International Journal of Human-Computer Interaction,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 14
Published: Dec. 4, 2024
Generative
Artificial
Intelligence
(GAI)
holds
significant
potential
to
enhance
pre-service
teacher
professional
development.
However,
research
has
primarily
focused
on
initial
acceptance,
neglecting
post-acceptance
behaviours,
particularly
the
factors
influencing
continued
GAI
use
among
teachers.
To
address
this
gap,
study
extends
an
Expectation-Confirmation
Model
(ECM)
include
information
quality
and
AI
self-efficacy
as
additional
determinants.
Using
partial
least
squares
structural
equation
modelling
(PLS-SEM)
approach,
we
analysed
data
from
506
Chinese
Findings
reveal
that
positively
impacts
perceived
usefulness
expectation
confirmation,
both
of
which
satisfaction.
Together
with
self-efficacy,
these
elements
emerged
key
predictors
intention
continue
using
GAI,
most
direct
factor.
Contrary
hypothesis,
personal
major
did
not
moderate
relationships.
This
contributes
a
deeper
understanding
behaviours
motivations
teachers
post-GAI
adoption,
offering
new
insights
into
sustained
development
integration
in
education.
Systems,
Journal Year:
2024,
Volume and Issue:
12(5), P. 176 - 176
Published: May 15, 2024
The
application
of
artificial
intelligence
(AI)
in
programming
assistance
has
garnered
researchers’
attention
for
its
potential
to
reduce
learning
costs
users,
increase
work
efficiency,
and
decrease
repetitive
coding
tasks.
However,
given
the
novelty
AI
Coding
Assistant
Tools
(AICATs),
user
acceptance
is
currently
limited,
factors
influencing
this
phenomenon
are
unclear.
This
study
proposes
an
expanded
model
based
on
Technology
Acceptance
Model
(TAM)
that
incorporates
characteristics
AICAT
users
explore
key
affecting
college
students’
willingness
use
AICATs.
Utilizing
a
survey
methodology,
303
Chinese
participants
completed
questionnaire.
Factor
analysis
Structural
Equation
Modeling
(SEM)
results
indicate
users’
dependence
worry
(DW)
about
AICATs
positively
affects
perceived
risk
(PR),
which
turn
negatively
impacts
usefulness
(PU)
ease
(PEOU),
thus
reducing
use.
Dependence
concerns
also
impact
trust
(PT),
while
PT
PU
PEOU,
thereby
enhancing
Additionally,
user’s
self-efficacy
(SE)
DW
PEOU.
discusses
significance
these
findings
offers
suggestions
developers
foster
promote
widespread
Journal of University Teaching and Learning Practice,
Journal Year:
2024,
Volume and Issue:
21(06)
Published: April 19, 2024
This
study
explores
the
utilization
and
perception
of
Artificial
Intelligence
(AI)
tools
among
students
in
higher
education.
With
growing
accessibility
AI
technologies,
their
integration
into
educational
settings
presents
a
new
frontier
for
enhancing
learning
experiences.
research
adopts
mixed-methods
approach,
including
surveys
interviews,
to
delve
how
employ
perceived
benefits
drawbacks
usage
context
entrepreneurship
education
business
school.
The
findings
reveal
diverse
range
applications,
highlighting
such
as
increased
productivity,
personalized
learning,
enhanced
linguistic
capability.
However,
concerns
regarding
academic
integrity,
over-reliance
on
AI,
need
clear
guidelines
are
also
identified.
contributes
understanding
AI's
role
provides
much-needed
empirical
evidence
from
students’
perspectives.
Our
underscore
importance
balanced,
informed,
ethical
use
Review of Educational Research,
Journal Year:
2024,
Volume and Issue:
unknown
Published: June 14, 2024
Given
the
importance
of
conversation
practice
in
language
learning,
chatbots,
especially
ChatGPT,
have
attracted
considerable
attention
for
their
ability
to
converse
with
learners
using
natural
language.
This
review
contributes
literature
by
examining
currently
unclear
overall
effect
chatbots
on
learning
performance
and
comprehensively
identifying
important
study
characteristics
that
affect
effectiveness.
We
meta-analyzed
70
sizes
from
28
studies,
robust
variance
estimation.
The
effects
were
assessed
based
18
about
learners,
objectives,
context,
communication/interaction,
methodological
pedagogical
designs.
Results
indicated
produced
a
positive
(
g
=
0.484),
compared
nonchatbot
conditions.
Moreover,
four
(i.e.,
educational
level,
interface
design,
interaction
capability)
affected
In
an
in-depth
discussion
how
are
related
effectiveness,
future
implications
research
presented.
Systems,
Journal Year:
2024,
Volume and Issue:
12(9), P. 332 - 332
Published: Aug. 29, 2024
This
study
investigates
the
factors
influencing
undergraduate
students’
self-directed
learning
(SDL)
abilities
in
generative
Artificial
Intelligence
(AI)-driven
interactive
environments.
The
advent
of
AI
has
revolutionized
environments,
offering
unprecedented
opportunities
for
personalized
and
adaptive
education.
Generative
supports
teachers
delivering
smart
education,
enhancing
acceptance
technology,
providing
personalized,
experiences.
Nevertheless,
application
higher
education
is
underexplored.
explores
how
these
AI-driven
platforms
impact
abilities,
focusing
on
key
teacher
support,
strategies,
technology
acceptance.
Through
a
quantitative
approach
involving
surveys
306
undergraduates,
we
identified
motivation,
technological
familiarity,
quality
interaction.
findings
reveal
mediating
roles
self-efficacy
motivation.
Also,
confirmed
that
improvements
support
strategies
within
AI-enhanced
environments
contribute
to
increasing
self-efficacy,
acceptance,
contributes
uncovering
can
inform
design
more
effective
educational
technologies
enhance
student
autonomy
outcomes.
Our
theoretical
model
research
deepen
understanding
applying
while
important
contributions
managerial
implications.
Behavioral Sciences,
Journal Year:
2025,
Volume and Issue:
15(3), P. 328 - 328
Published: March 7, 2025
Although
the
adoption
and
benefits
of
GenAI
(Generative
Artificial
Intelligence)
tools
among
higher
education
students
have
been
widely
explored
in
existing
studies,
less
is
known
about
how
individual
capabilities
influence
use
these
tools.
Drawing
on
Information
System
Success
Model
(ISSM)
Expectation–Confirmation
(ECM),
this
study
examines
students’
capabilities,
including
critical
thinking,
self-directed
learning
ability,
AI
literacy,
impact
quality
information
obtained
from
Additionally,
it
explores
relationships
quality,
student
satisfaction,
intention
to
continue
using
education.
Survey
data
1448
users
Chinese
universities
reveal
that
with
stronger
tend
extract
higher-quality
information,
which
turn
fosters
their
satisfaction
The
findings
highlight
crucial
role
maximizing
potential
tools,
emphasizes
need
cultivate
literacy
achieve
sustainable
success
era.
Theoretically,
extends
ISSM
ECM
by
exploring
mediating
user
between
Practically,
provides
implications
for
educators
policymakers
enhance
thus
