Engineering students' perceptions and actual use of AI-based math tools for solving mathematical problems
Acta Psychologica,
Год журнала:
2025,
Номер
256, С. 105004 - 105004
Опубликована: Апрель 12, 2025
Язык: Английский
Engineering Students’ Use of Large Language Model Tools: An Empirical Study Based on a Survey of Students from 12 Universities
Education Sciences,
Год журнала:
2025,
Номер
15(3), С. 280 - 280
Опубликована: Фев. 24, 2025
Large
language
model
(LLM)
tools,
such
as
ChatGPT,
are
rapidly
transforming
engineering
education
by
enhancing
tasks
like
information
retrieval,
coding,
and
writing
refinement,
which
critical
to
the
problem-solving
technical
focus
of
disciplines.
This
study
investigates
how
students
use
LLM
tools
challenges
they
face,
offering
insights
into
adoption
AI
technologies
in
academic
settings.
A
survey
539
from
12
leading
Chinese
universities,
using
UTAUT
framework,
examines
factors
technological
expectations,
environmental
support,
personal
characteristics.
The
key
findings
include
following:
(1)
Over
40%
with
18.8%
regarding
them
indispensable.
(2)
Trust
AI-generated
content
remains
a
central
challenge,
must
critically
evaluate
its
accuracy
reliability.
(3)
Environmental
support
significantly
affects
usage,
notable
regional
disparities,
particularly
between
eastern
other
regions
China.
(4)
persistent
digital
divide,
influenced
gender,
level,
socioeconomic
background,
depth
effectiveness
tool
use.
These
results
underscore
need
for
targeted
address
demographic
disparities
optimize
integration
education.
Язык: Английский
Large language models and GenAI in education: Insights from Nigerian in-service teachers through a hybrid ANN-PLS-SEM approach
F1000Research,
Год журнала:
2025,
Номер
14, С. 258 - 258
Опубликована: Март 4, 2025
Background
The
rapid
integration
of
Artificial
Intelligence
(AI)
in
education
offers
transformative
opportunities
to
enhance
teaching
and
learning.
Among
these
innovations,
Large
Language
Models
(LLMs)
like
ChatGPT
hold
immense
potential
for
instructional
design,
personalized
learning,
administrative
efficiency.
However,
integrating
tools
into
resource-constrained
settings
such
as
Nigeria
presents
significant
challenges,
including
inadequate
infrastructure,
digital
inequities,
teacher
readiness.
Despite
the
growing
research
on
AI
adoption,
limited
studies
focus
developing
regions,
leaving
a
critical
gap
understanding
how
educators
perceive
adopt
technologies.
Methods
We
adopted
hybrid
approach,
combining
Partial
Least
Squares
Structural
Equation
Modelling
(PLS-SEM)
Neural
Networks
(ANN)
uncover
both
linear
nonlinear
dynamics
influencing
behavioral
intention
(BI)
260
Nigerian
in-service
teachers
regarding
after
participating
structured
training.
Key
predictors
examined
include
Perceived
Ease
Use
(PEU),
Usefulness
(PUC),
Attitude
Towards
(ATC),
Your
Colleagues
(YCC),
Technology
Anxiety
(TA),
Teachers’
Trust
(TTC),
Privacy
Issues
(PIU).
Results
Our
PLS-SEM
results
highlight
PUC,
TA,
YCC,
PEU,
that
order
importance,
predictors,
explaining
15.8%
variance
BI.
Complementing
these,
ANN
analysis
identified
ATC,
PUC
most
factors,
demonstrating
substantial
predictive
accuracy
with
an
RMSE
0.87.
This
suggests
while
drives
PEU
positive
attitudes
are
foundational
fostering
engagement
Conclusion
need
targeted
professional
development
initiatives
teachers’
competencies,
reduce
technology-related
anxiety,
build
trust
ChatGPT.
study
actionable
insights
policymakers
educational
stakeholders,
emphasizing
importance
inclusive
ethical
ecosystem.
aim
empower
support
AI-driven
transformation
resource-limited
environments
by
addressing
contextual
barriers.
Язык: Английский
Examining predictors of generative-AI acceptance and usage in academic research: a sequential mixed-methods approach
Benchmarking An International Journal,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 24, 2025
Purpose
This
research
uses
a
mixed-methods
approach
to
identify
predictors
of
Generative
artificial
intelligence
(Gen-AI)
adoption
and
usage
among
academics
educational
researchers.
It
examines
drivers
barriers
based
on
the
diffusion
innovation
theory
(DIT)
planned
behaviour
(TPB).
Design/methodology/approach
A
qualitative
investigation
was
carried
out
by
conducting
interviews
academic
researchers
who
used
Gen-AI
tools
such
as
ChatGPT.
Based
DIT,
TPB
analysis
results,
an
integrated
model
proposed
tested
using
survey
data
collected
from
analysed
partial
least
squares-structural
equation
modelling
(PLS-SEM).
Findings
The
study
demonstrated
that
relative
advantages
observability
influence
attitude
subjective
norms,
these
in
turn
impact
behavioural
intentions.
Researchers'
perception
advantage
their
intentions
use
were
found
lead
positive
behaviours.
However,
technical
limitations
ethical
concerns
acted
key
moderators
between
intention
norms
intention,
respectively.
Mediation
effects
also
observed.
Research
limitations/implications
utilised
DIT
its
base
models,
future
could
incorporate
additional
constructs
other
technology
theories.
concentrated
had
subsequently
reported
significant
factors
affecting
usage.
Future
studies
should
consider
perspective
non-users
tools.
Further,
geographical
focus
India,
broaden
scope.
Practical
implications
community
must
unite
develop
guidelines
for
plagiarism
research.
be
emphasising
importance
highlights
need
establishing
standards,
comprehensive
transparently
within
framework.
Originality/value
results
can
greatly
enhance
understanding
researchers,
particularly
light
about
integrity
potential
negative
consequences
Язык: Английский
An empirical examination of the adoption of artificial intelligence in banking services: the case of Mongolia
Future Business Journal,
Год журнала:
2025,
Номер
11(1)
Опубликована: Апрель 16, 2025
Abstract
Artificial
intelligence
(AI)
has
profoundly
impacted
banking
services,
particularly
in
the
context
of
rapid
technological
advancements.
The
success
sector
depends
on
establishing
customers’
intention
to
adopt
AI.
However,
research
AI
adoption
Mongolia’s
remains
limited,
underscoring
need
understand
consumer
behavior
and
key
factors.
This
paper
seeks
evaluate
attitudes
toward
adopting
services.
To
achieve
this
goal,
we
surveyed
perceptions
customers
from
selected
banks,
yielding
508
participants
487
valid
responses
for
subsequent
analysis.
proposed
model
was
assessed
using
a
partial
least
squares
approach
technical
acceptance
model.
Our
findings
indicate
that
banks
involved
study
have
already
integrated
various
products.
results
demonstrate
perceived
usefulness,
trust,
significantly
enhance
AI-enabled
Additionally,
examines
mediating
effect
banking,
identifying
ATT
as
variable
between
PEOU
PU
with
INT.
These
provide
practical
insights
stakeholders
seeking
AI-powered
customer
service
while
contributing
literature
perspective.
Язык: Английский
Exploring the Predictors of AI Chatbot Usage Intensity Among Students: Within- and Between-Person Relationships Using the Technology Acceptance Model
Computers in Human Behavior Artificial Humans,
Год журнала:
2024,
Номер
unknown, С. 100113 - 100113
Опубликована: Дек. 1, 2024
Язык: Английский