METHODOLOGICAL FOUNDATIONS OF USING ARTIFICIAL INTELLIGENCE IN TEACHING ENGLISH AND ITS ROLE IN DEVELOPING COMMUNICATIVE COMPETENCE

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METHODOLOGICAL FOUNDATIONS OF USING ARTIFICIAL
INTELLIGENCE IN TEACHING ENGLISH AND ITS ROLE IN
DEVELOPING COMMUNICATIVE COMPETENCE
Ma una Shakh iyo o a Shamsiddin qizi,
SamDCHTI Ingliz ili akul e i Xo ijiy il a adabiyo i yo’nalishi
2- bosqich 22-03 gu uh magis an i
shax iyo o ama [email protected]
+998886751295
Abs ac
This pape in es iga es he me hodological ounda ions o using A i icial In elligence
(AI) in English Language Teaching (ELT) and examines how such pedagogical
in e en ions con ibu e o he de elopmen o lea ne s’ communica i e compe ence.
D awing on empi ical s udies, sys ema ic e iews, and eache pe spec i es, he s udy
iden i ies bes p ac ices, pedagogical a o dances, and challenges, and epo s on he
e ec s o AI‐media ed lea ning on speaking abili y, engagemen , lea ne con idence,
and me hodological inno a ion. The implica ions sugges ha app op ia ely designed
AI ools, in eg a ed wi h communica i e me hodology, can signi ican ly enhance
communica i e compe ence, p o ided ha eache s a e suppo ed, asks a e eal‐wo ld
o ien ed, and assessmen p ac ices adap .
Key wo ds: a i icial in elligence (ai), english language eaching (el ), communica i e
compe ence, language lea ning echnology, ai-assis ed ins uc ion, adap i e lea ning,
cha bo s in educa ion, pedagogical me hodology, eache aining, lea ne au onomy
In oduc ion
In ecen yea s, A i icial In elligence (AI) has inc easingly pene a ed educa ional
se ings, p omising inno a ions in adap i e lea ning, cha bo s, au oma ed eedback,
and pe sonalized ins uc ion. In he domain o English Language Teaching (ELT),
esea che s ha e begun o explo e how AI‐suppo ed ools may acili a e
communica i e compe ence — ha is, he abili y o lea ne s no jus o know g amma
and ocabula y, bu o use language e ec i ely in au hen ic communica i e con ex s.
Ye , despi e g owing in e es , he e emain gaps in unde s anding he me hodological
ounda ions o in eg a ing AI in a way ha uly suppo s communica i e lea ning —
which me hods wo k, unde wha condi ions, and wha challenges mus be o e come.
Communica i e compe ence, o iginally heo ized by Hymes and elabo a ed by o he s,
includes g amma ical compe ence, sociolinguis ic compe ence, discou se compe ence,
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and s a egic compe ence. T adi ional communica i e language eaching app oaches
ha e emphasized in e ac ion, meaning nego ia ion, ask‐based lea ning, and au hen ic
inpu /ou pu [1]. The in eg a ion o AI aises ques ions: how do AI ools align wi h
communica i e me hodology? Wha design ea u es and pedagogical p ac ices a e
mos e ec i e? Wha is he empi ical e idence ha AI use leads o gains in
communica i e compe ence (especially speaking, in e ac ion, luency)? And wha
cons ain s ( eache belie s, in as uc u e, assessmen ) limi o mode a e hese e ec s?
This s udy aims o syn hesize ecen esea ch o add ess hese ques ions, d awing om
empi ical s udies, sys ema ic e iews, and eache epo s be ween 2023–2025 [2].
Speci ically, he esea ch ques ions a e:
1. Wha me hodological ounda ions (design, asks, eedback, in e ac ion) a e
epo ed o AI‐media ed ELT aimed a communica i e compe ence?
2. Wha is he e idence o AI’s impac on dimensions o communica i e
compe ence (especially speaking, engagemen , con idence)?
3. Wha challenges and cons ain s a e iden i ied, and wha implica ions do hey
ha e o me hodological p ac ice?
Me hods
Li e a u e Selec ion
A sys ema ic li e a u e sea ch was conduc ed ac oss academic da abases (e.g. Scopus,
Web o Science, ERIC, Google Schola ) o a icles published be ween 2023 and 2025
using keywo ds such as “AI in ELT”, “a i icial in elligence in English language
eaching communica i e compe ence”, “cha bo s o speaking p ac ice”, “adap i e
lea ning in language educa ion” [3]. F om an ini ial pool o ~120 a icles, inclusion
c i e ia we e:
 Empi ical s udies, sys ema ic e iews, concep ual/ heo e ical pape s explici ly
ouching on communica i e compe ence.
 Use o AI ools o sys ems (cha bo s, adap i e lea ning sys ems, au oma ed
eedback) in English language eaching con ex s.
 Repo ing o communica i e ou comes: speaking, in e ac ion, luency, lea ne
con idence, engagemen .
 Open access o accessible summa y o me hods and esul s.
A e sc eening i les, abs ac s, and ull ex s, 10 a icles we e chosen as mos
ele an . These include mixed‐me hods s udies, sys ema ic e iews, and eache
pe spec i e phenomenological s udies [4].
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Da a Ex ac ion and Analysis
F om each selec ed a icle, he ollowing da a we e ex ac ed:
 Con ex : age/le el o lea ne s, coun y, class size, du a ion.
 Type o AI ool (cha bo , adap i e sys em, eedback sys em, e c.).
 Pedagogical design: ask ypes, in e ac ion pa e ns, eedback ypes, eache
in ol emen .
 Measu ed ou comes ele an o communica i e compe ence: speaking luency,
in e ac ional compe ence, con idence, mo i a ion, e c.
 Repo ed challenges o cons ain s (e.g. in as uc u e, eache aining,
assessmen alignmen ).
Da a syn hesis ollowed a hema ic analysis app oach: coding o me hodological
ea u es, ou comes, challenges, hen g ouping indings in o cohe en hemes.
Resul s
Me hodological Founda ions: Design Fea u es and Pedagogical P ac ices
F om he li e a u e:
 Au hen ic, in e ac ional asks: S udies (e.g. “A sys ema ic e iew o AI-
powe ed cha bo s…”; “Design language lea ning wi h a i icial in elligence
(AI)” ) show ha AI ools pe o m bes when asks mimic eal communica ion
— ole-plays, dialogues wi h cha bo s, simula ed con e sa ions [5].
 Adap i e eedback and sca olding: Tools ha o e immedia e co ec i e
eedback, p onuncia ion p ac ice, and adap i e di icul y a e mo e e ec i e. The
mixed-me hods s udy by Wei e al. (2023) epo ed ha AI sys ems which adap
o lea ne e o s lead o g ea e gains in speaking accu acy.
 Lea ne au onomy/sel - egula ed lea ning: AI ools ha allow lea ne s o
p oceed a hei own pace, ack hei p og ess, and e lec on eedback
a o ably a ec mo i a ion and con idence. The s udy by Qiao e al. (2023)
ound inc eased sel - egula ion in classes using AI-based modules.
 Teache acili a ion and in eg a ion: AI is no s andalone; eache guidance,
ask design, and sca olding a e c i ical. Baha i (2025) emphasized in eg a ing
AI-assis ed lea ning wi h eache -led communica i e asks.
E idence o Impac on Communica i e Compe ence
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 Speaking luency / in e ac ion: Se e al s udies epo s a is ically signi ican
imp o emen s in lea ne s’ speaking luency when using AI-cha bo s o adap i e
speaking modules. Fo example, Du e al. (2024) showed lea ne s engaging wi h
cha bo s had highe sco es in o al es s and be e in e ac ional compe ence
du ing class oom asks [6].
 Lea ne con idence and engagemen : Ac oss mul iple s udies (e.g. Xiao an,
2025; Qiao e al., 2023), lea ne s epo ed inc eased con idence speaking in
English, less anxie y, mo e willingness o ini ia e speaking. Engagemen was
also highe due o no el y and adap i eness o AI ools.
 Mo i a ion and a ec : AI ools p o ided mo i a ional a o dances: immedia e
eedback, gami ied elemen s, digi al in e ac i i y. Wei e al. (2023) documen ed
highe mo i a ion sco es in expe imen al g oups using AI han con ol g oups.
 Limi a ions in ull communica i e compe ence gains: Some s udies no e ha
while luency and con idence imp o e, gains in sociolinguis ic compe ence
(app op iacy, p agma ics) and discou se compe ence a e less ma ked, likely
because AI ools o en lack cul u al/con ex ual nuance [7.
Discussion
In e p e a ion o Findings
The e idence indica es ha AI has s ong po en ial o enhance componen s o
communica i e compe ence — especially hose ela ed o luency, in e ac ion, lea ne
con idence, and engagemen — p o ided me hodological p ac ices a e well designed.
Key ounda ions include au hen ic communica i e asks, adap i e eedback, lea ne
au onomy, and eache in eg a ion.
Howe e , communica i e compe ence is mul i ace ed. While AI ools suppo
g amma ical, luency, and in e ac ional dimensions, hey a e less de eloped in
suppo ing sociolinguis ic no ms, p agma ic a ia ion, cul u al con ex , and discou se
complexi y. These equi e nuanced human media ion, cul u al inpu , and possibly
ad anced AI ha models p agma ics and cul u e mo e deeply.
Me hodological Implica ions
 Task design: Mus include au hen ic communica i e asks ha equi e
nego ia ion, spon aneous esponses, eal o simula ed in e locu o s.
 Feedback and adap i i y: Immedia e, speci ic eedback (p onuncia ion,
luency, e o co ec ion) is aluable. Sys ems should adjus di icul y o lea ne
pe o mance o main ain challenge wi hou us a ion [8].
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 Teache ole: Teache s mus be in ol ed as designe s, acili a o s, assesso s
a he han passi e o e see s. Teache aining p og ams should include
ins uc ion in how o selec , in eg a e, and adap AI ools [9].
 Assessmen e o m: To align wi h communica i e compe ence goals,
assessmen me hods need o inco po a e speaking, in e ac ion, p agma ics and
mus alue luency and nego ia ion o meaning, no jus accu acy.
Limi a ions
 The e iewed s udies a e ela i ely ecen and many a e small-scale; long- e m
e ec s on communica i e compe ence (o e semes e s/yea s) a e less well
documen ed.
 Many s udies ely on sel - epo da a (mo i a ion, con idence), which may be
subjec o bias [10].
 Con ex s a y widely (coun ies, lea ne le els, access o echnology), so
gene aliza ion o speci ic se ings (e.g., low- esou ce con ex s) should be
cau ious.
Conclusion
The in eg a ion o a i icial in elligence in o English language eaching ep esen s no
me ely a echnological shi bu a pedagogical ans o ma ion. The analysis o ecen
s udies demons a es ha AI ools — om adap i e lea ning sys ems o con e sa ional
cha bo s — can e ec i ely ein o ce lea ne s’ communica i e compe ence by c ea ing
in e ac i e, eedback- ich, and lea ne -cen e ed en i onmen s. These echnologies
ex end he bounda ies o he adi ional class oom, allowing o indi idualized
p ac ice, eal- ime co ec ion, and con ex ualized communica ion beyond he limi s o
ime and place.
Howe e , he e ec i eness o AI in os e ing communica i e compe ence is con ingen
upon he me hodological amewo k in which i ope a es. The success o AI-assis ed
ins uc ion depends no only on echnological sophis ica ion bu also on pedagogical
cohe ence — how eache s in eg a e AI asks in o communica i e app oaches, how
eedback is con ex ualized, and how lea ne s a e guided o use AI as a ool o
au hen ic exp ession a he han mechanical epe i ion. The e o e, AI should no
eplace he human elemen in language eaching bu complemen i , ampli ying
eache s’ abili y o acili a e meaning ul in e ac ion and in e cul u al unde s anding.
Mo ing o wa d, educa ional ins i u ions mus iew AI as a s a egic pa ne in
language educa ion — one ha can suppo di e en ia ed ins uc ion, inclusi i y, and
lea ne au onomy. A he same ime, sys ema ic eache aining, con inuous e alua ion

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o AI’s linguis ic and e hical dimensions, and he edesign o assessmen sys ems
emain essen ial p e equisi es o sus ainable in eg a ion. In essence, he u u e o
communica i e compe ence de elopmen lies no in echnology alone, bu in he
ha mony be ween in elligen ools, in o med eache s, and mo i a ed lea ne s.
Re e ences:
1. Baha i, A. (2025). In eg a ing CALL and AIALL o an in e ac i e pedagogical
model. Sp inge .
2. C omp on, H. (2024). AI and English language eaching: A o dances and
challenges. B i ish Jou nal o Educa ional Technology, 55(2), 245–260.
3. Du, J., Zhang, Y., & Li, W. (2024). A sys ema ic e iew o AI-powe ed cha bo s
o English as a o eign language lea ning. Compu e s & Educa ion, 210,
104768.
4. Li, Y. (2025). Designing language lea ning wi h a i icial in elligence (AI).
Sma Lea ning En i onmen s, 12(1), 33–49.
5. Madinabonu, J. (2024). Me hodological ounda ions o using a i icial
in elligence in English lea ning. Wes e n Eu opean S udies Jou nal, 8(4), 57–
65.
6. Qiao, H., Chen, L., & Sun, J. (2023). A i icial in elligence-based language
lea ning: E ec s on s uden s’ speaking skills and mo i a ion. Educa ion and
In o ma ion Technologies, 28(5), 6789–6804.
7. Wei, L., Zhao, M., & Xu, H. (2023). A i icial in elligence in language
ins uc ion: Impac on lea ne s’ achie emen and mo i a ion. F on ie s in
Psychology, 14, 1196721.
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lea ning: A phenomenological app oach. Con empo a y Educa ional
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9. Xi, X. (2024). Re isi ing communica i e compe ence in he age o a i icial
in elligence. Annual Re iew o Applied Linguis ics, 44, 118–135.
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