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
Integrating AI-Driven Emotional Intelligence in Language Learning Platforms to Improve English Speaking Skills through Real-Time Adaptive Feedback
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 31, 2025
Abstract
This
groundbreaking
study
introduces
the
first-ever
integration
of
emotional
intelligence
(EI)
with
artificial
in
English-speaking
instruction
through
an
emotionally
adaptive
language
learning
system.
Through
a
mixed-method
design,
research
examined
this
innovative
approach’s
impact
on
speaking
proficiency
among
40
high
school
students
(aged
15-18)
from
Varamin
County,
Iran.
The
experimental
group
(n=20)
engaged
novel
“Amazon
Alexa-Speak”
Speaking
Assessment
System,
featuring
AI-driven
EI-based
real-time
feedback;
contrast,
control
received
conventional
over
six
sessions
following
pretest
to
ensure
homogeneity.
employed
concurrent
mixed
method
collecting
quantitative
data
System
and
researcher-made
perception
questionnaire;
qualitative
came
classroom
observation
checklists
semi-structured
interviews
(n=20),
focusing
state
monitoring
anxiety
reduction
patterns.
Statistical
analyses
revealed
significant
positive
correlation
between
EI
performance
(p
<
0.05,
η2
=
0.42),
showing
substantially
enhanced
(F(1,38)
24.63,
p
0.05).
system’s
detection
algorithm
demonstrated
94%
accuracy
identifying
responding
learners’
affective
states.
presents
paradigm
shift
education
technology
by
introducing
first
system
that
simultaneously
addresses
cognitive
aspects
acquisition.
findings
have
implications
for
global
market,
particularly
addressing
barriers
learning.
technology’s
scalability
cross-cultural
applicability
make
it
potentially
transformative
solution
worldwide,
opening
new
avenues
intelligent
educational
development.
Язык: Английский
Artificial Intelligence‐Supported Student Engagement Research: Text Mining and Systematic Analysis
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
Язык: Английский
A collaborative reflection on the synergy of Artificial Intelligence (AI) and language teacher identity reconstruction
Teaching and Teacher Education,
Год журнала:
2025,
Номер
160, С. 105022 - 105022
Опубликована: Апрель 11, 2025
Язык: Английский
Beyond Voice Recognition: Integrating Alexa’s Emotional Intelligence and ChatGPT’s Language Processing for EFL Learners’ Development and Anxiety Reduction - A Comparative Analysis
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 11, 2025
Abstract
This
groundbreaking
study
investigates
the
integration
of
Amazon
Alexa,
an
emotionally
intelligent
AI
platform,
into
English
language
teaching
through
adaptive
learning
system.
Using
a
mixed-methods
design,
examined
impact
this
innovative
platform
on
speaking
skills
40
high
school
students
(aged
16–18)
from
Varamin
County,
Iran.
The
experimental
group
(n
=
20)
engaged
with
Alexa's
which
provides
AI-driven
real-time
feedback
based
emotional
intelligence
(EI);
in
contrast,
control
received
instruction
using
ChatGPT-3.5
over
eight
sessions
following
pre-test
to
ensure
homogeneity.
employed
concurrent
mixed
methods
quantitative
data
collected
researcher-developed
Speaking
Assessment
System
and
Perception
Questionnaire;
qualitative
were
derived
classroom
observation
checklists
semi-structured
interviews
15),
focusing
state
monitoring
anxiety
reduction
patterns.
Statistical
analyses
revealed
significant
positive
correlation
between
EI-based
performance
(p
<
0.05,
η2
0.42),
showing
significantly
improved
(F(1,38)
24.63,
p
0.05).
detection
capabilities
demonstrated
94%
accuracy
identifying
responding
learners'
states.
represents
paradigm
shift
technology,
leveraging
address
cognitive
aspects
acquisition
simultaneously.
findings
have
implications
for
global
market,
particularly
addressing
barriers
learning.
platform's
scalability
cross-cultural
applicability
make
it
potentially
transformative
solution
worldwide,
opening
up
new
avenues
development
educational
technology.
Язык: Английский
AI‐assisted learning environments in China: Exploring the intersections of emotion regulation strategies, grit tendencies, self‐compassion, L2 learning experiences and academic demotivation
British Educational Research Journal,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 15, 2025
Abstract
The
increasing
integration
of
artificial
intelligence
(AI)
in
education
has
led
to
a
surge
interest
AI‐assisted
learning
environments.
These
environments
offer
various
advantages,
yet
deeper
understanding
their
effects
on
key
student‐related
constructs
the
English
as
foreign
language
(EFL)
context
is
essential.
This
study
aimed
fill
this
gap
by
investigating
relationships
between
emotion
regulation
strategies,
grit,
self‐compassion,
L2
experiences
and
academic
demotivation
among
Chinese
EFL
learners
AI‐supported
settings.
A
quantitative
research
design
was
employed,
with
219
students
participating
through
purposive
sampling.
Data
were
collected
using
validated
questionnaires
measuring
five
target
analysed
structural
equation
modelling.
Results
revealed
that
strategies
positively
associated
negatively
demotivation.
Similarly,
grit
tendencies
demonstrated
positive
correlations
negative
Self‐compassion
similar
patterns,
associations
findings
important
pedagogical
implications
for
educators
developers
AI‐powered
platforms
China.
By
influence
regulation,
self‐compassion
learners'
motivation,
can
implement
foster
these
attributes.
Язык: Английский
AI-powered personalized learning: Enhancing self-efficacy, motivation, and digital literacy in adult education through expectancy-value theory
Learning and Motivation,
Год журнала:
2025,
Номер
90, С. 102129 - 102129
Опубликована: Апрель 15, 2025
Язык: Английский
University Learners' Readiness for ChatGPT‐Assisted English Learning: Scale Development and Validation
European Journal of Education,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 21, 2024
ABSTRACT
Recent
AI‐based
language
learning
research
highlights
learners'
crucial
role,
yet
university
learner
readiness
in
ChatGPT‐based
English
remains
unexplored.
Accordingly,
this
current
attempted
to
develop
and
validate
a
tool
evaluate
for
ChatGPT‐assisted
(LRCEL)
address
the
gap
that
prior
instruments
have
not
taken
into
account
features
characteristics
of
ChatGPT
teaching
as
well
students'
achievement
emotions.
Three
hundred
forty‐seven
Chinese
learners
participated
help
explore
confirm
constructs
LRCEL.
Guided
by
theory
planned
behaviour
control‐value
emotions,
results
first‐order
second‐order
confirmatory
factor
analysis,
exploratory
structural
equation
modelling,
convergent
validity
discriminant
supported
an
18‐item
questionnaire
comprising
seven
dimensions.
The
LRCEL
has
been
proven
valid
reliable,
enabling
educational
educators
understand
ChatGPT‐supported
with
domain‐specific
items.
Язык: Английский