Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: March 22, 2025
YouTube
has
become
a
dominant
source
of
medical
information
and
health-related
decision-making.
Yet,
many
videos
on
this
platform
contain
inaccurate
or
biased
information.
Although
expert
reviews
could
help
mitigate
situation,
the
vast
number
daily
uploads
makes
solution
impractical.
In
study,
we
explored
potential
Large
Language
Models
(LLMs)
to
assess
quality
content
YouTube.
We
collected
set
previously
evaluated
by
experts
prompted
twenty
models
rate
their
using
DISCERN
instrument.
then
analyzed
inter-rater
agreement
between
language
models'
experts'
ratings
Brennan–Prediger's
(BP)
Kappa.
found
that
LLMs
exhibited
wide
range
agreements
with
(ranging
from
−1.10
0.82).
All
tended
give
higher
scores
than
human
experts.
The
individual
questions
be
lower,
some
showing
significant
disagreement
Including
scoring
guidelines
in
prompt
improved
model
performance.
conclude
are
capable
evaluating
videos.
If
used
as
stand-alone
systems
embedded
into
traditional
recommender
systems,
these
can
issue
online
Behaviour and Information Technology,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 27
Published: Sept. 2, 2024
Generative
artificial
intelligence
(GenAI)
tools,
such
as
large
language
models
(LLMs),
generate
natural
and
other
types
of
content
to
perform
a
wide
range
tasks.
This
represents
significant
technological
advancement
that
poses
opportunities
challenges
educational
research
practice.
commentary
brings
together
contributions
from
nine
experts
working
in
the
intersection
learning
technology
presents
critical
reflections
on
opportunities,
challenges,
implications
related
GenAI
technologies
context
education.
In
commentary,
it
is
acknowledged
GenAI's
capabilities
can
enhance
some
teaching
practices,
design,
regulation
learning,
automated
content,
feedback,
assessment.
Nevertheless,
we
also
highlight
its
limitations,
potential
disruptions,
ethical
consequences,
misuses.
The
identified
avenues
for
further
include
development
new
insights
into
roles
human
play,
strong
continuous
evidence,
human-centric
design
technology,
necessary
policy,
support
competence
mechanisms.
Overall,
concur
with
general
skeptical
optimism
about
use
tools
LLMs
Moreover,
danger
hastily
adopting
education
without
deep
consideration
efficacy,
ecosystem-level
implications,
ethics,
pedagogical
soundness
practices.
Computers and Education Artificial Intelligence,
Journal Year:
2024,
Volume and Issue:
6, P. 100209 - 100209
Published: Jan. 25, 2024
The
potential
application
of
generative
artificial
intelligence
(AI)
in
schools
and
universities
poses
great
challenges,
especially
for
the
assessment
students'
texts.
Previous
research
has
shown
that
people
generally
have
difficulty
distinguishing
AI-generated
from
human-written
texts;
however,
ability
teachers
to
identify
an
text
among
student
essays
not
yet
been
investigated.
Here
we
show
two
experimental
studies
novice
(N
=
89)
experienced
200)
could
texts
generated
by
ChatGPT
student-written
However,
there
are
some
indications
more
made
differentiated
accurate
judgments.
Furthermore,
both
groups
were
overconfident
their
Effects
real
assumed
source
on
quality
heterogeneous.
Our
findings
demonstrate
with
relatively
little
prompting,
current
AI
can
generate
detectable
teachers,
which
a
challenge
grading
essays.
study
provides
empirical
evidence
debate
regarding
exam
strategies
light
latest
technological
developments.
JMIR Medical Education,
Journal Year:
2024,
Volume and Issue:
10, P. e59213 - e59213
Published: June 27, 2024
Although
history
taking
is
fundamental
for
diagnosing
medical
conditions,
teaching
and
providing
feedback
on
the
skill
can
be
challenging
due
to
resource
constraints.
Virtual
simulated
patients
web-based
chatbots
have
thus
emerged
as
educational
tools,
with
recent
advancements
in
artificial
intelligence
(AI)
such
large
language
models
(LLMs)
enhancing
their
realism
potential
provide
feedback.
British Journal of Educational Technology,
Journal Year:
2024,
Volume and Issue:
55(5), P. 1982 - 2002
Published: July 12, 2024
Abstract
Large
language
models
(LLMs)
are
increasingly
adopted
in
educational
contexts
to
provide
personalized
support
students
and
teachers.
The
unprecedented
capacity
of
LLM‐based
applications
understand
generate
natural
can
potentially
improve
instructional
effectiveness
learning
outcomes,
but
the
integration
LLMs
education
technology
has
renewed
concerns
over
algorithmic
bias,
which
may
exacerbate
inequalities.
Building
on
prior
work
that
mapped
traditional
machine
life
cycle,
we
a
framework
LLM
cycle
from
initial
development
customizing
pre‐trained
for
various
settings.
We
explain
each
step
identify
potential
sources
bias
arise
context
education.
discuss
why
current
measures
fail
transfer
LLM‐generated
text
(eg,
tutoring
conversations)
because
encodings
high‐dimensional,
there
be
multiple
correct
responses,
tailoring
responses
pedagogically
desirable
rather
than
unfair.
proposed
clarifies
complex
nature
provides
practical
guidance
their
evaluation
promote
equity.
Practitioner
notes
What
is
already
known
about
this
topic
(ML)
focus
predicting
labels
well
understood.
Biases
enter
ML
at
points
methods
measure
mitigate
these
biases
have
been
developed
tested.
other
forms
generative
artificial
intelligence
(GenAI)
technologies
(EdTech),
approaches
not
specific
domain
paper
adds
A
holistic
perspective
with
domain‐specific
examples
highlight
opportunities
challenges
incorporating
understanding
(NLU)
generation
(NLG)
into
EdTech.
Potential
identified
discussed
where
expect
harms
students,
teachers,
users
GenAI
education,
guide
measurement
mitigation.
Implications
practice
and/or
policy
Education
practitioners
policymakers
should
aware
originate
multitude
steps
offers
them
heuristic
asking
developers
assess
risk
bias.
Measuring
systems
use
more
ML,
large
part
highly
context‐dependent
what
counts
as
good
feedback
an
assignment
varies).
EdTech
play
important
role
collecting
curating
datasets
benchmarking
moving
forward.
Behaviour and Information Technology,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 18
Published: March 26, 2024
Generative
Artificial
Intelligence
(AI)
is
a
rapidly
expanding
field
that
aims
to
develop
machines
capable
of
performing
tasks
were
previously
considered
unique
humans,
such
as
learning,
reasoning,
problem-solving,
and
decision-making.
The
recent
release
several
tools
based
on
AI
(e.g.
ChatGPT)
has
sparked
debates
the
potential
this
technology
garnered
widespread
attention
in
mainstream
media.
Frontiers in Education,
Journal Year:
2025,
Volume and Issue:
9
Published: Jan. 8, 2025
The
increasingly
digital
landscape
of
higher
education
has
highlighted
the
importance
self-regulated
learning
in
environments.
To
support
this,
academic
goal
setting
is
frequently
used
to
enhance
order
improve
performance.
Although
many
studies
have
explored
implementation
activities
as
behavioral
modifiers,
across
these
varied,
and
there
little
consensus
on
components
which
should
be
included
reported
when
studying
activities.
provide
an
overview
current
state
field,
a
systematic
review
was
carried
out
examining
implemented
within
over
last
14
years
(2010–2024)
determine
for
whom,
what
contexts,
how
been
implemented.
results
from
60
reveal
wide
array
implementations
covering
countries
disciplines.
Overall,
are
highly
heterogeneous,
with
large
differences
between
out.
However,
also
show
strong
trend
toward
partial
digitalization,
most
using
technology
deliver
their
activities,
but
very
few
adopting
technologies
any
further
enhancements
or
support.
reveals
focus
non-experimental
exploring
content
student
goals,
only
small
selection
testing
effect
experimental
studies.
Based
we
suggest
future
work
focuses
setting,
especially
focusing
interplay
design
individual
needs,
well
investigation
emerging
educational
can
scale
