Student self-reflection as a tool for managing GenAI use in large class assessment
Discover Education,
Год журнала:
2025,
Номер
4(1)
Опубликована: Март 26, 2025
Written
assignments
for
large
classes
pose
a
far
more
significant
challenge
in
the
age
of
GenAI
revolution.
Suggestions
such
as
oral
exams
and
formative
assessments
are
not
always
feasible
with
many
students
class.
Therefore,
we
conducted
study
South
Africa
involved
280
Honors
to
explore
usefulness
Turnitin's
AI
detector
conjunction
student
self-reflection.
Using
Mixed
Methods
Research
(MMR)
approach,
analysed
data
generated
from
Turnitin
reports,
our
grading
rubrics,
qualitative
The
findings
show
that
incorporating
self-reflection
into
supports
ethical
use
improves
transparency
lecturers
need
decision-making.
A
declaration
form
allowed
be
upfront
about
using
Generative
Artificial
Intelligence
tools.
We
found
who
can
reflect
on
their
learning
relied
less
content.
However,
high
detected
scores
(>
20%)
did
adequately
how
tools
supported
could
give
credible
explanations
use.
contribute
body
knowledge
by
providing
academics
examples
responsibly
handling
AI-detected
large-class
settings.
present
guided
an
support
help
make
decisions
when
grading.
also
decision
tree
graders
evaluating
assessments.
Язык: Английский
Artificial Intelligence Tools Usage: A Structural Equation Modeling of Undergraduates’ Technological Readiness, Self-Efficacy and Attitudes
Journal for STEM Education Research,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 23, 2024
Язык: Английский
The Application of AI in Chemistry Learning: Experiences of Secondary School Students in Zimbabwe
European Journal of Mathematics and Science Education,
Год журнала:
2025,
Номер
6(1), С. 1 - 15
Опубликована: Март 12, 2025
This
study
investigated
the
integration
of
artificial
intelligence
(AI)
tools
into
secondary
school
chemistry
education
in
Zimbabwe,
assessing
their
impact
on
student
engagement
and
academic
performance.
Grounded
Vygotsky’s
Sociocultural
Theory
Cognitive
Load
Theory,
research
employed
a
mixed-methods
approach
within
pragmatic
framework.
Quantitative
data
were
collected
through
pre-test
post-test
assessments
structured
surveys,
comparing
an
experimental
group
using
AI
with
control
employing
traditional
methods.
Qualitative
from
teacher
interviews
classroom
observations
analysed
thematically.
ANCOVA
analysis
revealed
statistically
significant
difference
scores
between
groups,
F
(1,
117)
=
188.86,
p
<
.005,
η²
0.617,
demonstrating
large
effect
size
Students
exhibited
mean
improvement
20%,
controlling
for
differences.
Additionally,
interaction
effects
use
gender
(F
(1,115)
0.17,
.684)
as
well
prior
knowledge
0.05,
.829)
not
significant.
Furthermore,
85%
reported
higher
levels,
confirming
AI’s
role
fostering
motivation
conceptual
understanding.
facilitated
personalized
learning
paths,
interactive
simulations,
real-time
feedback,
optimizing
cognitive
efficiency
deep
learning.
Despite
these
advantages,
challenges
emerged,
including
limited
internet
access,
insufficient
technological
resources,
lack
training,
curriculum
difficulties.
These
barriers
highlight
need
strategic
investments
digital
infrastructure,
professional
development
educators,
revisions
to
fully
integrate
education.
The
findings
underscore
transformative
potential
STEM
developing
nations.
Addressing
infrastructural
pedagogical
is
critical
maximizing
AI's
impact,
ensuring
equitable
long-term
sustainability
educational
innovation.
Язык: Английский
Evaluating Artificial Intelligence Anxiety Among Pre-Service Teachers in University Teacher Education Programs
Journal of Mathematics Instruction Social Research and Opinion,
Год журнала:
2024,
Номер
4(1), С. 1 - 18
Опубликована: Дек. 2, 2024
Adopting
artificial
intelligence
(AI)
in
education
for
various
purposes
has
become
prominent
and
raised
many
user
concerns.
The
concerns,
apprehension,
or
fear
that
comes
with
the
use
of
AI
are
referred
to
as
Artificial
Intelligence
Anxiety
(AI
anxiety).
Undergraduates
most
frequent
users
higher
education.
This
study
assessed
anxiety
among
pre-service
teachers.
A
survey
conducted
online
was
used
data
collection.
sample
1067
teachers
mathematics,
science,
technology
teacher
programs
were
purposefully
selected
study.
questionnaire
collect
regarding
teachers'
AI-Anxiety
six
dimensions:
intimidation,
societal
impact,
job
displacement,
technological
dependence,
dread,
ethical
instrument
hosted
through
Google
Forms,
gathered
analyzed
descriptively
(percentage,
mean,
standard
deviation)
inferentially
(ANOVA
regression
analysis).
reveals
a
moderate
level
Levels
vary
across
dimensions,
five
found
be
high
while
only
one
at
level.
It
also
significant
variations
based
on
their
area
speciality.
Also,
identified
no
influences
demographic
characteristics
teachers,
emphasizing
gender.
Thus,
educators
institutions
should
urgently
embark
literacy
improve
technologies
ameliorate
anxiety.
Язык: Английский