How AI‐Enhanced Social–Emotional Learning Framework Transforms EFL Students' Engagement and Emotional Well‐Being
European Journal of Education,
Год журнала:
2025,
Номер
60(1)
Опубликована: Янв. 12, 2025
ABSTRACT
This
study
explores
the
transformative
role
of
AI‐enhanced
social–emotional
learning
(SEL)
frameworks
in
improving
engagement
and
emotional
well‐being
English
as
a
foreign
language
(EFL)
students
China.
A
survey
was
conducted
among
816
undergraduate
postgraduate
from
universities
across
five
provinces,
utilising
convenience
sampling.
The
research
focused
on
how
AI
tools
integrated
into
contribute
to
student
stability.
Data
were
analysed
using
SPSS
for
descriptive
regression
analyses
AMOS
structural
equation
modelling.
findings
highlight
that
SEL
significantly
boosts
well‐being.
By
providing
tailored
experiences
based
students'
cognitive
needs,
systems
facilitate
better
regulation,
increased
focus
improved
academic
performance.
results
suggest
offer
personalised
support
not
only
enhances
outcomes
but
also
creates
more
emotionally
supportive
environment,
contributing
overall
success
Язык: Английский
A latent growth curve modeling of Chinese EFL learners’ emotional fluctuations in AI-mediated L2 education: is positivity or negativity on the rise?
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.
Язык: Английский
Developing and Validating a Scale of Artificial Intelligence Anxiety Among Chinese EFL Teachers
European Journal of Education,
Год журнала:
2025,
Номер
60(1)
Опубликована: Янв. 7, 2025
ABSTRACT
As
artificial
intelligence
(AI)
technology
continues
to
advance,
its
influences
across
various
industries
have
grown,
leading
increasing
levels
of
anxiety,
including
that
in
education.
Nonetheless,
terms
current
knowledge,
the
literature
lacks
a
valid
scale
measure
AI
anxiety
among
EFL
teachers,
particularly
university
teachers.
Moreover,
underlying
dimensions
this
construct
yet
be
clarified.
Against
these
gaps,
study
aims
develop
and
validate
assess
teachers
China.
We
used
qualitative
interviews
quantitative
surveys
combined
identify
key
In
so
doing,
251
Chinese
completed
newly
designed
scale.
The
result
exploratory
factor
analyses
indicated
five
21
items
questionnaire.
Five
were
identified:
technical
proficiency,
job
displacement,
technological
support,
student
experience
research
development.
Next,
another
415
participated
validating
confirmatory
analysis
demonstrated
strong
reliability,
validity
an
acceptable
model
fit.
This
new
provides
useful
tool
for
assessing
highlights
unique
challenges
they
face
adapting
AI,
offering
basis
future
targeted
support.
Язык: Английский
Primary School Students' Perceptions of Artificial Intelligence: Metaphor and Drawing Analysis
European Journal of Education,
Год журнала:
2025,
Номер
60(1)
Опубликована: Янв. 27, 2025
ABSTRACT
Due
to
the
frequent
use
of
artificial
intelligence
(AI)
technologies
in
daily
life,
it
is
thought
that
primary
school
students
acquire
information
about
this
concept
from
various
sources.
The
way
these
sources
present
AI
may
affect
students'
perceptions
AI.
In
study,
was
aimed
examine
third
and
fourth
grade
through
metaphors
drawings.
This
research,
which
conducted
with
participation
262
students,
phenomenological
design.
When
participants
were
analysed,
determined
they
produced
100
metaphors,
evaluated
17
categories
as
humanistic
feature,
source,
danger,
development,
superhuman
service,
source
happiness,
productivity,
orientation,
commitment,
pervasiveness,
necessity,
security,
speed,
difficulty,
virtual
environment
uncertainty.
Accordingly,
many
different
perspectives
most
danger.
It
human,
brain
living
prominent
human
characteristic
category;
teacher,
wise
book
finally,
enemy,
weapon
monster
danger
category.
drawing
findings
37
codes
represented
four
categories:
purpose,
object,
interaction
environment.
purpose
category,
information,
happiness;
object
mostly
humanoid
robot;
emphasising
interaction;
not
specified.
line
obtained,
literature
discussions
made
suggestions
made.
Язык: Английский
A Comparison of Human‐Written Versus AI‐Generated Text in Discussions at Educational Settings: Investigating Features for ChatGPT, Gemini and BingAI
European Journal of Education,
Год журнала:
2025,
Номер
60(1)
Опубликована: Янв. 31, 2025
ABSTRACT
Generative
artificial
intelligence
(GenAI)
models,
such
as
ChatGPT,
Gemini,
and
BingAI,
have
become
integral
to
educational
sciences,
bringing
about
significant
transformations
in
the
education
system
processes
of
knowledge
production.
These
advancements
facilitated
new
methods
teaching,
learning,
information
dissemination.
However,
widespread
adoption
these
technologies
raises
serious
concerns
academic
ethics,
content
authenticity,
potential
for
misuse
settings.
This
study
aims
evaluate
linguistic
features
differences
between
AI‐generated
human‐generated
articles
contexts.
By
analysing
various
attributes
singular
word
usage,
sentence
lengths,
presence
repetitive
or
stereotypical
phrases,
identifies
key
distinctions
two
types
content.
The
findings
indicate
that
exhibit
higher
average
usage
longer
lengths
compared
articles,
suggesting
a
more
complex
nuanced
language
structure
human
writing.
Furthermore,
employs
ensemble
learning
including
Random
Forest,
Gradient
Boosting,
AdaBoost,
Bagging,
Extra
Trees,
classify
distinguish
texts.
Among
these,
Trees
model
achieved
highest
classification
accuracy
93%,
highlighting
its
effectiveness
identifying
Additionally,
experiments
using
BERTurk
model,
transformer‐based
demonstrated
95%,
particularly
distinguishing
from
those
produced
by
Gemini.
results
this
implications
future
education,
they
underscore
critical
need
robust
tools
methodologies
differentiate
Язык: Английский
Emotion-related theories in classroom language learning: the conceptualization and causation of emotions
Frontiers in Psychology,
Год журнала:
2025,
Номер
16
Опубликована: Фев. 26, 2025
Language
classrooms
are
embedded
with
a
wide
range
of
emotions.
Emotions
play
significant
role
in
affecting
learners'
language
learning
and
academic
performance.
Yet,
while
the
emotions
L2
has
been
recognized,
very
scant
studies
have
investigated
underlying
theoretical
frameworks
great
depth
regard
to
conceptualization
causation
Moreover,
few
review
paid
sufficient
attention
antecedents
or
causes
underpinned
by
certain
theories
field
SLA.
Therefore,
offer
complementary
emotion-related
provide
fresh
insights
into
emotional
research
SLA,
present
study
first
explains
approaches
emotions,
elucidates
how
these
applied
learning,
identifies
effect
interplay
between
cognitive,
psychological,
social,
contextual
factors
on
development
learning.
Finally,
practical
implications,
like
regulation
strategies
for
both
teachers
learners
future
directions,
integration
AI
tools
researchers,
teachers,
teacher
educators
who
interested
also
discussed.
Язык: Английский