Uncovering personalized L2 motivation and self-regulation in ChatGPT-assisted language learning: A hybrid PLS-SEM-ANN approach
Computers in Human Behavior Reports,
Год журнала:
2024,
Номер
17, С. 100539 - 100539
Опубликована: Дек. 7, 2024
Язык: Английский
Language learners’ surface, deep, and organizing approaches to ChatGPT-assisted language learning: What contextual, individual, and ChatGPT-related factors contribute?
Smart Learning Environments,
Год журнала:
2025,
Номер
12(1)
Опубликована: Фев. 6, 2025
Abstract
Researchers
have
significantly
explored
language
learners'
attitudes
toward
ChatGPT
through
the
lens
of
technology
acceptance
models,
particularly
with
its
development
and
integration
into
computer-assisted
learning
(CALL).
However,
further
research
in
this
area
is
necessary
to
apply
a
theoretical
framework
pedagogical-oriented
perspective.
Therefore,
study,
researchers
utilized
students'
approaches
environment
(SAL)
extended
it
by
incorporating
multilevel
perspective
that
encompasses
contextual,
individual,
ChatGPT-related
factors.
Accordingly,
integrated
their
syllabus
guided
learners
three
universities
Ardabil
City
use
during
academic
year
2023–2024.
In
end,
214
participants
answered
study
questionnaire.
The
result
partial
least
squares
modeling
(PLS-SEM),
Importance
performance
map
analysis
(IPMA)
showed
leadership,
where
university
executive
provides
atmosphere
for
norms
integration,
could
shape
learners’
organizing
approach
using
daily
schedule.
Additionally,
personalization
anthropomorphism
were
among
significant
factors
shaped
deep
as
source
meaningful,
cross-referenced
CALL
tool.
low
feedback
reliability,
privacy
concerns,
ChatGPT's
perceived
value
contributed
surface
minimizing
ChaGPT-related
factor.
On
basis
these
findings,
introduces
new
conceptual
artificial
intelligence
(AILL)
suggests
leadership
should
be
promoted
at
macro-contextual
level
might
cover
other
micro-contextual,
personal,
factors,
including
price-value,
personalization,
motivation,
which
are
important
elements
CHAGPTALL.
Язык: Английский
Exploring the Scientific Validity of ChatGPT’s Responses in Elementary Science for Sustainable Education
Sustainability,
Год журнала:
2025,
Номер
17(7), С. 2962 - 2962
Опубликована: Март 27, 2025
As
AI
integration
in
education
increases,
it
is
crucial
to
evaluate
its
effectiveness
elementary
science
learning,
particularly
promoting
sustainable
through
equitable
access
knowledge.
This
study
aims
assess
the
validity
and
applicability
of
ChatGPT3.5
(free
version)
responses
Earth
Space
science.
A
document
analysis
1200
AI-generated
was
conducted
scientific
validity,
explanatory
clarity,
pedagogical
relevance.
The
employed
quantitative
methods
accuracy
alignment
with
curricula,
while
qualitative
insights
identified
linguistic
conceptual
challenges.
findings
indicate
that
94.2%
were
scientifically
valid,
70.6%
clear,
but
only
12.8%
aligned
curricula.
While
ChatGPT
provides
accurate
information,
many
included
complex
terminology
unsuitable
for
young
learners.
Additionally,
87.2%
lacked
posing
challenges
effective
classroom
integration.
Despite
these
limitations,
shows
potential
simplifying
concepts
expanding
educational
resources.
Refining
content
curriculum-based
filtering,
adaptive
language
processing,
teacher
mediation
necessary.
Strengthening
AI-driven
strategies
a
sustainability
focus
can
ensure
long-term
improvements
learning.
highlights
need
further
research
on
optimizing
tools
education.
Язык: Английский
Continuous use of AI technology: the roles of trust and satisfaction
Aslib Journal of Information Management,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 14, 2025
Purpose
Chat
Generative
Pretrained
Transformer
(ChatGPT),
a
chatbot
with
artificial
intelligence
(AI)
technology,
opens
up
new
directions
for
innovation.
However,
the
extent
to
which
literature
has
not
considered
trustworthiness
and
satisfaction
of
ChatGPT.
Those
are
important
elements
leading
continuous
use
(CU).
Particularly,
this
study
investigates
ChatGPT
Translate
function.
Requirements
task-AI-technology
fit,
trust
relevant
addressed
in
study.
Design/methodology/approach
Task-technology
fit
(TTF)
theory
forms
theoretical
lens
examine
influences
TTF,
AI-tech
on
CU
AI
technology.
A
questionnaire
survey
was
used
data
collection.
Structural
equation
modeling
employed
test
research
model.
Findings
The
findings
show
task
technology
characteristics
have
positive
effects
fit.
Task-AI-technology
effect
trust,
turn
Finally,
level
by
users
satisfied
its
responses
is
higher
than
dissatisfied
responses.
Originality/value
results
practical
implications
academia
industry
devise
strategies
policies
free-to-use
system.
Язык: Английский
Exploring socio-cultural influences on generative AI engagement in Nigerian higher education: an activity theory analysis
Smart Learning Environments,
Год журнала:
2024,
Номер
11(1)
Опубликована: Дек. 23, 2024
Abstract
This
study
explores
how
socio-cultural
dynamics
influence
student
engagement
with
Generative
AI
technology
in
Nigerian
higher
education,
using
activity
theory
as
theoretical
underpinning.
By
examining
the
roles
of
community
norms,
technological
accessibility,
and
educational
objectives,
research
identifies
critical
factors
that
impact
adoption
utilisation
GenAI.
We
employ
quantitative
analysis
to
analyse
899
survey
responses
from
students
across
seventeen
(17)
universities
derive
interesting
insights.
Findings
reveal
ease
use
GenAI
tools
their
alignment
goals
enhance
engagement.
Conversely,
regular
need
for
technical
support
negatively
affect
engagement,
suggesting
underlying
issues.
These
insights
provide
actionable
recommendations
educators,
administrators,
policymakers,
emphasising
importance
user-friendly
tools,
comprehensive
training
programs,
robust
systems.
contributes
understanding
culturally
diverse
settings
offers
strategies
improve
practices
outcomes
both
education
also
potentially
other
African
(developing
countries),
where
similar
might
integration
advancements.
Язык: Английский
ВИКОРИСТАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В ОСВІТІ: ТЕНДЕНЦІЇ ТА ПЕРСПЕКТИВИ В УКРАЇНІ ТА ЗА КОРДОНОМ
UNESCO Chair Journal Lifelong Professional Education in the XXI Century,
Год журнала:
2024,
Номер
2(10), С. 152 - 161
Опубликована: Ноя. 29, 2024
В
оглядовій
статті
проаналізовано
проблему
використання
систем
штучного
інтелекту
(ШІ)
як
інструменту
цифровізації
освіти.
Проаналізовано
досвід
України
і
зарубіжних
країн,
теорію
та
практику
застосування
учасниками
освітнього
процесу.
Вивчено
результати
досліджень
українських
науковців
щодо
в
галузі
освіти,
виокремлено
впливи
інтелекту,
зокрема,
їх
сучасного
покоління
–
генеративного
інтелекту.
нормативно-правову
база
впровадження
освітню
галузь.
Виокремлено
міжнародні
підходи,
загальні
тенденції
описано
напрями
перспективи
для
підтримки
До
сучасних
технологій
ШІ,
що
вже
частково
використовуються
освіті,
належать:
експертні
системи,
чат-боти,
інтелектуальні
репетитори,
персоналізовані
системи
навчання,
візуалізації
віртуальні
навчальні
середовища,
технології
машинного
навчання.
Основним
напрямами
ШІ
розвиток
яких
сприяє
підтримці
освітньої
є:
персоналізація
інтелектуальних
систем-помічників,
аналітика
автоматизація
рутинних
завдань,
інноваційні
методи
навчання
з
використанням
Метою
є
аналіз
стану
упровадження
у
сфері
обґрунтування
тенденцій
розвитку
Україні
світі,
окреслення
перспектив
освітньому
процесі.
Практичне
значення
дослідження
полягає
обґрунтуванні
рекомендацій
Акцентовано
увагу
на
викликах
якими
стикаються
сучасні
заклади
а
саме:
проблема
етики,
забезпечення
конфіденційності
безпеки,
недостатній
рівень
сформованості
цифрової
компетентності
аспекті
фрагментарність
навчального
й
науково-методичного
освітній
процес
(рекомендації,
методики,
моделі
тощо.
A Bibliometric Analysis of Smart Learning Environments Under the Digital Pedagogy Paradigm
Advances in educational technologies and instructional design book series,
Год журнала:
2024,
Номер
unknown, С. 1 - 26
Опубликована: Дек. 20, 2024
Focusing
on
the
identification
of
important
themes,
new
trends,
and
essential
elements
that
define
subject,
this
report
offers
a
thorough
bibliometric
analysis
research
terrain
surrounding
Smart
Learning
Environments
(SLEs).
Three
main
clusters
were
found
by
means
investigation
keyword
co-occurrence
network
visualization:
learner-centric
technologies
methods,
artificial
intelligence
adaptive
learning
systems,
assessment
results.
Driven
developments
in
mobile
learning,
virtual
reality,
intelligence,
analytics,
study
exposes
growing
focus
tailored,
immersive,
data-driven
educational
experiences.
The
also
looks
at
affecting
SLE
adoption:
technology
infrastructure,
instructional
efficacy,
personal
attitudes,
organizational
support.
results
underline
dynamic
changing
character
provide
understanding
how
these
settings
may
be
efficiently
used
into
teaching
strategies
to
improve
outcomes
for
learning.
Язык: Английский