The
integration
of
Artificial
Intelligence
(AI)
in
dissertation
examination
presents
a
transformative
opportunity
for
higher
education
institutions
Zambia,
Rwanda,
and
Kenya.
As
student
enrollments
continue
to
rise,
universities
face
challenges
efficiently
evaluating
dissertations
while
maintaining
academic
integrity.
AI-driven
tools
offer
innovative
solutions
by
automating
tasks
such
as
plagiarism
detection,
language
quality
assessment,
contract
cheating
identification.
This
study
aims
explore
the
opportunities,
challenges,
impact
AI
adoption
assessment
across
selected
universities.
A
mixed-methods
research
design
was
employed,
incorporating
surveys,
semi-structured
interviews,
data
analysis
from
AI-assisted
evaluations
at
Copperbelt
University
(Zambia),
Jomo
Kenyatta
Agriculture
Technology
(Kenya).
Findings
indicate
that
enhances
efficiency
reducing
faculty
workload
improving
feedback
students.
However,
digital
literacy
gaps,
infrastructure
limitations,
concerns
over
AI’s
fairness
ethical
implications
hinder
full
adoption.
Despite
these
obstacles,
there
is
strong
support
among
students
integration,
provided
it
complemented
human
oversight.
concludes
has
significant
potential
revolutionize
evaluation
but
requires
investment
infrastructure,
training,
policy
frameworks
ensure
responsible
implementation.
Collaboration
universities,
policymakers,
technology
providers
essential
optimizing
upholding
rigour.
Information,
Год журнала:
2024,
Номер
15(10), С. 596 - 596
Опубликована: Сен. 30, 2024
This
paper
presents
a
novel
framework,
artificial
intelligence-enabled
intelligent
assistant
(AIIA),
for
personalized
and
adaptive
learning
in
higher
education.
The
AIIA
system
leverages
advanced
AI
natural
language
processing
(NLP)
techniques
to
create
an
interactive
engaging
platform.
platform
is
engineered
reduce
cognitive
load
on
learners
by
providing
easy
access
information,
facilitating
knowledge
assessment,
delivering
support
tailored
individual
needs
styles.
AIIA’s
capabilities
include
understanding
responding
student
inquiries,
generating
quizzes
flashcards,
offering
pathways.
research
findings
have
the
potential
significantly
impact
design,
implementation,
evaluation
of
AI-enabled
virtual
teaching
assistants
(VTAs)
education,
informing
development
innovative
educational
tools
that
can
enhance
outcomes,
engagement,
satisfaction.
methodology,
architecture,
services,
integration
with
management
systems
(LMSs)
while
discussing
challenges,
limitations,
future
directions
SSRN Electronic Journal,
Год журнала:
2023,
Номер
unknown
Опубликована: Янв. 1, 2023
The
incorporation
of
artificial
intelligence
(AI)
technologies,
with
a
particular
focus
on
generative
models
such
as
ChatGPT,
has
ushered
in
revolutionary
era
the
field
education,
fundamentally
altering
way
students
engage
mathematical
problem-solving.
This
scholarly
article
investigates
diverse
roles
and
obstacles
associated
harnessing
potential
ChatGPT
other
AI
tools
to
enhance
proficiency.
By
implementing
these
cutting-edge
educators
can
provide
tailored
learning
experiences
that
cater
wide
array
styles
paces.
enables
receive
immediate
feedback,
participate
interactive
problem-solving
dialogues,
access
step-by-step
solutions
align
their
individual
requirements.
Nevertheless,
integration
into
education
is
not
without
its
complexities.
A
central
challenge
revolves
around
ensuring
accuracy
dependability
AI-generated
solutions,
mitigating
any
biases
present
training
data,
maintaining
harmonious
balance
between
automation
human
intervention
process.
Ethical
concerns,
encompassing
matters
data
privacy
ethical
use
AI,
also
demand
meticulous
consideration.
Furthermore,
delves
cognitive
impacts
students,
scrutinizing
how
reliance
might
affect
critical
thinking
skills
grasp
fundamental
concepts.
It
explores
methodologies
aimed
at
fostering
synergistic
relationship
intelligence,
encouraging
development
robust
strategies
while
computational
capabilities
AI.
research
illuminates
promising
incorporating
notably
realm
mathematics
education.
addressing
challenges
adopting
nuanced
approach,
harness
power
craft
enriching
efficient
environments,
nurturing
generation
individuals
adept
well-equipped
tackle
intricacies
modern
world.
Assessment & Evaluation in Higher Education,
Год журнала:
2024,
Номер
49(6), С. 781 - 798
Опубликована: Янв. 17, 2024
Media
coverage
suggests
that
ChatGPT
can
pass
examinations
based
on
multiple
choice
questions
(MCQs),
including
those
used
to
qualify
doctors,
lawyers,
scientists
etc.
This
poses
a
potential
risk
the
integrity
of
examinations.
We
reviewed
current
research
evidence
regarding
performance
MCQ-based
in
higher
education,
along
with
recommendations
for
how
educators
might
address
challenges
and
benefits
arising
from
these
data.
53
studies
were
included,
covering
114
question
sets,
totalling
49014
MCQs.
Free
versions
upon
GPT-3/3.5
performed
better
than
random
guessing
but
failed
most
examinations,
performing
significantly
worse
average
human
student.
GPT-4
passed
was
par
subjects.
These
findings
indicate
all
summative
assessments
should
be
conducted
under
secure
conditions
restricted
access
similar
tools,
particularly
which
assess
foundational
knowledge.
The
research
creates
a
professional
certification
survey
to
test
large
language
models
and
evaluate
their
employable
skills.
It
compares
the
performance
of
two
AI
models,
GPT-3
Turbo-GPT3.5,
on
benchmark
dataset
1149
certifications,
emphasizing
vocational
readiness
rather
than
academic
performance.
achieved
passing
score
(>70%
correct)
in
39%
certifications
without
fine-tuning
or
exam
preparation.
demonstrated
qualifications
various
computer-related
fields,
such
as
cloud
virtualization,
business
analytics,
cybersecurity,
network
setup
repair,
data
analytics.
Turbo-GPT3.5
scored
100%
valuable
Offensive
Security
Certified
Professional
(OSCP)
exam.
also
displayed
competence
other
domains,
including
nursing,
licensed
counseling,
pharmacy,
teaching.
passed
Financial
Industry
Regulatory
Authority
(FINRA)
Series
6
with
70%
grade
Interestingly,
performed
well
customer
service
tasks,
suggesting
potential
applications
human
augmentation
for
chatbots
call
centers
routine
advice
services.
sensory
experience-based
tests
wine
sommelier,
beer
taster,
emotional
quotient,
body
reader.
OpenAI
model
improvement
from
Babbage
Turbo
resulted
median
60%
better-graded
less
few
years.
This
progress
suggests
that
focusing
latest
model's
shortcomings
could
lead
highly
performant
capable
mastering
most
demanding
certifications.
We
open-source
expand
range
testable
skills
improve
gain
emergent
capabilities.
Cell Reports Physical Science,
Год журнала:
2023,
Номер
4(11), С. 101672 - 101672
Опубликована: Ноя. 1, 2023
Large
language
models
like
ChatGPT
can
generate
authentic-seeming
text
at
lightning
speed,
but
many
journal
publishers
reject
as
authors
on
manuscripts.
Thus,
a
means
to
accurately
distinguish
human-generated
from
artificial
intelligence
(AI)-generated
is
immediately
needed.
We
recently
developed
an
accurate
AI
detector
for
scientific
journals
and,
herein,
test
its
ability
in
variety
of
challenging
situations,
including
human
wide
chemistry
journals,
the
most
advanced
publicly
available
model
(GPT-4),
important,
generated
using
prompts
designed
obfuscate
use.
In
all
cases,
and
was
assigned
with
high
accuracy.
ChatGPT-generated
be
readily
detected
journals;
this
advance
fundamental
prerequisite
understanding
how
automated
generation
will
impact
publishing
now
into
future.
Higher Education,
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 24, 2024
Abstract
Artificial
intelligence
(AI)
chatbots
trained
on
large
language
models
are
an
example
of
generative
AI
which
brings
promises
and
threats
to
the
higher
education
sector.
In
this
study,
we
examine
emerging
research
area
in
(HE),
focusing
specifically
empirical
studies
conducted
since
release
ChatGPT.
Our
review
includes
23
articles
published
between
December
2022
2023
exploring
use
HE
settings.
We
take
a
three-pronged
approach
data.
first
state
field
HE.
Second,
identify
theories
learning
used
Third,
scrutinise
discourses
framing
latest
work
chatbots.
findings
contribute
better
understanding
eclectic
nascent
HE,
lack
common
conceptual
groundings
about
human
learning,
presence
both
dystopian
utopian
future
role
