The Integration of AI and Metaverse in Education: A Systematic Literature Review
Applied Sciences,
Год журнала:
2025,
Номер
15(2), С. 863 - 863
Опубликована: Янв. 16, 2025
The
use
of
the
metaverse
in
educational
environments
has
grown
significantly
recent
years,
particularly
following
shift
major
tech
companies
towards
virtual
worlds
and
immersive
technologies.
Virtual
reality
augmented
technologies
are
employed
to
construct
learning
environments.
is
generally
understood
as
a
vast
digital
ecosystem
or
space,
facilitating
transition
individuals
from
physical
environments,
applicable
domains
where
practical
experiments
challenging
fraught
with
risks,
such
space
exploration,
chemical
experimentation,
flight
simulation
training.
In
addition,
integration
artificial
intelligence
within
contexts
enriched
environment,
giving
rise
AI-driven
teaching
systems
tailored
each
student’s
individual
pace
modalities.
As
result,
number
research
articles
have
been
conducted
explore
applications
education.
This
paper
provides
systematic
literature
review
PRISMA
methodology
analyze
investigate
significance
impact
education,
specific
focus
on
AI
metaverse.
We
address
inquiries
regarding
applications,
challenges,
academic
disciplines,
effects
integrating
education
that
not
yet
explored
most
articles.
Additionally,
we
study
techniques
used
their
roles.
affirms
utilization
will
enrich
by
improving
students’
understanding
comprehension
across
diverse
disciplines.
Язык: Английский
Shaping the Future of Healthcare: Ethical Clinical Challenges and Pathways to Trustworthy AI
Journal of Clinical Medicine,
Год журнала:
2025,
Номер
14(5), С. 1605 - 1605
Опубликована: Фев. 27, 2025
Background/Objectives:
Artificial
intelligence
(AI)
is
transforming
healthcare,
enabling
advances
in
diagnostics,
treatment
optimization,
and
patient
care.
Yet,
its
integration
raises
ethical,
regulatory,
societal
challenges.
Key
concerns
include
data
privacy
risks,
algorithmic
bias,
regulatory
gaps
that
struggle
to
keep
pace
with
AI
advancements.
This
study
aims
synthesize
a
multidisciplinary
framework
for
trustworthy
focusing
on
transparency,
accountability,
fairness,
sustainability,
global
collaboration.
It
moves
beyond
high-level
ethical
discussions
provide
actionable
strategies
implementing
clinical
contexts.
Methods:
A
structured
literature
review
was
conducted
using
PubMed,
Scopus,
Web
of
Science.
Studies
were
selected
based
relevance
ethics,
governance,
policy
prioritizing
peer-reviewed
articles,
analyses,
case
studies,
guidelines
from
authoritative
sources
published
within
the
last
decade.
The
conceptual
approach
integrates
perspectives
clinicians,
ethicists,
policymakers,
technologists,
offering
holistic
“ecosystem”
view
AI.
No
trials
or
patient-level
interventions
conducted.
Results:
analysis
identifies
key
current
governance
introduces
Regulatory
Genome—an
adaptive
oversight
aligned
trends
Sustainable
Development
Goals.
quantifiable
trustworthiness
metrics,
comparative
categories
applications,
bias
mitigation
strategies.
Additionally,
it
presents
interdisciplinary
recommendations
aligning
deployment
environmental
sustainability
goals.
emphasizes
measurable
standards,
multi-stakeholder
engagement
strategies,
partnerships
ensure
future
innovations
meet
practical
healthcare
needs.
Conclusions:
Trustworthy
requires
more
than
technical
advancements—it
demands
robust
safeguards,
proactive
regulation,
continuous
By
adopting
recommended
roadmap,
stakeholders
can
foster
responsible
innovation,
improve
outcomes,
maintain
public
trust
AI-driven
healthcare.
Язык: Английский
Understanding GAI risk awareness among higher vocational education students: An AI literacy perspective
Education and Information Technologies,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 27, 2025
Язык: Английский
Analysing the Suitability of Artificial Intelligence in Healthcare and the Role of AI Governance
Health Care Analysis,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 6, 2025
Язык: Английский
AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assurance
Frontiers in Nutrition,
Год журнала:
2025,
Номер
12
Опубликована: Март 13, 2025
This
study
aims
to
explore
the
transformative
role
of
Artificial
Intelligence
(AI)
in
food
manufacturing
by
optimizing
production,
reducing
waste,
and
enhancing
sustainability.
review
follows
a
literature
approach,
synthesizing
findings
from
peer-reviewed
studies
published
between
2019
2024.
A
structured
methodology
was
employed,
including
database
searches
inclusion/exclusion
criteria
assess
AI
applications
manufacturing.
By
leveraging
predictive
analytics,
real-time
monitoring,
computer
vision,
streamlines
workflows,
minimizes
environmental
footprints,
ensures
product
consistency.
The
examines
AI-driven
solutions
for
waste
reduction
through
data-driven
modeling
circular
economy
practices,
aligning
industry
with
global
sustainability
goals.
Additionally,
it
identifies
key
barriers
adoption—including
infrastructure
limitations,
ethical
concerns,
economic
constraints—and
proposes
strategies
overcoming
them.
highlight
necessity
cross-sector
collaboration
among
stakeholders,
policymakers,
technology
developers
fully
harness
AI's
potential
building
resilient
sustainable
ecosystem.
Язык: Английский
Investigation of Pressure Injuries With Visual ChatGPT Integration: A Descriptive Cross‐Sectional Study
Journal of Advanced Nursing,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 14, 2025
ABSTRACT
Aim
This
study
aimed
to
assess
the
performance
of
Visual
ChatGPT
in
staging
pressure
injuries
using
real
patient
images,
compare
it
manual
by
expert
nurses,
and
evaluate
its
applicability
as
a
supportive
tool
wound
care
management.
Design
used
descriptive
comparative
cross‐sectional
design.
Methods
The
analysed
155
injury
images
from
hospital
database,
staged
nurses
National
Pressure
Injury
Advisory
Panel
guidelines.
ChatGPT's
was
tested
two
scenarios:
with
only
plus
characteristics.
Diagnostic
evaluated,
including
sensitivity,
specificity,
accuracy,
inter‐rater
agreement
(Kappa).
Results
Expert
demonstrated
superior
accuracy
specificity
across
most
stages.
performed
comparably
early‐stage
injuries,
especially
when
characteristics
were
included,
but
struggled
unstageable
deep‐tissue
injuries.
Conclusion
shows
potential
an
artificial
intelligence
for
management
nursing.
However,
improvements
are
necessary
complex
cases,
ensuring
that
complements
clinical
judgement.
Implications
Profession
and/or
Patient
Care
can
serve
innovative
settings,
assisting
less
experienced
those
areas
limited
specialists
managing
Reporting
Method
STROBE
checklist
followed
reporting
studies
line
relevant
EQUATOR
Contribution
No
or
public
contribution.
Язык: Английский