Determinants of Adopting 3D Technology Integrated With Artificial Intelligence in STEM Higher Education: A UTAUT2 Model Approach
Computer Applications in Engineering Education,
Год журнала:
2025,
Номер
33(3)
Опубликована: Апрель 6, 2025
ABSTRACT
Incorporating
3D
technology
and
artificial
intelligence,
often
known
as
AI,
into
STEM
education
in
the
current
day
is
creating
new
opportunities
to
improve
student
engagement
performance.
With
an
emphasis
on
areas
specifically,
this
study
attempts
bring
elements
that
affect
uptake
of
AI‐enabled
instructional
technology.
A
survey
was
carried
out
with
300
participants,
including
teachers
students
from
universities.
To
gauge
participant
impressions,
used
UTAUT2—the
Unified
Theory
Acceptance
Use
Technology
2
framework.
IMB
SPSS
25
AMOS
24
has
been
calculate,
evaluate,
analyze
data
determine
main
variables
influencing
adoption
these
technologies.
The
results
show
while
AI
technologies
have
a
great
deal
potential
enhance
users'
interaction,
understanding,
difficult
scientific
concepts,
there
are
still
obstacles
overcome,
those
related
infrastructure,
cost,
requirement
for
faculty
training.
Furthermore,
it
discovered
moderating
factors
experience,
gender,
age,
level
had
very
little
effect
final
outcomes.
This
provides
insightful
information
how
successfully
incorporate
curricula
at
university
level.
Язык: Английский
Mitigating Conceptual Learning Gaps in Mixed-Ability Classrooms: A Learning Analytics-Based Evaluation of AI-Driven Adaptive Feedback for Struggling Learners
Applied Sciences,
Год журнала:
2025,
Номер
15(8), С. 4473 - 4473
Опубликована: Апрель 18, 2025
Adaptation
through
Artificial
Intelligence
(AI)
creates
individual-centered
feedback
strategies
to
reduce
academic
achievement
disparities
among
students.
The
study
evaluates
the
effectiveness
of
AI-driven
adaptive
in
mitigating
these
gaps
by
providing
personalized
learning
support
struggling
learners.
A
analytics-based
evaluation
was
conducted
on
700
undergraduate
students
enrolled
STEM-related
courses
across
three
different
departments
at
Beaconhouse
International
College
(BIC).
employed
a
quasi-experimental
design,
where
350
received
while
control
group
followed
traditional
instructor-led
methods.
Data
were
collected
over
20
weeks,
utilizing
pre-
and
post-assessments,
real-time
engagement
tracking,
survey
responses.
Results
indicate
that
receiving
demonstrated
28%
improvement
conceptual
mastery,
compared
14%
group.
Additionally,
student
increased
35%,
with
22%
reduction
cognitive
overload.
Analysis
interaction
logs
revealed
frequent
AI-generated
led
40%
increase
retention
rates.
Despite
benefits,
variations
impact
observed
based
prior
knowledge
levels
consistency.
findings
highlight
potential
smart
environments
enhance
educational
equity.
Future
research
should
explore
long-term
effects,
scalability,
ethical
considerations
AI-based
systems.
Язык: Английский