Chap e 12
Emb acing machine ansla ion in L2
educa ion: B idging heo y and p ac ice
in he AI Age
A sushi Mizumo o
Kansai Uni e si y, Japan
This chap e examines he e ol ing ole o machine ansla ion (MT) in second
language (L2) educa ion. I i s summa ises ecen esea ch on MT’s pedagogical
applica ions, syn hesising indings om sys ema ic e iews ha highligh MT’s e -
ec i eness in L2 w i ing when used app op ia ely. The chap e explo es lea ne
and eache pe cep ions, no ing a gene ally posi i e a i ude among s uden s bu
a mo e di ided s ance among educa o s. I p esen s a comp ehensi e amewo k
o unde s anding ac o s in luencing MT use in L2 w i ing, encompassing linguis-
ic, pe sonal, con ex ual, and ideological dimensions. The chap e in oduces he
concep o “MT as Augmen ed L2 Compe ence,” o e ing a new pa adigm o in e-
g a ing MT in o language lea ning. I discusses a ious models o MT ins uc ion,
including he Guided Use o MT (GUMT) model. Finally, he chap e p oposes
he Me acogni i e Resou ce Use (MRU) amewo k, which posi ions lea ne s as
me acogni i e agen s capable o s a egically u ilising a wide ange o language e-
sou ces, including MT and gene a i e AI (GenAI) ools. This in eg a ed app oach
aims o os e au onomous lea ne s who can e ec i ely na iga e he complex land-
scape o digi al language lea ning esou ces.
1 Cu en s a us o MT use in L2 educa ion
1.1 Pedagogical applica ions
As e idenced by he ecen inc ease in publica ions on MT use o L2 lea ning and
eaching, a weal h o esea ch epo s a ious pedagogical applica ions o MT in
A sushi Mizumo o. 2026. Emb acing machine ansla ion in L2 educa ion: B idging
heo y and p ac ice in he AI Age. In JC Pene , Joss Moo kens & Masa u Yamada
(eds.), Teaching ansla ion in he age o gene a i e AI: New pa adigm, new lea ning?,
233–248. Be lin: Language Science P ess. DOI: 10.5281/zenodo.17641087
A sushi Mizumo o
L2 educa ion. These applica ions include using MT as a esou ce o ocabula y
acquisi ion (Lo 2025, 2024) and g amma p ac ice (Lee & Kang 2024), os e ing
me alinguis ic awa eness h ough compa a i e analysis o o iginal and machine-
ansla ed ex s (Tsai 2019), and enhancing lea ne au onomy by p omo ing sel -
di ec ed lea ning (Lee 2020).
Indeed, he p oli e a ion o MT- ela ed esea ch o e he pas wo decades
has led o he eme gence o se e al sys ema ic e iews (Jolley & Maimone 2022,
Klimo a e al. 2022, Lee 2023, Ohashi 2024). These comp ehensi e analyses p o-
ide aluable insigh s in o he cu en s a e o MT use in L2 educa ion and high-
ligh key ends and indings ac oss mul iple s udies. Jolley & Maimone (2022)
e iewed MT s udies om 2000 o 2019, ocusing on MT accu acy, educa ional
applica ions, and pe cep ions o lea ne s and eache s. They no ed he imp o e-
men in MT quali y o e ime and i s e ec i eness in L2 w i ing, while also high-
ligh ing unce ain ies abou i s long- e m impac on language lea ning. Klimo a
e al. (2022) analyzed 13 s udies published be ween 2018 and 2021, speci ically
examining he impac o neu al machine ansla ion (NMT) in o eign language
educa ion. Thei e iew ound ha NMT can be bene icial o language lea ning,
pa icula ly o ad anced lea ne s, bu also iden i ied a lack o eache aining
in his a ea.
Lee (2023) conduc ed a sys ema ic e iew o 87 s udies om 2000 o 2019, in-
es iga ing he e ec i eness o MT in o eign language educa ion. The e iew
highligh ed MT’s use ulness in L2 w i ing, mixed pe cep ions among lea ne s,
and disc epancies be ween eache and s uden pe cep ions. I also emphasised
he need o u he esea ch on long- e m lea ning e ec s and app op ia e us-
age me hods. Lee also conduc ed a me a-analysis wi h 12 s udies ha sa is ied
p ede ined c i e ia. I e ealed ha u ilising MT had a posi i e impac on L2
lea ning wi h a small e ec size (g= 0.345, 95% CI [0.201, 0.489]). The analysis
o subca ego ies showed ha using MT was e ec i e in a ious linguis ic a eas,
including lexical accu acy (g= 0.616, 95% CI [0.243, 0.676]), syn ac ic accu acy
(g= 0.562, 95% CI [0.371, 0.754]), syn ac ic complexi y (g= 0.453, 95% CI [0.181,
0.726]), o hog aphy (g= 0.741, 95% CI [0.424, 1.059]), and o e all w i ing qual-
i y (g= 0.768, 95% CI [0.547, 0.990]). Howe e , he e ec on lexical complexi y
was no s a is ically signi ican (g= 0.253, 95% CI [-0.058, 0.564]). These indings
p o ide quan i a i e e idence o he posi i e impac o MT on a ious aspec s
o language lea ning, pa icula ly in w i ing skills. This addi ional in o ma ion
s eng hens he o e all na a i e by p o iding speci ic quan i a i e e idence o
MT’s e ec i eness in a ious aspec s o language lea ning. I suppo s he gen-
e al consensus among he e iews ha MT can be bene icial o L2 w i ing when
234
12 Emb acing machine ansla ion in L2 educa ion:
used app op ia ely, while also highligh ing a eas whe e i s impac may be less
p onounced (such as lexical complexi y).
Ohashi (2024) examined 14 s udies published be ween 2020 and 2022, epo -
ing on he la es ends and key indings in MT esea ch. This e iew emphasised
ha MT can be a good s a ing poin o language lea ning, bu i s e ec i eness
a ies depending on lea ne p o iciency and a ge language. I also s essed he
impo ance o communica ion be ween eache s and lea ne s and he need o
eache aining in MT use.
The sys ema ic e iews collec i ely indica e ha MT can be an e ec i e ool
o language lea ning when used app op ia ely. They emphasise ha MT is pa -
icula ly bene icial o L2 w i ing, helping s uden s educe lexico-g amma ical
e o s and ocus mo e on con en (Klimo a e al. 2022, Lee 2023). Howe e , he
e iews also poin ou ha he e ec i eness o MT use may a y depending on
ac o s such as s uden s’ p o iciency le els, a ge language pai s, and speci ic
lea ning asks (Ohashi 2024). Fu he mo e, hese e iews unde sco e he impo -
ance o p ope guidance and aining o bo h s uden s and eache s in e ec-
i ely using MT o language lea ning. They highligh a gap be ween eache s’
pe cep ions and s uden s’ ac ual p ac ices, emphasising he need o be e com-
munica ion and mu ual unde s anding (Jolley & Maimone 2022, Lee 2023). The
e iews also call o mo e esea ch on he long- e m e ec s o MT use on lan-
guage acquisi ion and he de elopmen o app op ia e pedagogical s a egies o
in eg a e MT in o language cu icula e ec i ely.
1.2 Lea ne and eache pe cep ions
As epo ed abo e, MT use is widesp ead among language lea ne s, who u ilise
hese ools o a a ie y o pu poses. S udies ha e ound ha a signi ican pe cen -
age o s uden s, pa icula ly hose in highe educa ion se ings, epo equen ly
using MT ools like Google T ansla e o language lea ning asks, including w i -
ing and ansla ion (Lee 2020, Tsai 2019). This widesp ead use pe sis s despi e
lea ne s o en being awa e o MT’s limi a ions, pa icula ly ega ding accu acy
and he po en ial o g amma ical e o s (Jolley & Maimone 2022).
Lea ne s p ima ily iew MT as a con enien and bene icial aid o language
lea ning, using i o asks such as looking up wo ds and ph ases, acili a ing
ansla ion, and enhancing he e ision p ocess. While some lea ne s exp ess
conce ns abou o e - eliance on MT and i s po en ial impac on lea ning, e-
sea ch indica es a gene ally posi i e a i ude owa ds MT’s ole in suppo ing
language acquisi ion (Chung & Ahn 2022).
235
A sushi Mizumo o
In con as o lea ne pe spec i es, he li e a u e highligh s a mo e di ided
s ance among eache s ega ding he use o MT in L2 educa ion. While many
eache s con inue o exp ess conce ns abou MT’s po en ial o hinde language
acquisi ion and acili a e academic dishones y, o he s ecognise i s pedagogical
po en ial as a aluable lea ning esou ce (Duca & Schocke 2018).
The eluc ance among eache s o emb ace MT s ems om se e al ac o s.
Fi s ly, he his o ical associa ion o MT wi h inaccu a e ansla ions, o en e-
e ed o as a “bad model,” and i s pe cei ed h ea o adi ional eaching me h-
ods con ibu es o skep icism (Lo 2025). Conce ns abou academic in eg i y, wi h
MT being iewed as a o m o chea ing, also con ibu e o his eluc ance. This
o en leads eache s o ocus on de ec ing and p e en ing MT use a he han
explo ing i s po en ial bene i s (Jolley & Maimone 2022).
Howe e , he e is a g owing mo emen among esea che s and p ac i ione s
o in eg a e MT in o language class ooms in a meaning ul and esponsible man-
ne (Hellmich 2023, Lee 2020). This shi is d i en by he unde s anding ha
MT use is now ubiqui ous and canno be e ec i ely banned (Duca & Schocke
2018). Ins ead, ad oca es o MT in eg a ion call o ins uc ing lea ne s abou i s
s eng hs and weaknesses, equipping hem o use i c i ically and e hically as a
ool o language lea ning (Klimo a e al. 2022).
1.3 Fac o s and iews in luencing MT use
The scoping e iew by Jiang e al. (2024), which syn hesises 29 MT s udies in
L2 w i ing om 2009 o 2023, examines he ac o s in luencing MT use in L2
w i ing, ocusing on ou main ca ego ies: linguis ic, pe son/indi idual, con ex-
ual, and ideological ac o s, amed h ough cogni i e, sociocul u al, and c i ical
heo e ical pe spec i es.
• Linguis ic ac o s (cogni i e pe spec i e): The e iew analyzes MT’s ole
as a linguis ic p ocesso , explo ing how he cogni i e p ocessing o MT
impac s L2 w i ing. I explo es he e ec s o di e en w i ing asks on MT
u ilisa ion and assesses how he quali y o inpu and ou pu om MT sys-
ems a ec s he esul ing ex complexi y and s uc u e. These conside a-
ions a e c ucial in de e mining how bo h eache s and s uden s pe cei e
and employ MT, shaping i s in eg a ion in o educa ional p ac ices.
• Pe son/indi idual ac o s (sociocul u al pe spec i e): MT is iewed as a
media ional a i ac / ool wi hin social con ex s, inco po a ing ac o s such
as s uden s’ belie s abou MT’s e ec i eness, hei w i ing s a egies, L2
236
12 Emb acing machine ansla ion in L2 educa ion:
p o iciency, and digi al li e acy. Addi ionally, eache - ela ed ac o s in-
clude hei a i udes owa ds MT, pedagogical in eg a ion o MT, and abil-
i y o de ec i s use. These indi idual ac o s c i ically in luence educa-
ional app oaches and he e ec i eness o MT.
• Con ex ual ac o s (sociocul u al pe spec i e): The ole o MT is in luenced
by he dynamics wi hin educa ional en i onmen s, such as pee in e ac-
ions, he p esence o MT aining o sca olding in ins uc ional se ings,
and ins i u ional policies ha may ei he suppo o es ic MT usage.
These con ex ual ac o s a e pi o al in shaping he ope a ional and si u-
a ional en i onmen s o MT, impac ing i s pe cep ion and e icacy in ed-
uca ional se ings.
• Ideological ac o s (c i ical pe spec i e): Ideological conside a ions ocus
on language- ela ed ideologies, including monolingual biases, s anda d
language ideologies, and anslanguaging s ances, and how hese in lu-
ence he use and pe cep ion o MT. Such ideologies challenge con en-
ional language educa ion p ac ices and in luence how MT is in eg a ed
and impac s linguis ic hie a chies wi hin educa ional con ex s.
Jiang e al. (2024) p opose a laye ed concep ual amewo k o unde s and he
ac o s in luencing MT use in L2 w i ing. Wi h ideological ac o s as he ou e -
mos laye , con ex ual ac o s encompass bo h pe son/indi idual and linguis ic
ac o s, wi h he la e being posi ioned a he inne mos co e. These laye s in e -
ac o shape he co e ac i i ies in ol ed in he cogni i e p ocessing o linguis ic
ac o s. The model e ec i ely illus a es he complex in e play om b oad soci-
e al and ideological in luences o speci ic indi idual and cogni i e in e ac ions,
highligh ing he necessi y o conside ing a ange o ac o s om socie al ideolo-
gies o indi idual cogni i e p ocesses when in eg a ing MT in o L2 educa ion.
Building on he comp ehensi e amewo k p oposed by Jiang e al. (2024), we
can see how ideological ac o s o m he ou e mos laye in luencing MT use in
L2 educa ion. This ideological dimension is pa icula ly e iden in wo ecen
s udies ha highligh con as ing pe spec i es on MT use in academic se ings.
G ie e e al. (2024) conduc ed a quali a i e s udy explo ing he e hical iews
o nu sing and midwi e y s uden s on using AI machine ansla ion so wa e o
uni e si y assignmen s. Thei indings e eal a complex in e play o ac o s in lu-
encing s uden s’ e hical decision-making, including owne ship o ideas, ai ness
and espec , and pe sonal g ow h. Impo an ly, he s udy iden i ied a ension be-
ween de ici -o ien ed and anslanguaging pe spec i es among s uden s, wi h
237
A sushi Mizumo o
he la e e e ing o an app oach ha iews lea ne s’ use o mul iple languages
as a esou ce a he han a p oblem.
The de ici -o ien ed pe spec i e, o en aligned wi h adi ional language each-
ing me hodologies, pe cei es L1 o MT use as de imen al o L2 acquisi ion. This
pe spec i e ypically leads o policies discou aging o p ohibi ing MT use, iew-
ing i as an impedimen o au hen ic language lea ning. Wi h his pe spec i e,
i is belie ed ha ha MT use may hinde he de elopmen o c i ical hinking
skills in he a ge language and in e e e wi h imme si e language expe iences.
Con e sely, he anslanguaging iew adop s a mo e inclusi e app oach o lan-
guage lea ning. This pe spec i e alues lea ne s’ en i e linguis ic epe oi e as a
esou ce (Wei 2018). Ad oca es o anslanguaging a gue ha MT can be a alu-
able ool o accessing and le e aging lea ne s’ ull ange o linguis ic knowl-
edge, po en ially enhancing bo h language awa eness and lea ning ou comes.
This iew aligns wi h con empo a y unde s andings o bilingualism and mul ilin-
gualism, which concep ualise languages as pa o an in eg a ed communica ion
sys em a he han as sepa a e en i ies.
These con as ing pe spec i es on MT use in language educa ion a e u he
exempli ied in he app oaches educa o s and ins i u ions ake when add essing
s uden use o MT. Jolley and Maimone’s (2022) comp ehensi e e iew o h ee
decades o MT esea ch in language eaching and lea ning highligh s wo dis-
inc app oaches: he “MT as Chea ing” app oach, which leads o a De ec -Reac -
P e en Response, and he “MT as Resou ce” app oach, which encou ages an
In eg a e-Educa e-Model s a egy.
The “MT as Chea ing” pe spec i e, aligned wi h he de ici -o ien ed iew,
ea s MT use as a o m o academic dishones y. This app oach ocuses on s a e-
gies o de ec unau ho ised MT use, eac puni i ely, and p e en u u e occu -
ences. P oponen s o his iew ecommend implemen ing clea syllabus policies
agains MT use, designing assignmen s esis an o MT use, and educa ing s u-
den s abou he pi alls o elying on MT. This pe spec i e o en leads o policies
ha ban MT use ou igh , iewing i as incompa ible wi h language lea ning
goals.
In con as , he “MT as Resou ce” app oach, mo e closely aligned wi h he
anslanguaging iew, sees MT as a po en ial ool o language lea ning. This
pe spec i e ad oca es in eg a ing MT in o he cu iculum, educa ing s uden s on
i s app op ia e use, and modeling e ec i e s a egies o le e aging MT in lan-
guage lea ning. Resea che s like S aple on & Kin (2019) and Niño (2020) a gue
o accep ing he eali y o MT use and inding ways o inco po a e i meaning-
ully in o language educa ion. This app oach acknowledges he ubiqui y o MT
in mode n li e and seeks o p epa e s uden s o use i c i ically and e ec i ely.
238
12 Emb acing machine ansla ion in L2 educa ion:
The shi om he De ec -Reac -P e en mindse o he In eg a e-Educa e-
Model app oach e lec s a g owing ecogni ion o he ine i abili y o MT use in
language lea ning con ex s. As Duca & Schocke (2018) no e, he key ques ion is
no longe whe he eache s can p e en lea ne s om using MT, bu a he how
o help hem use i e hically and e ec i ely as pa o hei language lea ning
jou ney.
These con as ing app oaches o MT use in language educa ion exempli y he
b oade ideological ensions iden i ied in G ie e e al. (2024) and e lec he ou e -
mos laye o ideological ac o s in Jiang e al.’s (2024) amewo k. They demon-
s a e how deeply held belie s abou language acquisi ion and he ole o ech-
nology can shape educa ional policies, pedagogical p ac ices, and ul ima ely, s u-
den s’ engagemen wi h and pe cep ions o MT in hei L2 de elopmen p ocess.
This in e play be ween ideological s ances and p ac ical app oaches unde sco es
he complexi y o in eg a ing MT in o language educa ion and highligh s he
need o con ex -sensi i e s a egies ha conside bo h he po en ial bene i s and
challenges o MT use in L2 lea ning and eaching.
2 In eg a ing MT in o L2 educa ion: A new pa adigm
2.1 MT as augmen ed L2 compe ence
The anslanguaging pe spec i e and “MT as Resou ce” app oach, implemen ed
h ough he In eg a e-Educa e-Model s a egy, p o ide a heo e ical and p ac-
ical ounda ion o inco po a ing MT in o L2 educa ion. Building upon hese
concep s, we can u he concep ualise MT use in language lea ning h ough he
lens o “MT as Augmen ed L2 Compe ence.” This model o e s a isual ep esen-
a ion o how MT can enhance lea ne s’ language abili ies, pa icula ly in b idg-
ing he gap be ween ecep i e and p oduc i e skills. By iewing MT as a ool
o augmen ing compe ence a he han eplacing language lea ning, we align
wi h he anslanguaging idea o luid language p ac ices and he “MT as Re-
sou ce” app oach. The In eg a e-Educa e-Model s a egy can hen be applied o
help lea ne s e ec i ely u ilise MT o expand hei augmen ed compe ence zone,
while simul aneously de eloping hei own language skills. This in eg a ed pe -
spec i e no only jus i ies he use o MT in language lea ning bu also p o ides
a amewo k o unde s anding i s ole in enhancing o e all L2 p o iciency.
The concep o MT as augmen ed L2 compe ence is illus a ed in Figu e 1,
which p o ides a isual ep esen a ion o how MT, and also GenAI such as Cha -
GPT, can enhance language lea ne s’ abili ies.
239
A sushi Mizumo o
Augmen ed compe ence
wi h MT (AI)
P oduc i e compe ence
Recep i e compe ence
Figu e 1: The concep o MT as augmen ed L2 compe ence
The igu e demons a es he ela ionship be ween ecep i e compe ence, p o-
duc i e compe ence, and he po en ial o augmen ed compe ence h ough MT
use. He e is a b eakdown o he key elemen s:
• Recep i e Compe ence: This is ep esen ed by he la ge , ou e o al. I
e e s o he abili y o unde s and he a ge language (L2), which is ypi-
cally mo e de eloped han p oduc i e skills. Fo mos L2 English language
lea ne s, hei capaci y o comp ehend English exceeds hei abili y o p o-
duce i .
• P oduc i e Compe ence: Shown as he smalle , inne o al, his ep esen s
he lea ne ’s abili y o ac i ely use he language. I is gene ally mo e lim-
i ed han ecep i e compe ence, which aligns wi h heo ies like Swain’s
ou pu hypo hesis (1985), emphasising he impo ance o language p oduc-
ion in second language acquisi ion.
• Augmen ed Compe ence wi h MT (AI): This is depic ed by he da k g ay
a ea ex ending beyond he p oduc i e compe ence o al. I illus a es how
MT can b idge he gap be ween wha lea ne s can ecognise as co ec
( ecep i e knowledge) and wha hey can p oduce on hei own.
Figu e 1 sugges s ha MT can se e as a ool o augmen lea ne s’ compe ence,
pa icula ly in a eas whe e hey can ecognise co ec ness by sigh bu s uggle
o p oduce i accu a ely. This augmen a ion is especially bene icial o mo e p o-
icien lea ne s, as suppo ed by p e ious s udies (Klimo a e al. 2022, Ohashi
240
12 Emb acing machine ansla ion in L2 educa ion:
2024). Highe p o iciency lea ne s end o ha e a la ge gap be ween hei ecep-
i e and p oduc i e skills, p o iding mo e oom o MT o assis in b idging his
di ide.
Impo an ly, his model unde sco es ha he e emains a s ong a ionale o
s udying English (o any L2). The augmen ed compe ence p o ided by MT is
buil upon he ounda ion o he lea ne ’s own language skills. Wi hou de el-
oping one’s own ecep i e and p oduc i e compe encies, he bene i s o MT aug-
men a ion would be limi ed. Fu he mo e, as lea ne s’ p o iciency inc eases, hey
become be e equipped o e ec i ely u ilise MT, maximising i s po en ial as a
lea ning ool.
This concep ualisa ion o MT as augmen ed L2 compe ence aligns wi h he
indings om sys ema ic e iews (Jolley & Maimone 2022, Lee 2023) ha high-
ligh MT’s e ec i eness when used app op ia ely, pa icula ly o mo e ad-
anced lea ne s. I also suppo s he need o p ope guidance and aining in
MT use, as he ool’s e ec i eness is con ingen upon he lea ne ’s abili y o
c i ically e alua e and apply i s ou pu .
In sum, he model p esen ed he e p o ides a amewo k o unde s anding
how MT can be in eg a ed in o language lea ning p ocesses. I emphasises ha
MT is no a eplacemen o language s udy, bu a he a ool ha can en-
hance and ex end lea ne s’ exis ing compe encies, po en ially accele a ing hei
p og ess owa ds highe le els o language p o iciency.
2.2 MT ins uc ion o L2 lea ning
Niño (2009) p oposed ou models o MT use in L2 educa ion: a “bad model”, a
“good model”, oca ional applica ions (pa icula ly in ansla ion- ela ed ields),
and as a compu e -assis ed language lea ning (CALL) ool. Ini ially, MT was em-
ployed as a “bad model”, whe e s uden s iden i ied and co ec ed e o s h ough
pos -edi ing, a p ocess necessi a ed by he limi ed accu acy o ea ly sys ems. In
con as , he “good model” in ol ed using MT ou pu s as exempla s o s uden s.
These models, e lec ing he e olu ion o MT echnology and i s pedagogical ap-
plica ions, illus a e a signi ican shi in ocus. As MT echnology has ad anced,
i s p ima y use has ansi ioned o se ing as a CALL ool, whe e i acili a es
s uden engagemen in sol ing language p oblems independen ly, as e idenced
in ecen s udies (Lee 2020, S aple on & Kin 2019, Tsai 2019).
Lee (2023) ecommends ha eache s should p o ide guidelines o using MT
and explici ly each e ec i e s a egies o s uden s p io o using i , which leads
o enhancing s uden pe o mance, as suppo ed by O’Neill (2016).
241
A sushi Mizumo o
Ryu, Jieun, Young Ae Kim, Seojin Pa k, Seungmin Eum, Sojung Chun & Sun-
young Yang. 2022. Explo ing o eign language s uden s’ pe cep ions o he
guided use o machine ansla ion (GUMT) model o Ko ean w i ing. L2 Jou -
nal 14(1). 136–165. DOI: 10.5070/L214151759.
S aple on, Paul & Becky Leung Ka Kin. 2019. Assessing he accu acy and eache s’
imp essions o Google T ansla e: A s udy o p ima y L2 w i e s in Hong Kong.
English o Speci ic Pu poses 56. 18–34. DOI: 10.1016/j.esp.2019.07.001.
Swain, Me ill. 1985. Communica i e compe ence: Some oles o comp ehensible
inpu and comp ehensible ou pu in i s de elopmen . In Susan Gass & Ca olyn
Madden (eds.), Inpu in second language acquisi ion, 235–253. Newbu y House.
Tsai, Shu-Chiao. 2019. Using Google T ansla e in EFL d a s: A p elimina y in es-
iga ion. Compu e Assis ed Language Lea ning 32(5-6). 510–526. DOI: 10.1080/
09588221.2018.1527361.
Wei, Li. 2018. T anslanguaging as a p ac ical heo y o language. Applied Linguis-
ics 39(1). 9–30. DOI: 10.1093/applin/amx039.
248
