Abstract
This
paper
presents
a
novel
approach
to
training
evaluation
using
Language
Models
(LLM)
and
Generative
AI
(GenAI)
build
classification
model.
The
study
aims
develop
resource-efficient
solution
for
analyzing
rubrics
transcripts,
thereby
enhancing
the
assessment
of
learning
outcomes
performance.
methodology
involves
data
collection,
preprocessing,
model
fine-tuning,
prompting,
evaluation.
A
pre-trained
LLM
is
fine-tuned
on
preprocessed
allowing
it
adapt
specific
language
patterns
structures.
generates
prompts
classify
materials
based
predefined
criteria,
with
domain
expertise
incorporated
complex
rules.
Results
demonstrate
60%
reduction
in
processing
time
evaluating
transcripts
compared
manual
assessment.
implemented
has
significantly
reduced
workload
department
improved
efficiency
analysis.
Furthermore,
model's
feedback
led
targeted
improvements
content,
resulting
higher
learner
satisfaction.
innovative
application
GenAI
offers
new
perspective
leveraging
enhance
educational
processes
manner.
Education Sciences,
Год журнала:
2024,
Номер
14(6), С. 656 - 656
Опубликована: Июнь 17, 2024
This
study
addresses
the
significant
challenge
posed
by
use
of
Large
Language
Models
(LLMs)
such
as
ChatGPT
on
integrity
online
examinations,
focusing
how
these
models
can
undermine
academic
honesty
demonstrating
their
latent
and
advanced
reasoning
capabilities.
An
iterative
self-reflective
strategy
was
developed
for
invoking
critical
thinking
higher-order
in
LLMs
when
responding
to
complex
multimodal
exam
questions
involving
both
visual
textual
data.
The
proposed
demonstrated
evaluated
real
subject
experts
performance
(GPT-4)
with
vision
estimated
an
additional
dataset
600
text
descriptions
questions.
results
indicate
that
invoke
multi-hop
capabilities
within
LLMs,
effectively
steering
them
towards
correct
answers
integrating
from
each
modality
into
final
response.
Meanwhile,
considerable
proficiency
being
able
answer
across
12
subjects.
These
findings
prior
assertions
about
limitations
emphasise
need
robust
security
measures
proctoring
systems
more
sophisticated
mitigate
potential
misconduct
enabled
AI
technologies.
British Journal of Biomedical Science,
Год журнала:
2025,
Номер
81
Опубликована: Янв. 9, 2025
Generative
Artificial
Intelligence
(GenAI)
is
rapidly
transforming
the
landscape
of
higher
education,
offering
novel
opportunities
for
personalised
learning
and
innovative
assessment
methods.
This
paper
explores
dual-edged
nature
GenAI's
integration
into
educational
practices,
focusing
on
both
its
potential
to
enhance
student
engagement
outcomes
significant
challenges
it
poses
academic
integrity
equity.
Through
a
comprehensive
review
current
literature,
we
examine
implications
GenAI
highlighting
need
robust
ethical
frameworks
guide
use.
Our
analysis
framed
within
pedagogical
theories,
including
social
constructivism
competency-based
learning,
importance
balancing
human
expertise
AI
capabilities.
We
also
address
broader
concerns
associated
with
GenAI,
such
as
risks
bias,
digital
divide,
environmental
impact
technologies.
argues
that
while
can
provide
substantial
benefits
in
terms
automation
efficiency,
must
be
managed
care
avoid
undermining
authenticity
work
exacerbating
existing
inequalities.
Finally,
propose
set
recommendations
institutions,
developing
literacy
programmes,
revising
designs
incorporate
critical
thinking
creativity,
establishing
transparent
policies
ensure
fairness
accountability
By
fostering
responsible
approach
education
harness
safeguarding
core
values
inclusive
education.
Trends in Higher Education,
Год журнала:
2025,
Номер
4(1), С. 2 - 2
Опубликована: Янв. 8, 2025
This
collective
systematic
literature
review
is
part
of
an
Erasmus+
project,
“TaLAI:
Teaching
and
Learning
with
AI
in
Higher
Education”.
The
investigates
the
current
state
Generative
Artificial
Intelligence
(GenAI)
higher
education,
aiming
to
inform
curriculum
design
further
developments
within
digital
education.
Employing
a
descriptive,
textual
narrative
synthesis
approach,
study
analysed
across
four
thematic
areas:
learning
objectives,
teaching
activities,
development,
institutional
support
for
ethical
responsible
GenAI
use.
93
peer-reviewed
articles
from
eight
databases
using
keyword-based
search
strategy,
collaborative
coding
process
involving
multiple
researchers,
vivo
transparent
documentation.
findings
provide
overview
recommendations
integrating
into
learning,
contributing
development
effective
AI-enhanced
environments
reveals
consensus
on
importance
incorporating
Common
themes
like
mentorship,
personalised
creativity,
emotional
intelligence,
higher-order
thinking
highlight
persistent
need
align
human-centred
educational
practices
capabilities
technologies.
Health Science Reports,
Год журнала:
2025,
Номер
8(2)
Опубликована: Янв. 29, 2025
ABSTRACT
Background/Aims
Since
the
emergence
of
generative
AI
(GenAI)
in
fall
2022,
its
impact
on
higher
education
has
been
significant
yet
under‐researched,
leading
to
mixed
reactions
among
nurse
educators,
ranging
from
enthusiasm
skepticism.
A
preliminary
search
seven
databases
found
no
scoping
reviews
specifically
that
addressed
educators'
concerns
about
using
GenAI.
Therefore,
this
study
aims
map
existing
literature
regarding
use
GenAI
education.
Inclusion
Criteria
Included
are
any
types
sources
(peer‐reviewed
and
nonpeer‐reviewed)
English
country
were
authored
by
an
academic
educator
reported
“academic
educators,”
“artificial
intelligence”
(such
as
GenAI,
Generative
AI,
ChatGPT,
large
language
models)
nursing
Articles
did
not
report
“nurse
concerns,”
or
focused
clinical
practice
excluded.
Methods
This
protocol
(see
PRISMA‐P
Appendix
1)
establishes
parameters
for
planned
review,
which
will
be
conducted
April
July
2024.
We
follow
Joanna
Briggs
Institute,
a
comprehensive
methodology,
ensure
rigorous
approach.
The
final
review
include
relevant
eight
published
Fall
2022
through
Data
PRISMA‐ScR
checklist
flow
diagram
(2020)
along
with
other
visual
diagrams
add
validity
our
findings.
An
inductive
analysis
approach
used
code
evolving
data,
identify
recurring
themes,
pinpoint
potential
gaps
literature.
Results
present
results,
inclusion
process,
data
analysis.
Conclusion
Our
potentially
provide
crucial
insights
into
pinpointing
within
literature,
providing
direction
future
research.
Review
Registration
was
registered
May
8,
2024,
Open
Science
Framework
(OSF).
registry
number
is
OSF.IO/SZ8WR.
registration
ensures
transparency
credibility
research
it
provides
public
record
design
methods.
Computers and Education Artificial Intelligence,
Год журнала:
2024,
Номер
7, С. 100273 - 100273
Опубликована: Июль 29, 2024
The
rapid
adoption
of
generative
AI
tools
such
as
ChatGPT
by
students
has
the
potential
to
disrupt
higher
education
sector,
with
concerns
being
raised
academics
about
threats
academic
integrity.
This
paper
contributes
pressing
discussion
responses
examining
students'
perceptions
and
use
assist
them
assessments.
Based
on
a
survey
among
337
Australian
university
students,
this
study
found
that
more
than
third
have
used
chatbot
for
assistance
an
assessment,
do
not
necessarily
perceive
breach
further
investigated
what
extent
different
psychosocial
factors
learning
motivations,
distress
or
resilience
are
associated
chatbots
in
order
ascertain
environmental
conditions
risk
driving
their
use.
Findings
suggest
sector
faces
challenge
only
defining
clear
policies
guidelines
ethical
academically
honest
ways
integrate
into
assessments,
but
also
rethink
design
assessment
pieces.
Journal of Educational Computing Research,
Год журнала:
2024,
Номер
62(7), С. 1896 - 1933
Опубликована: Авг. 27, 2024
The
use
of
generative
artificial
intelligence
(Gen-AI)
to
assist
college
students
in
their
studies
has
become
a
trend.
However,
there
is
no
academic
consensus
on
whether
Gen-AI
can
enhance
the
achievement
students.
Using
meta-analytic
approach,
this
study
aims
investigate
effectiveness
improving
and
explore
effects
different
moderating
variables.
A
total
28
articles
(65
independent
studies,
1909
participants)
met
inclusion
criteria
for
study.
results
showed
that
significantly
improved
students’
with
medium
effect
size
(Hedges’s
g
=
0.533,
95%
CI
[0.408,0.659],
p
<
.05).
There
were
within-group
differences
three
moderator
variables,
activity
categories,
sample
size,
generated
content,
when
content
was
text
(
0.554,
.05),
21–40
0.776,
learning
styles
0.600,
.05)
had
most
significant
improvement
student’s
achievement.
intervention
duration,
discipline
types,
assessment
tools
also
moderate
positive
impact
achievement,
but
any
This
provides
theoretical
basis
empirical
evidence
scientific
application
development
educational
technology
policy.
Advances in educational technologies and instructional design book series,
Год журнала:
2025,
Номер
unknown, С. 277 - 326
Опубликована: Фев. 6, 2025
This
chapter
explores
the
transformative
potential
of
Generative
Artificial
Intelligence
(GAI)
in
educational
assessment,
examining
its
impact
on
learners,
educators,
and
institutions.
Through
a
comprehensive
literature
review
analysis
existing
GAI-based
assessment
systems,
we
investigate
how
GAI
is
reshaping
traditional
practices,
enabling
more
personalized,
adaptive,
continuous
evaluation
student
learning.
The
discusses
major
challenges
opportunities
associated
with
GAI,
including
issues
data
privacy,
fairness,
evolving
role
educators.
We
also
examine
concrete
examples
applications
such
as
adaptive
learning
platforms
automated
grading
systems.
concludes
by
outlining
future
research
directions
considering
ethical
implications
widespread
adoption
education.
While
offers
for
enhancing
implementation
requires
careful
consideration
ethical,
pedagogical,
technical
Vysshee Obrazovanie v Rossii = Higher Education in Russia,
Год журнала:
2025,
Номер
34(2), С. 31 - 50
Опубликована: Фев. 26, 2025
The
issue
of
using
generative
artificial
intelligence
(GenAI)
in
education
is
the
focus
both
its
advocates
and
critics.
world
academic
community
trying
to
consider
rapidly
spreading
phenomenon,
determine
place
educational
process
work
out
regulatory
framework.
application
GenAI-powered
services
changes
conceptual
didactic
foundations
education.
In
order
predict
scenarios
university
development
timely
response
on
managerial
level,
needs
survey
data
use
tools
by
actors
–
staff
students.
paper
contributes
study
patterns
students
teachers.
authors
surveyed
(
N
=
450),
researchers
teaching
228)
Moscow
City
University.
greater
popularity
GenAI
among
a
more
discreet
position
teachers
determined
different
strategies
their
use.
complementary
function
active
strategy
teacher
does
not
change
essence
compared
students’
one.
accomplishment
written
assignments
with
help
as
most
common
transforms
conventional
understanding
responsibility
transparency
results.
findings
highlight
reconsideration
higher
nature
require
transform
practices
learning.
conclude
that
contradictory
attitudes
towards
assumption
ethical
norms
for
(higher)
education,
well
increasing
level
AI
literacy
