European Journal of Education,
Год журнала:
2025,
Номер
60(1)
Опубликована: Янв. 31, 2025
ABSTRACT
Artificial
intelligence
(AI)
is
increasingly
exploited
to
promote
student
engagement.
This
study
combined
topic
modelling,
keyword
analysis,
trend
test
and
systematic
analysis
methodologies
analyse
AI‐supported
engagement
(AIsE)
studies
regarding
research
keywords
topics,
AI
roles,
systems
algorithms,
methods
domains,
samples
outcomes.
Findings
included
the
following:
(1)
frequent‐used
emerging
comprised
‘machine
learning’,
‘artificial
chatbot’
‘collaborative
knowledge
building’.
(2)
Frequently
studied
topics
‘AI
for
MOOCs
self‐regulated
learning’
‘affective
computing
emotional
engagement’.
(3)
Most
adopted
intelligent
tutoring
systems,
traditional
machine
learning
natural
language
processing.
(4)
Emotional
affective
or
psychological
states
among
college
students
received
most
attention.
(5)
quantitative
approaches
concerned
computer
science
education.
Accordingly,
we
highlighted
AI's
roles
as
tutors,
advisors,
partners,
tutees
regulators
behavioural,
cognitive
inspire
effective
integration
into
European Journal of Education,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 13, 2024
ABSTRACT
The
rise
of
artificial
intelligence
(AI)
has
significantly
impacted
education,
yet
few
scholars
have
explored
AI‐assisted
classrooms,
particularly
in
language
education
China.
Understanding
the
roles
classroom
climate,
AI
literacy,
and
resilience
is
essential,
as
these
factors
foster
positive
learning
environments
enhance
student
engagement.
In
this
sense,
study,
grounded
Social
Cognitive
Theory,
employs
structural
equation
modelling
to
investigate
influencing
engagement
Chinese
English
a
Foreign
Language
(EFL)
classrooms.
It
examines
data
from
606
university
EFL
learners
explore
interactions
among
variables
mediating
role
resilience.
findings
indicate
that
all
predict
engagement,
highlighting
importance
both
environmental
cognitive
fostering
active
participation.
Furthermore,
serves
crucial
mediator,
linking
climate
literacy
This
study
provides
some
insights
for
educators
policymakers,
emphasising
need
cultivate
supportive
environments,
promote
programs,
strengthen
students'
optimise
educational
settings.
Acta Psychologica,
Год журнала:
2024,
Номер
249, С. 104442 - 104442
Опубликована: Авг. 6, 2024
Prior
research
highlights
the
critical
role
of
AI
in
enhancing
second
language
(L2)
learning.
However,
factors
that
practically
affect
L2
learners
to
engage
with
resources
are
still
underexplored.
Given
widespread
availability
digital
devices
among
college
students,
they
particularly
poised
benefit
from
AI-assisted
As
such,
this
study,
grounded
an
extended
Technology
Acceptance
Model
(TAM),
investigates
predictors
learners'
actual
use
tools,
focusing
on
self-efficacy,
AI-related
anxiety,
and
their
overall
attitude
toward
AI.
Data
was
gathered
429
at
Chinese
universities
via
online
questionnaire,
utilizing
four
established
scales.
Through
structural
equation
modeling
(SEM)
AMOS
24,
results
indicate
self-efficacy
could
negatively
positively
influence
both
tools.
Besides,
anxiety
predicted
Moreover,
a
positive
predictor
through
reducing
AI,
or
combination
both.
This
study
also
discusses
theoretical
pedagogical
implications
suggests
directions
for
future
research.
Innovation in Language Learning and Teaching,
Год журнала:
2025,
Номер
unknown, С. 1 - 14
Опубликована: Янв. 1, 2025
The
role
of
artificial
intelligence
(AI)
tools
in
promoting
different
aspects
second
language
(L2)
education
has
recently
obtained
increasing
attention.
However,
there
is
insufficient
evidence
about
the
contribution
AI-mediated
L2
instruction
to
English
as
a
foreign
(EFL)
learners'
positive
and
negative
emotions.
To
address
gap,
this
study
conducted
latent
growth
curve
modeling
(LGCM)
analysis
find
out
changes
350
Chinese
EFL
classroom
engagement
enjoyment.
Two
questionnaires
were
used
collect
data
at
points
semester
that
was
taught
through
AI
tools.
results
showed
both
enjoyment
significantly
increased
learners
over
time.
While
grew
steadily
participants,
rate
not
equal
among
them.
Furthermore,
it
found
student
had
going-togetherness
time,
from
beginning
end
course.
are
discussed
implications
for
adoption
classes
provided
teachers
teacher
educators.
European Journal of Education,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 8, 2024
ABSTRACT
Artificial
intelligence
(AI)
is
transforming
L2
education,
yet
its
specific
impacts
on
English
as
a
foreign
language
(EFL)
teachers
and
learners'
engagement
remain
understudied.
To
address
this
deficiency,
study,
grounded
in
Fredricks,
Blumenfeld,
Paris's
(
Review
of
Educational
Research
,
74
109)
three‐dimensional
model,
explored
the
AI
behavioural,
cognitive
emotional
EFL
learners
through
semi‐structured
interviews
with
24
38
college
learners,
followed
by
thematic
analysis
MAXQDA
to
uncover
effectiveness
AI.
The
study
found
that
behavioural
showcased
integration
tools,
highlighting
increased
frequency
use
their
practical
applications
enhancing
acquisition
tasks.
Cognitive
was
marked
recognition
capacity
augment
teaching
strategies
learning
processes,
although
it
also
surfaced
concerns
about
potential
overreliance
technology.
Emotional
reflected
complex
interplay
attitudes,
most
informants
viewing
positively
but
acknowledging
job
displacement,
emotions
students
well
relations
between
them.
concluded
while
held
promise
for
must
consider
limitations
ethical
implications.
research
provided
valuable
insights
educators,
technology
developers
policymakers,
encouraging
innovative
practices
informed
decision‐making
education.
International Journal of Applied Linguistics,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 24, 2024
Abstract
Teachers
play
a
critical
role
in
fostering
students’
creativity,
especially
English
as
foreign
language
(EFL)
classes,
known
teaching
for
creativity
(TfC).
Despite
this,
no
comprehensive
study
has
explored
how
the
work
environment
influences
EFL
teachers’
TfC.
Therefore,
this
investigates
various
holistic
factors
affect
TfC
among
Chinese
teachers.
Drawing
on
dynamic
componential
model
of
employs
mixed‐methods
approach,
combining
quantitative
data
from
survey
406
teachers
analyzed
using
partial
least
squares
structural
equation
modeling
Smart
PLS
3,
and
qualitative
insights
semi‐structured
interviews
with
20
MAXQDA
2022.
The
results
reveal
that
perceived
climate
peer
group
interaction
positively
significantly
impact
TfC,
whereas
supervisory
relationship
(SR)
does
not
show
significant
effects.
findings
validate
these
outcomes
offer
deeper
into
PC
PGI
specifically
facilitate
or
impede
alongside
explanations
non‐significant
SR.
Additionally,
analysis
identifies
another
influential
factor
TfC:
teacher–student
interaction.
These
carry
theoretical
practical
implications
teacher
educators
professional
development
European Journal of Education,
Год журнала:
2024,
Номер
59(4)
Опубликована: Сен. 25, 2024
ABSTRACT
The
COVID‐19
pandemic
has
significantly
altered
teaching
methodologies
by
integrating
technology
into
syllabi,
emphasising
the
crucial
role
of
teacher
well‐being
influenced
positive
psychology.
Also,
as
foremost
issues
education,
teachers’
individual
factors
should
be
considered
their
beliefs
in
capabilities
to
persist
case
difficulties
and
emotion
regulation
(ER)
have
been
underlined
literature.
Therefore,
this
study
examined
correlation
between
self‐efficacy
(TSE),
resilience
ER
among
424
Chinese
teachers.
findings
through
running
structural
equation
model
revealed
that
those
teachers
with
a
heightened
degree
TSE,
are
more
likely
better
well‐being.
Multiple
regression
analysis
indicated
TSE
explained
61%
variance
Meanwhile,
same
found
54%
51%
well‐being,
respectively.
Succinctly,
some
educational
implications
provided
for
members
attract
attention
these
constructs
technology‐enhanced
teaching.
European Journal of Education,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 31, 2024
ABSTRACT
The
use
of
artificial
intelligence
(AI)
technologies
in
second/foreign
language
education
has
recently
gained
a
bulk
attention.
However,
the
emotional
experiences
English
as
foreign
(EFL)
learners
AI‐mediated
classes
have
been
ignored.
To
fill
this
gap,
present
qualitative
study
examined
34
Chinese
EFL
students'
perceptions
AI‐induced
emotions
and
regulation
strategies.
A
semi‐structured
interview
narrative
frame
were
used
to
collect
data.
gathered
data
thematically
analysed
through
latest
version
MAXQDA
software
(v.
2023).
findings
revealed
that
students
had
mostly
experienced
positive
‘motivation’,
‘excitement’,
‘engagement’
‘confidence’.
On
negative
side,
they
reported
experiencing
‘frustration’,
‘anxiety’
‘stress’
more
frequently
their
classes.
Furthermore,
indicated
participants
six
strategies,
namely
‘seeking
help
from
others’,
‘shifting
attention’,
‘cognitive
change’,
‘persistent
practice’,
‘staying
positive’
‘suppression’
regulate
emotions.
are
discussed
implications
provided
for
educators
understand
aspect
AI
injection
into
L2
education.
