Journal of Science Education and Technology,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 5, 2024
Abstract
As
students
read
scientific
texts
created
in
generative
artificial
intelligence
(GenAI)
tools,
they
need
to
draw
on
their
epistemic
knowledge
of
GenAI
as
well
that
science.
However,
only
a
few
research
discussed
multimodality
methodological
approach
characterising
students’
ideas
GenAI-science
reading.
This
study
qualitatively
explored
44
eighth
and
ninth
graders’
multimodal
representations
about
reading
developed
an
analytical
framework
based
Lemke’s
(1998)
typology
representational
meaning,
namely
presentational,
organisational,
orientational
meanings.
Under
each
several
categories
were
inductively
generated
while
expressed
preferences
using
drawn,
written,
or
both
drawn
written
mode
express
certain
categories.
Findings
indicate
is
fruitful
semiotic
resources
meaning-making
We
suggested
implications
regarding
future
intervention
studies
tracking
the
this
study.
Sustainability,
Год журнала:
2025,
Номер
17(3), С. 1127 - 1127
Опубликована: Янв. 30, 2025
Generative
artificial
intelligence
(GenAI)
technologies
based
on
big
language
models
are
becoming
a
transformative
power
that
reshapes
the
future
shape
of
education.
Although
impact
GenAI
education
is
key
issue,
there
little
exploration
challenges
and
response
strategies
sustainability
from
public
perspective.
This
data
mining
study
selected
ChatGPT
as
representative
tool
for
GenAI.
Five
topics
14
modular
semantic
communities
attitudes
towards
using
in
were
identified
through
Latent
Dirichlet
Allocation
(LDA)
topic
modeling
network
community
discovery
process
40,179
user
comments
collected
social
media
platforms.
The
results
indicate
ambivalence
about
whether
technology
empowering
or
disruptive
to
On
one
hand,
recognizes
potential
education,
including
intelligent
tutoring,
role-playing,
personalized
services,
content
creation,
learning,
where
effective
communication
interaction
can
stimulate
users’
creativity.
other
worried
technological
dependence
development
innovative
capabilities,
erosion
traditional
knowledge
production
by
AI-generated
(AIGC),
undermining
educational
equity
cheating,
substitution
students
passing
good
performance
skills
tests.
In
addition,
some
irresponsible
unethical
usage
behaviors
identified,
direct
use
AIGC
pass
similarity
checks.
provides
practical
basis
institutions
re-examine
teaching
learning
approaches,
assessment
strategies,
talent
goals
formulate
policies
AI
promote
vision
sustainable
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Март 27, 2025
Abstract
This
study
examines
how
ChatGPT-supported
collaborative
learning
influences
critical
thinking
in
education
using
Bloom’s
Taxonomy.
Purposive
sampling
was
used
to
collect
data
from
665
Indonesian
pharmacy
students
through
an
online
survey.
PLS-SEM
assessed
the
direct
effects
of
cognitive
processes
on
thinking.
NCA
identified
essential
conditions,
while
fsQCA
explored
different
pathways
leading
high
or
low
Collaborative
significantly
enhances
understanding,
applying,
and
remembering.
Understanding
has
strongest
effect
thinking,
applying
remembering
have
moderate
effects.
These
findings
suggest
that
deep
comprehension
drives
analytical
reasoning,
whereas
serve
complementary
roles.
confirms
understanding
are
necessary
for
fostering
plays
a
supporting
role.
results
indicate
who
combine
with
memory
retention
exhibit
strong
In
contrast,
rely
solely
without
application
struggle
develop
higher-order
reasoning.
reveals
ChatGPT
does
not
inherently
enhance
but
must
be
integrated
into
structured
learning.
Effective
AI-assisted
requires
active
discussion,
application,
evaluation
AI-generated
insights.
offer
framework
optimizing
AI-driven
support
both
knowledge
acquisition
reasoning
clinical
decision-making.
Journal of Educational Computing Research,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 17, 2025
This
study
addresses
the
gap
in
understanding
graduate
students’
sustained
engagement
behavior
(SEB)
with
generative
artificial
intelligence
(GAI)
by
integrating
Technology
Acceptance
Model
(TAM),
Expectation
Confirmation
Theory
(ECT),
and
of
Reasoned
Action
(TRA)
into
a
comprehensive
embedding
model.
It
introduces
Readiness
Index
for
Innovation
(TRII)
Perception-Oriented
Learning
Style
(POLS)
as
key
factors,
analyzed
through
Structural
Equation
Modeling
(SEM)
Qualitative
Comparative
Analysis
(QCA).
Data
from
862
students
China
were
tested
reliability
validity.
SEM
results
demonstrated
that
TRII
significantly
influences
usage
expectations
(UE),
effort
expectancy
(EE),
performance
(PE),
SEB,
cognitive
affective
factors
mediating
these
relationships.
QCA
revealed
multiple
causal
pathways
leading
to
high
highlighting
principle
equifinality.
The
integration
provided
insights
dual
pathways—implicit
expectation
development
system
processing—that
shape
GAI
adoption,
offering
practical
implications
effective
implementation
higher
education.
