IntechOpen eBooks,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 23, 2024
This
chapter
employs
a
system
dynamics
lens
to
examine
the
intricate
interplay
between
artificial
intelligence
(AI)
integration
and
landscape
of
higher
education.
Employing
causal
loop
diagrams,
it
delves
into
evolving
various
key
indicators
in
education
affected
by
AI
implementation.
Beginning
with
an
overview
disruptive
technologies’
current
roles
academia,
including
AI,
proceeds
illustrate
interrelationships
form
feedback
loops
technological
advancements,
pedagogical
methodologies,
institutional
structures,
societal
factors.
Subsequently,
explores
systemic
shifts
student
learning
experiences,
faculty
roles,
administrative
practices
catalysed
infusion.
By
illuminating
complex
web
interactions,
this
aims
provide
insights
crucial
for
fostering
harmonious
effective
within
systems.
Journal of the Medical Library Association JMLA,
Год журнала:
2025,
Номер
113(1), С. 31 - 38
Опубликована: Янв. 14, 2025
Sexual
and
gender
minority
(SGM)
populations
experience
health
disparities
compared
to
heterosexual
cisgender
populations.
The
development
of
accurate,
comprehensive
sexual
orientation
identity
(SOGI)
measures
is
fundamental
quantify
address
SGM
disparities,
which
first
requires
identifying
SOGI-related
research.
As
part
a
larger
project
reviewing
synthesizing
how
SOGI
has
been
assessed
within
the
literature,
we
provide
an
example
application
automated
tools
for
systematic
reviews
area
measurement.
In
collaboration
with
research
librarians,
three-phase
approach
was
used
prioritize
screening
set
11,441
measurement
studies
published
since
2012.
Phase
1,
search
results
were
stratified
into
two
groups
(title
vs.
without
measurement-related
terms);
titles
terms
manually
screened.
2,
supervised
clustering
using
DoCTER
software
sort
remaining
based
on
relevance.
3,
machine
learning
further
identify
deemed
low
relevance
in
2
should
be
prioritized
manual
screening.
1,607
identified
1.
Across
Phases
team
excluded
5,056
9,834
DoCTER.
review,
percentage
relevant
screened
low,
ranging
from
0.1
7.8
percent.
Automated
librarians
have
potential
save
hundreds
hours
human
labor
large-scale
Journal of Infrastructure Policy and Development,
Год журнала:
2025,
Номер
9(1), С. 10412 - 10412
Опубликована: Янв. 13, 2025
This
paper
presents
a
comprehensive
and
integrated
paradigm
for
intelligent
universities
using
artificial
intelligence
(AI)
to
transform
management
systems
teaching,
thus
complementing
sustainable
development
objectives.
Through
systematic
examination
of
top
worldwide
universities’
AI
applications,
this
study
reveals
key
achievements,
obstacles,
strategies
successfully
implementing
AI-driven
universities.
Every
case
focuses
on
particular
project,
including
the
adaptive
learning
at
MIT,
teaching
assistant
Jill
Watson
Georgia
Tech,
AI-enabled
quality
control
system
Cambridge
University.
Combining
review,
meta-analysis,
studies
under
mixed-methods
approach,
provides
practical
guide
improve
administrative
academic
roles.
Results
show
how
can
solve
institutional
issues,
automate
assurance,
personalize
learning.
Recommendations
advocate
gradual
adoption
strategies,
ethical
deployment,
capacity-building
measures
enable
digital
transformation.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 4, 2025
ABSTRACT
The
level
of
cellular
organization
bridging
the
mesoscale
and
whole-cell
scale
is
coming
into
focus
as
a
new
frontier
in
cell
biology.
Great
progress
has
been
made
unraveling
complex
physical
functional
interconnectivity
organelles,
but
how
entire
organelle
network
spatially
arranges
within
cytoplasm
only
beginning
to
be
explored.
Drawing
on
cross-disciplinary
research
synthesis
methods,
we
systematically
curated
volumetric
imaging
literature
through
3
rounds
screening
involving
independent
reviewers,
resulting
89
top
hits
38
“borderline”
studies.
We
describe
trajectory
current
state
field
(2004-2024).
A
broad
characterization,
or
“scoping
review”,
bibliometrics,
study
design,
reporting
practices
shows
accelerating
technological
development
output.
find
high
variability
design
practices,
including
modality,
model
organism,
contexts,
organelles
imaged,
analyses.
Due
laborious,
low-throughput
nature
most
trends
toward
small
sample
sizes
(<30
cells)
types.
common
quantitative
analyses
across
studies,
ratios
inter-organelle
contact
Our
dataset
now
enables
future
aggregate
comparative
potentially
reveal
larger
patterns
generate
more
generalized
hypotheses.
This
work
establishes
growing
data,
motivates
call
for
standardized
reporting,
data
sharing
practices.
More
broadly,
showcase
potential
rigorous
secondary
methods
strengthen
biology’s
review
reproducibility
toolkit,
create
avenues
discovery,
promote
open
that
support
data-reuse
integration.
Machine Learning and Knowledge Extraction,
Год журнала:
2025,
Номер
7(2), С. 28 - 28
Опубликована: Март 26, 2025
As
climate
change
transforms
our
environment
and
human
intrusion
into
natural
ecosystems
escalates,
there
is
a
growing
demand
for
disease
spread
models
to
forecast
plan
the
next
zoonotic
outbreak.
Accurate
parametrization
of
these
requires
data
from
diverse
sources,
including
scientific
literature.
Despite
abundance
publications,
manual
extraction
via
systematic
literature
reviews
remains
significant
bottleneck,
requiring
extensive
time
resources,
susceptible
error.
This
study
examines
application
large
language
model
(LLM)
as
an
assessor
screening
prioritisation
in
climate-sensitive
research.
By
framing
selection
criteria
articles
question–answer
task
utilising
zero-shot
chain-of-thought
prompting,
proposed
method
achieves
saving
at
least
70%
work
effort
compared
recall
level
95%
(NWSS@95%).
was
validated
across
four
datasets
containing
distinct
diseases
critical
variable
(rainfall).
The
approach
additionally
produces
explainable
AI
rationales
each
ranked
article.
effectiveness
multiple
demonstrates
potential
broad
reviews.
substantial
reduction
effort,
along
with
provision
rationales,
marks
important
step
toward
automated
parameter
Journal of Teacher Education,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 29, 2025
The
autoethnographic
study
investigates
the
transformative
impact
of
generative
AI
on
educational
research,
instructional
design,
and
teaching
practices
over
a
5-month
period
(May–October
2024).
By
integrating
tools
into
every
phase
research
process,
examines
AI’s
role
as
both
partner
subject
inquiry.
Field
notes,
queries,
AI-generated
outputs
were
systematically
collected,
creating
corpus
for
analysis.
Grounded
in
activity
theory,
this
offers
reflective
narrative
evolving
work
routines
designers
educators,
emphasizing
orchestration
technology
rather
than
prescriptive
best
practices.
contributes
to
by
documenting
use
at
specific
point
time,
providing
foundation
future
inquiry
practical
implications
education.
