👂🎴 🕸️
Introduction
:::
Narrative
Primer
Artefact
:::
Narrative
AI
in
Education
:::
Summary
Once
upon
a
time
''
a
book
had
been
made
''
a
book
which
contained
all
the
other
books
''
including
its
own
construction
&
programming
manual
...<
br
>
Narrative
AI
in
Education
refers
to
the
use
of
artificial
intelligence
to
deliver
learning
experiences
through
storytelling
''
blending
traditional
educational
methods
with
advanced
''
personalized
digital
tools
.
This
approach
leverages
technologies
such
as
speech
-
to
-
text
''
text
-
to
-
speech
''
language
models
and
''
optionally
''
image
generation
to
create
interactive
''
voice
-
driven
narratives
that
adapt
to
each
learner
’
s
needs
''
preferences
''
and
abilities
.
Unlike
conventional
screen
-
based
learning
''
narrative
AI
fosters
engagement
through
immersive
storytelling
''
making
education
accessible
also
to
children
with
visual
impairment
or
from
screen
-
critical
communities
. 
<
div
>
By
integrating
speech
-
to
-
text
(
STT
)''
text
-
to
-
speech
(
TTS
)''
mid
-
sized
large
-
language
models
(
MLMs
)
and
sufficient
and
necessary
knowledge
base
stored
in
the
vector
database
''
the
NP
provides
a
personalized
and
interactive
learning
experience
. 
div
><
div
><
br
>
div
><
div
>
It
emphasizes
the
traditional
educational
practice
of
storytelling
''
enhanced
by
modern
AI
capabilities
''
to
promote
basic
literacy
''
arithmetic
''
musical
skills
and
uncorruptable
personality
.<
br
>
div
>
<
p
class
=
„
fragment
“
>
spectacular
things
are
already
happening
in
„
open
source
“
branch
of
AIED
p
><
p
class
=
„
fragment
“
>
with
USA
gradually
becoming
the
prey
of
the
dark
side
''
immediate
deployment
of
„
walled
garden
“
approaches
is
of
utmost
importance
p
><
p
class
=
„
fragment
“
>
all
„
bricks
“
to
build
Your
educational
„
cathedral
“
are
available
out
there
(
GitHub
''
Huggingface
)
and
ready
to
serve
p
><
p
class
=
„
fragment
“
>
the
future
will
be
more
weird
than
a
dream
and
the
key
to
that
dream
is
...
p
><
p
class
=
„
fragment
“
>...
education
p
>
We
use
mid
-
sized
(<
8
billion
parameters
)
large
language
models
derived
from
Llama
3
.
1
8B
 
and
Mistral
7b
base
models
.<
br
/><
br
/>
On
top
of
these
models
''
we
subsequently
train
specific
adapters
by
means
of
Low
Rank
Adaptation
(
LoRA
)
methodology
.<
br
/><
br
/>
Additionally
''
Retrieval
Augmented
Generation
(
RAG
)
is
also
deployed
in
order
to
increase
response
accuracy
.
A
Mid
-
Sized
Language
Model
(
MLM
)
is
a
generative
language
model
is
an
advanced
AI
system
comprising
of
maximum
10
billion
(
miliarden
!)
parameters
''
organized
into
multiple
layers
with
attention
mechanisms
.
These
layers
process
and
interpret
vast
amounts
of
text
data
''
while
the
attention
mechanisms
allow
the
model
to
focus
on
relevant
parts
of
the
input
.
This
architecture
enables
the
model
to
understand
and
generate
human
-
like
language
''
perform
nuanced
tasks
like
answering
complex
questions
''
writing
detailed
texts
''
and
engaging
in
sophisticated
conversations
''
leveraging
its
deep
learning
capabilities
.
Essentially
''
You
have
three
options
:<
br
><
p
class
=
„
fragment
“
>
fine
-
tuning
(
in
every
training
step
''
training
process
updates
billions
and
billions
of
parameters
)
p
><
p
class
=
„
fragment
“
>
training
a
LoRa
(
instead
of
updating
billions
of
parameters
''
You
update
just
few
millions
)
p
><
p
class
=
„
fragment
“
>
do
Retrieval
Augmented
Generation
p
>
„
I
“
-
Avatarization
is
the
process
whereby
a
living
human
H
consciously
creates
''
develops
''
fine
tunes
and
optimizes
(
his
|
her
)
own
generative
AI
avatar
datasets
&
models
.
<
br
><
br
>
That
is
''
using
datasets
(
mails
''
chat
transcripts
etc
.)
to
create
a
generative
AI
copy
of
one
'
s
self
(
an
„
I
-
Avatar
“
)
which
could
provide
information
in
situation
when
H
(
her
|
him
)
self
is
not
alive
anymore
.
On
a
machine
amsel
.
udk
.
ai
(
running
somewhere
in
this
room
)''
there
are
many
nice
Generative
AI
tools
installed
''
including
:<
br
><
p
class
=
„
fragment
“
>
text
-
generation
-
webui
web
-
based
interface
for
work
with
language
models
p
><
p
class
=
„
fragment
“
>
Training
PRO
extension
for
training
LoRas
for
Mistral
-
architecture
models
p
><
p
class
=
„
fragment
“
>
superbooga
-
v2
for
easy
RAG
prototyping
p
>
We
define
an
Extended
Educational
Environment
(
EEE
or
E3
)
as
an
immersive
and
interactive
XR
learning
environment
enriched
with
AI
-
driven
artifacts
and
avatars
.
These
avatars
and
artifacts
''
developed
using
tools
like
UnrealEngine
and
MetaHuman
''
each
possess
distinct
„
personalities
“
or
„
characters
“
reflecting
their
underlying
knowledge
bases
and
machine
learning
models
.<
br
>
Join
the
HuggingFace
udk
.
ai
community
!
Front
.
Educ
.''
2023
<
br
>
Sec
.
Digital
Education
<
br
>
Volume
8
-
2023
|
<
a
href
=
„
https
://
doi
.
org
/
10
.
3389
/
feduc
.
2023
.
1063337
“
>
https
://
doi
.
org
/
10
.
3389
/
feduc
.
2023
.
1063337
a
><
br
><
div
>
Proof
-
of
-
concept
of
feasibility
of
human
–
machine
peer
learning
for
German
noun
vocabulary
learning
<
br
>
div
>
Human
–
Machine
Peer
Learning
(
HMPL
)
is
a
proposal
that
is
positioned
at
the
very
frontier
between
educational
''
cognitive
''
and
computer
sciences
.
HMPL
'
s
core
precepts
which
I
introduced
in
my
2022
and
2023
papers
are
simple
:
<
br
/><
br
/><
div
style
=
„
text
-
align
:
center
;
“
>
Humans
and
machines
can
learn
together
.
div
><
div
style
=
„
text
-
align
:
center
;
“
>
Humans
and
machines
can
learn
from
each
other
.
div
>
In
the
second
Hromada
&
Kim
(
2023
)
article
''
we
describe
first
''
syllable
-
oriented
exercise
by
means
of
which
the
Primer
aimed
to
assist
one
5
-
year
-
old
pre
-
schooler
in
increase
of
her
reading
competence
.
The
pupil
went
through
sequence
of
exercises
composed
of
evaluation
and
learning
tasks
.
Consistently
with
previous
HMPL
study
''
we
observe
increase
of
both
child
'
s
reading
skill
as
well
as
of
machine
'
s
ability
to
accurately
process
child
'
s
speech
.<
br
>
<
div
>
Next
talk
:
 
Make
Your
Own
Not
-
so
-
large
-
language
-
model
 
@
State
Of
The
Art
(
s
)_
GenAI
Applied
_<
br
/>
July
5th
from
17
:
00
-
18
:
45
at
Gallerie
at
Medienhaus
(
Grunewaldstrasse
2
)<
br
/><
br
/>
Keywords
:
Large
Language
Models
''
Low
Rank
Adaptation
''
Retrieval
Augmented
Generation
''
Direct
Preference
Optimization
''
Embeddings
div
>
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