Cu en end and
u u e oppo uni y. Jou nal o Human
Cen e ed Technology. 2022;1(2):81–96.
DOI: 10.11113/humen ech. 1n2.27
[2] Pi sikalis S, Lasica IE, Kos as A,
Vi silaki C. Educa ional design
guidelines o eaching wi h imme si e
echnologies—upda ing lea ning
ou comes o he Eu opean quali ica ion
amewo k. T ends in Highe
Educa ion. 2024;3(4):1091–1108. DOI:
10.3390/highe edu3040064
[3] Bu ke D, C omp on H, Nickel C.
The use o ex ended eali y (XR) in
highe educa ion: A sys ema ic e iew.
TechT ends. 2025;17:1–4. 10.1007/s1152
8-025-01092-y
[4] Ru en N, B ouwe -T uijen K.
De ining XR-speci ic eache
compe encies: Ex ending he
DigCompEdu amewo k o imme si e
educa ion. T ends in Highe Educa ion.
2025;4(1):11. DOI: 10.3390/
highe edu4010011
[5] Thomason J. Me aheal h-how will
he me a e se change heal h ca e?.
Jou nal o Me a e se. 2021;1(1):13–16.
[6] Ga zón J, Pa ón J, Baldi is S.
Sys ema ic e iew and me a-analysis o
augmen ed eali y in educa ional
se ings. Vi ual Reali y. 2019;23(4):447–
459. DOI: 10.1007/s10055-019-00379-9
[7] Chiu TK, Xia Q, Zhou X, Chai CS,
Cheng M. Sys ema ic li e a u e e iew
on oppo uni ies, challenges, and u u e
esea ch ecommenda ions o a i icial
in elligence in educa ion. Compu e s
and Educa ion: A i icial In elligence.
2023;4. DOI: 10.1016/
j.caeai.2022.100118
[8] Ouyang F, Jiao P. A i icial
in elligence in educa ion: The h ee
pa adigms. Compu e s and
Educa ion: A i icial In elligence.
2021;2. DOI: 10.1016/
j.caeai.2021.100020
[9] Tsa ami sis K, Lasica I,
Lamp opoulos G, Tsinakos A, Kazanidis I,
Te zopoulos G, Ba es K, Hazboun S,
Mangina E Empowe ing ci ic educa ion
h ough Me a e se-en iched eaching
scena ios. In: P oceedings o he 2025
Annual Con e ence o he Eu opean
Associa ion o Ex ended Reali y
(Eu oXR 2025). 2025
[10] Re alis S, Lasica IE,
Lamp opoulos G, Pana I, A e ou G
Designing imme si e educa ional
scena ios o ci ic educa ion: T aining
u u e ins uc ional scena io designe s
(in G eek). In: P oceedings o he 14 h
Con e ence “ICT in Educa ion” (ETPE
2025). 2025
[11] Redecke C, Punie Y, edi o .
Eu opean F amewo k o he Digi al
Compe ence o Educa o s:
DigCompEdu. Luxembou g:
Publica ions O ice o he Eu opean
Union; 2017. DOI: 10.2760/159770
[12] Eu opean Commission.
Di ec o a e-gene al o educa ion,
you h, spo and cul u e. In: Digi al
Educa ion Ac ion Plan 2021-2027:
Imp o ing he P o ision o Digi al Skills
in Educa ion and T aining.
Luxembou g: Publica ions O ice o he
Eu opean Union online ; 2023.
10.2766/149764
Me a e se, Me aIn elligence and In ini e Wo lds wi h AI
20
[13] Pi sikalis S, Lasica IE, Kos as A,
Vi silaki C In eg a ing augmen ed eali y
in o educa ion and aining: Rema ks and
insigh s om a i e-yea expe ience in he
ield. In: EDULEARN22 P oceedings 2022
(pp. 1565–1571). IATED. DOI: 10.21125/
edulea n.2022.0414
[14] Alemán-Sa a ia AC,
Technology D-A-A, Pedagogy and
Con en (TPACK amewo k):
Sys ema ic li e a u e e iew. In: 2021
XVI La in Ame ican Con e ence on
Lea ning Technologies (LACLO) 2021
Oc 19. (pp. 104–111). IEEE. DOI: 10.11
09/LACLO54177.2021.00069
[15] Mak ansky G, Pe e sen GB. 2021.
The cogni i e a ec i e model o
imme si e lea ning (CAMIL):
A heo e ical esea ch-based model o
lea ning in imme si e i ual eali y.
Educa ional Psychology Re iew. 33
(3):937–958. DOI: 10.1007/s10648-020-0
9586-2
[16] Ve degaal S, Mo ssink-San ing VE,
B ouwe -T uijen K, Ru en N.
Examining he Educa ional
E ec i eness o VR and AR: Insigh s
om Recen Li e a u e (V1.0). PAX
P ojec , Wo k Package 2, Deli e able
2.2. Enschede: Saxion Uni e si y o
Applied Sciences; 2024
[17] Ru en N, B ouwe -T uijen K. An
Analysis o Di e se Pe spec i es on
Teaching wi h XR Resul ing in Conc e e
Building Blocks o XR-based Educa o
P o essionaliza ion. PAX P ojec , Wo k
Package 2, Task 2.1. Enschede: Saxion
Uni e si y o Applied Sciences; 2024
[18] Albe S, Bu ian R, Pu gaj J,
Khongklad C, Ma z F, Wi ba z K, e al.
PAX F amewo k o Teache s (1s
D a ). PAX P ojec , Deli e able D3.1.
Vienna: Uni e si y o Teache
Educa ion Vienna; 2024
[19] Khongklad C, K üge M,
Win e s S, Sme s J, Sagi N,
Zacha iadis A. PAX Guideline o
De elope s (1s D a ). PAX P ojec ,
Wo k Package 4, Deli e able D4.1.
Müns e : FH Müns e Uni e si y o
Applied Sciences; 2024
[20] Council o he Eu opean Union.
Council ecommenda ion o
23 No embe 2023 on he key enabling
ac o s o success ul digi al educa ion
and aining (C/2024/1115) [In e ne ].
O icial Jou nal o he Eu opean Union,
C 2024/1115, 2024. A ailable om:
h ps://eu -lex.eu opa.eu/legal-con en /
EN/TXT/PDF/?u i=OJ/:C_202401115
[Accessed: 2025-Augus -12]
[21] Eu opean Commission. Digi al
educa ion ac ion plan 2021–2027:
Rese ing educa ion and aining o he
digi al age [In e ne ]. Luxembou g:
Publica ions O ice o he Eu opean
Union; 2020. A ailable om: h ps://
educa ion.ec./eu/ opa./eu/ ocus- opics/
digi al/educa ion/-ac ion-plan
[Accessed: 2025-Augus -12]
[22] PAX P ojec . The PAX amewo k:
Pedagogical model o in eg a ing XR in
eache educa ion [In e ne ]. E asmus+
P og amme; 2024. A ailable om:
h ps://pax-p ojec ./eu// [Accessed:
2025-Augus -12]
[23] P ojec M. Designing imme si e
ci ic educa ion scena ios wi h XR and AI
ools [In e ne ]. E asmus+ P og amme;
2024. A ailable om: h ps://me aci ic
.eu// [Accessed: 2025-Augus -12]
[24] Asensio-Pé ez JI, Dimi iadis Y,
Pozzi F, He nández-Leo D, P ie o LP,
Pe sico D, Villag á-Sob ino SL. Towa ds
eaching as design: Explo ing he
in e play be ween ull-li ecycle lea ning
design ooling and eache p o essional
de elopmen . Compu e s & Educa ion.
In eg a ing Ex ended Reali y and A i icial In elligence in Educa ion…
DOI: h p://dx.doi.o g/10.5772/in echopen.1012606
21
2017;114:92–116. 10.1016/
j.compedu.2017.06.011
[25] Lau illa d D, Kennedy E,
Cha l on P, Wild J, Dimakopoulos D.
2018. Using echnology o de elop
eache s as designe s o TEL: E alua ing
he lea ning designe . B i ish Jou nal
o Educa ional Technology. 49
(6):1044–1058. DOI: 10.1111/bje .12697
[26] Pozzi F, Asensio-Pe ez JI,
Ce egini A, Dagnino FM, Dimi iadis Y,
Ea p J. Suppo ing and ep esen ing
lea ning design wi h digi al ools: In
be ween guidance and lexibili y.
Technology, Pedagogy and Educa ion.
2020;29(1):109–128. DOI: 10.1080/
1475939X.2020.1714708
[27] Boloudakis M, Re alis S,
Psa omiligkos Y. T aining no ice
eache s o design moodle‐based uni s o
lea ning using a CADMOS‐enabled
lea ning design sp in . B i ish Jou nal o
Educa ional Technology. 2018;49
(6):1059–1076. DOI: 10.1111/bje .12678
[28] Cas elhano M, Mo gado L,
Ped osa D. Ins uc ional design models
o imme si e i ual eali y:
A sys ema ic li e a u e e iew. SIIE23.
XXV Simpósio In e nacional de
In o má ica Educa i a. 2023;272–278
[29] Ca alho L, Ma inez-Maldonado R,
Tsai YS, Ma kauskai e L, De Laa M. How
can we design o lea ning in an AI
wo ld?. Compu e s and Educa ion:
A i icial In elligence. 2022;3. DOI: 10.10
16/j.caeai.2022.100053
[30] O. Nyumba T, Wilson K,
De ick CJ, Mukhe jee N. 2018. The use
o ocus g oup discussion me hodology:
Insigh s om wo decades o applica ion
in conse a ion. Me hods in Ecology
and E olu ion. 9(1):20–32. DOI: 10.111
1/2041-210X.12860
[31] Plano Cla k VL. Mixed me hods
esea ch. The Jou nal o Posi i e
Psychology. 2017;12(3):305‑6. DOI: 10.
1080/17439760.2016.1262619
[32] Johnson RB, Onwuegbuzie AJ.
Mixed me hods esea ch: A esea ch
pa adigm whose ime has come.
Educa ional Resea che . 2004;33
(7):14–26. DOI: 10.3102/
0013189X033007014
[33] Peel KL. A beginne ’s guide o
applied educa ional esea ch using
hema ic analysis. P ac ical Assessmen
Resea ch and E alua ion. 2020;25(1).
DOI: 10.7275/ y 5-k983
[34] Zhi Y, Wu L. Ex ended eali y in
language lea ning: A cogni i e a ec i e
model o imme si e lea ning
pe spec i e. F on ie s in Psychology.
2023;14. DOI: 10.3389/
psyg.2023.1109025
Me a e se, Me aIn elligence and In ini e Wo lds wi h AI
22
