Special Issue: Neu osophy in A i icial In elligence: Ad ances and Applica ions om he Join
Con e ences o BARNA Managemen School (Dominican Republic) and Uni e sidad del
T abajo del U uguay (Augus 6–8, 2025), Vol. 92, 2025
Débo a Lucia Ponce Ri e a, Ma yu i Y onne Guale Gómez, Rudy Ga cia Cobas,Ca los Robe o Humanan e Cab e a, Isaac
Roge Ma ínez. . Au oma ed Valida ion wi h AI o Neu osophic Pli hogenic Hypo heses in he Imp o emen o Mul ig ade
Li e acy Imp o emen .
Uni e si y o New Mexico
Au oma ed AI Valida ion o Neu osophic Pli ho-
genic Hypo heses in Mul ig ade Li e acy Imp o emen
Débo a Lucia Ponce Ri e a1*, Ma yu i Y onne Guale Gómez1, Rudy Ga cia Cobas1, Ca los Robe o Huma-
nan e Cab e a1, and Isaac Roge Ma ínez1
1 Uni e sidad Boli a iana del Ecuado (UBE), Ecuado . dlponce @ube.edu.ec (D.L.P.R.); [email protected]
(M.Y.G.G.); ga ciac_[email p o ec ed] (R.G.C.); c humanan [email protected] (C.R.H.C.); [email p o ec ed] (I.R.M.).
Abs ac . Li e acy is acqui ed in mul ig ade class ooms in complica ed scena ios because o a ying li e acy com-
pe encies and abili ies and a ied esou ces and ma e ials. Thus, i 's ha d o de e mine i ce ain eaching in e en-
ions wo k. This is also a g owing conce n, a imely conside a ion, because as he ins i u ions y o be e he Qual-
i y o Educa ion and p e en lea ning lags o mul i a ia e class ooms a e conce ned. Ye he li e a u e con ains
gaps whe e no di ec a emp o s abilize eaching in e en ions is made despi e he indings o many s udies gen-
e a ing didac ic in e en ions h ough he p oceedings. Thus, his s udy ills he gap wi h an app oach based upon
hypo hesis gene a ion ia neu osophics pli hogenic heo y and in ulne abili y a i ma ion ia non-p og amming
AIs o simul aneously e alua e mul iple, some imes con adic o y, indings o any eaching in e en ion. The e-
sul s indica e ha while combina ion educes subjec i i y a one le el, a ew le els up i co ec ly iden i ies A, B,
and C as posi i e e inemen s o emedia ion owa d mo e app op ia e u u e e inemen s. Thus, his s udy p e-
sen s a heo e ically d i en ye p ac ically applicable a enue o be e Educa ional in e en ion in he mul i-g ade
class oom as well as AI exploi able s eps o ANY subjec a ea.
Keywo ds: Educa ional AI, Pli hogenic Hypo heses, Neu osophic, Mul ig ade, Li e acy, Au oma ed Valida ion,
Teaching Imp o emen .
1. In oduc ion
The in eg a ion o a i icial in elligence (AI) in o mul i-g ade educa ional p ocesses, pa icula ly in
li e acy imp o emen , is o p essing ele ance in he cu en pedagogical landscape; ecen esea ch
indica es ha AI has he po en ial o o e au oma ed assessmen s, immedia e eedback, and pe sonal-
ized eaching adap a ions [1], while symbolic and explainable AI ools ha e led o mo e eliable and
in e p e able models [2]. In his con ex , explo ing how such echnologies can alida e pedagogical hy-
po heses is essen ial and imely.
mul ig ade eaching has oscilla ed be ween eache -cen e ed app oaches and collabo a i e s a egies,
howe e , he ne wo k o a iables - di e se hy hms, limi ed esou ces, cul u al he e ogenei y - has
demanded mo e obus analy ical me hods. In u n, he neu osophic heo y, concei ed by
Sma andache as a amewo k o manage unce ain y h ough iples (T, I, F) - u h, inde e minacy and
alsi y - has been applied in mul iple domains, om logic o s a is ics [3 ]– [5]. Mo eo e , ad ances in
pli hogenici y ha e expanded his pa adigm, b inging oge he mul iple simul aneous a ibu es in
complex analyses [6].
Howe e , a me hodological gap pe sis s: he e a e ew s a egies ha use AI o sys ema ically ali-
da e neu osophic pli hogenic hypo heses in mul ig ade se ings. How can echnology assis in he au-
oma ic es ing o such in ica e educa ional hypo heses? This ques ion emains unanswe ed in he spe-
cialized li e a u e o da e, highligh ing a c i ical gap a he in e sec ion o explainable AI, neu osophic
modeling, and educa ional assessmen . The e o e, his s udy p oposes o employ AI as an assis ed
Neu osophy in A i icial In elligence: Ad ances and Applica ions om he Join Con e ences o BARNA Managemen
School (Dominican Republic) and Uni e sidad del T abajo del U uguay (Augus 6–8, 2025), Vol. 92, 2025
Débo a Lucia Ponce Ri e a, Ma yu i Y onne Guale Gómez, Rudy Ga cia Cobas,Ca los Robe o Humanan e Cab e a, Isaac
Roge Ma ínez. . Au oma ed Valida ion wi h AI o Neu osophic Pli hogenic Hypo heses in he Imp o emen o Mul ig ade
Li e acy Imp o emen .
632
analysis ool, wi hou equi ing complex p og amming. Neu osophic pli hogenic hypo heses will be
o mula ed and alida ed using "no-code" AI pla o ms ha in eg a e quali a i e and quan i a i e anal-
ysis in an accessible isual en i onmen . This is a p agma ic app oach, allowing he esea che o design,
in e p e , and adjus he hypo heses wi hou elying on so wa e de elopmen .
The me hodological app oach balances heo e ical igo and p ac ical u ili y: pli hogenic hypo heses
cap u e mul iple a ibu es—such as ype o wo kshee , equency o use, collabo a ion, and mo i a-
ion—while he neu osophic componen conside s he unce ain y inhe en in educa ional in o ma ion.
In pa allel, AI ools ex ac pa e ns, sugges weigh s, and quan i y he le el o u h, ambigui y, and
alsi y o each hypo hesis o mula ed. The expec ed esul s consis o he gene a ion o au oma ic ali-
da ion maps, e ealing which educa ional aspec s mos signi ican ly in luence li e acy p og ess, as well
as he iden i ica ion o con ex s whe e he hypo hesis is inconclusi e. Such da a will allow o he o -
mula ion o well- ounded eaching ecommenda ions leading o conc e e ins uc ional adjus men s .
Finally, he main objec i e o his s udy is o demons a e ha he alida ion o complex pedagogical
hypo heses can be pe o med in an au oma ed, in e p e i e, and accessible manne , wi hou p og am-
ming, using AI applied o neu osophic pli hogenic models. The ul ima e goal is o en ich mul ig ade
educa ional p ac ice wi h obus , adap able, and a o dable analy ical ools.
2. P elimina ies
2.1. AI in Mul ig ade Li e acy Imp o emen .
The inco po a ion o a i icial in elligence (AI) in mul i-g ade educa ional con ex s ep esen s a
unique oppo uni y o pe sonalize li e acy eaching in he e ogeneous class ooms, whe e s uden s o
di e en le els sha e esou ces and physical space. A a ime when inno a ion mus go beyond adi-
ional me hods, AI can ac as an adap i e u o , adjus ing con en o di e se lea ning hy hms and cog-
ni i e s yles. In ac , in o he a eas o educa ion, hyb id AI sys ems—which combine human supe ision
wi h au oma ed in elligence—ha e been shown o p omo e deepe and mo e sel - egula ed lea ning [7]
by de ec ing speci ic di icul ies, iden i ying pa e ns o comp ehension, and gene a ing didac ic in e -
en ions ha eed a cycle o con inuous imp o emen . Howe e , much o he specialized li e a u e on
AI in li e acy ocuses on single-g ade en i onmen s , omi ing he complexi y o wo king wi h mul i-
g ade g oups, p esen in many u al and low-income con ex s, whe e his modali y is common and
poses pa icula challenges [8]. In addi ion, many echnological solu ions equi e ad anced p og am-
ming, which es ic s hei use by eache s wi hou echnical knowledge, and he sca ci y o accessible
pla o ms limi s he exploi a ion o he po en ial o AI in hese scena ios. In con as , when well de-
signed, AI can become a s a egic ally, o e ing adap i e eedback, de ec ing speci ic needs and allowing
eache s o play a ole mo e ocused on lea ning managemen han on echnical asks [9], hus s eng h-
ening hei pedagogical ole. Howe e , i s implemen a ion equi es conside ing challenges such as eq-
ui y in access, cul u al adequacy o con en and anspa ency in adap a ion c i e ia, in addi ion o e hical
aspec s such as da a p o ec ion, inclusion and mi iga ion o algo i hmic biases. Despi e his, i s po en ial
bene i s a e signi ican : p ope ly managed AI encou ages he de elopmen o pe sonalized eading and
w i ing skills, s imula es au onomy and ees up ime o collabo a i e and c ea i e ac i i ies [10]. In e-
g a ing AI in o mul ig ade li e acy imp o emen no only ep esen s a echnological inno a ion bu also
a pa adigm shi ha ans o ms eache s in o acili a o s o adap i e p ocesses and s uden s in o ac i e
pa icipan s in hei lea ning. The e o e, i is essen ial o p omo e AI solu ions ha a e accessible, e hical,
and adap ed o he mul ig ade con ex , hus helping o b idge he gap be ween u ban single-g ade en-
i onmen s and mo e complex educa ional eali ies. Ul ima ely, he esponsible adop ion o AI in mul-
ig ade class ooms p omises educa ional ans o ma ion, p o ided i is accompanied by solid pedagog-
ical amewo ks, eache aining, and app op ia e echnological esou ces, so ha au oma ion comple-
men s, a he han eplaces, human expe ise in eaching.
Neu osophy in A i icial In elligence: Ad ances and Applica ions om he Join Con e ences o BARNA Managemen
School (Dominican Republic) and Uni e sidad del T abajo del U uguay (Augus 6–8, 2025), Vol. 92, 2025
Débo a Lucia Ponce Ri e a, Ma yu i Y onne Guale Gómez, Rudy Ga cia Cobas,Ca los Robe o Humanan e Cab e a, Isaac
Roge Ma ínez. . Au oma ed Valida ion wi h AI o Neu osophic Pli hogenic Hypo heses in he Imp o emen o Mul ig ade
Li e acy Imp o emen .
633
2.2. Pli hogenic P obabili y
Neu osophic (o inde e mina e) da a a e cha ac e ized by inhe en agueness, lack o cla i y, in-
comple eness, pa ial unknowns, and con lic ing in o ma ion [11,15]. Da a can be classi ied as quan i a-
i e (me ic), quali a i e (ca ego ical), o a combina ion o bo h. Pli hogenic a iable da a [16] desc ibe
he connec ions o co ela ions be ween neu osophic a iables. A neu osophic a iable [17,18], which
can be a unc ion o ope a o , ea s neu osophic da a in i s a gumen s, i s alues, o bo h. Complex
p oblems o en equi e mul iple measu emen s and obse a ions due o hei mul idimensional na u e,
such as he measu emen s needed in scien i ic in es iga ions. Neu osophic a iables may exhibi de-
pendence, independence, pa ial dependence, pa ial independence, o pa ial inde e minacy as in sci-
ence [19].
A Pli hogenic Se [20, 21] is a non-emp y se 𝑃whose elemen s wi hin he domain o discou se 𝑈( 𝑃 ⊆
𝑈) a e cha ac e ized by one o mo e a ibu es 𝐴1, 𝐴2,⋯,𝐴𝑚, whe e m is a leas 1. whe e each a ibu e
can ha e a se o possible alues wi hin he spec um 𝑆o alues (s a es), such ha 𝑆i can be a ini e,
in ini e, disc e e, con inuous, open o closed se .
Each elemen 𝑥 ∈ 𝑃is cha ac e ized by all possible alues o he a ibu es ound wi hin he se 𝑉 =
{𝜈1,𝜈2,⋯,𝜈𝑛 }. The alue o an a ibu e has a deg ee o membe ship 𝑑(𝑥,𝑣)in an elemen 𝑥o he se .𝑃,
based on a speci ic c i e ion . The deg ee o membe ship can be di use, di use in ui ionis o neu o-
sophic, among o he s [22 ] .
Tha means,
∀𝑥 ∈ 𝑃,𝑑: 𝑃 × 𝑉 → 𝒫 ([0,1]𝑧 ) (1)
Whe e𝑑(𝑥,𝑣) ⊆ [0,1]𝑧 and 𝒫 ([0,1]𝑧 )is he powe se o [0,1]𝑧.𝑧 = 1 ( he di use deg ee o belong-
ing), 𝑧 = 2( he in ui ionis di use deg ee o belonging) o 𝑧 = 3 ( he neu osophic deg ee o belong-
ing).
pli hogenic [23], de i ed om he analysis o pli hogenic a iables, ep esen s a mul idimensional
p obabili y (" pli ho " meaning "many" and synonym o "mul i"). I can be conside ed a p obabili y com-
posed o subp obabili ies, whe e each subp obabili y desc ibes he beha io o a speci ic a iable. The
e en unde s udy is assumed o be in luenced by one o mo e a iables , each ep esen ed by a p oba-
bili y dis ibu ion (densi y) unc ion (PDF).
Conside an e en E in a gi en p obabili y space, ei he classical o neu osophic, de e mined by 𝑛 ≥
2 a iables 𝑣1,𝑣2,…,𝑣𝑛, deno ed as 𝐸(𝑣1,𝑣2,…,𝑣𝑛). The mul i a ia e p obabili y o e en E occu ing,
called MVP(E), is based on mul iple p obabili ies. Speci ically, i depends on he p obabili y o e en E
occu ing wi h espec o each a iable: 𝑃1(𝐸(𝑣1)) o a iable 𝑣1, 𝑃2(𝐸(𝑣2)) o a iable 𝑣2, e c. The e-
o e, 𝑀𝑉𝑃(𝐸(𝑣1,𝑣2,…,𝑣𝑛))is ep esen ed as (𝑃1(𝐸(𝑣1)),𝑃2(𝐸(𝑣2)),…,𝑃𝑛(𝐸(𝑣𝑛))). The a iables
𝑣1,𝑣2,…,𝑣𝑛, and p obabili ies 𝑃1,𝑃2,…,𝑃𝑛, can be classical o ha e some deg ee o inde e minacy [24].
To make he ansi ion om pli hogenic neu osophic p obabili y (PNP) o uni a ia e neu osophic
p obabili y UNP, we use he conjunc ion ope a o [25]:
𝑈𝑁𝑃(𝑣1, 𝑣2,..., 𝑣𝑛) = 𝑣1⋀ 𝑣𝑛
𝑛
𝑖=1 ( 2 )
∧ In his con ex , i is a neu osophic conjunc ion ( -no m). I we ake∧𝑝 as he pli hogenic conjunc-
ion be ween p obabili ies o he PNP ype, whe e (𝑇𝐴,𝐼𝐴,𝐹𝐴)∧𝑝(𝑇𝐵,𝐼𝐵,𝐹𝐵)=(𝑇𝐴∧𝑇𝐵,𝐼𝐴∨𝐼𝐵,𝐹𝐴∨𝐹𝐵),
such ha ∧is he minimum -no m o uzzy logic and ∨ he maximum -no m [26, 27].
a. Fo mula e he hypo hesis
S a by explici ly s a ing he hypo hesis you in end o es . Make su e i indica es a cause-and-e -
ec ela ionship be ween he a iables. Fo example, "Mo e s udy ime leads o highe es sco es."
b. Iden i y key a iables
Iden i y he independen a iable, which is he cause, and he dependen a iable, which is he e -
ec , in you hypo hesis. This helps di ec you esea ch ques ions owa d he exac ela ionship you
need o in es iga e.
c. Fo mula e speci ic esea ch ques ions
Neu osophy in A i icial In elligence: Ad ances and Applica ions om he Join Con e ences o BARNA Managemen
School (Dominican Republic) and Uni e sidad del T abajo del U uguay (Augus 6–8, 2025), Vol. 92, 2025
Débo a Lucia Ponce Ri e a, Ma yu i Y onne Guale Gómez, Rudy Ga cia Cobas,Ca los Robe o Humanan e Cab e a, Isaac
Roge Ma ínez. . Au oma ed Valida ion wi h AI o Neu osophic Pli hogenic Hypo heses in he Imp o emen o Mul ig ade
Li e acy Imp o emen .
634
B eak he hypo hesis down in o p ecise esea ch ques ions ph ased as "Does X cause Y?" This allows
o a ho ough and ocused examina ion o he pos ula ed co ela ion.
d. Conduc sen imen analysis on scien i ic li e a u e.
To pe o m a sen imen analysis on a esea ch pape and quan i y he occu ences o "Yes," "Possi-
bili y/Unce ain y," and "No," a sen imen analysis ool o scien i ic s a emen s is needed. In his case,
we used Consensus Me e algo i hms o ca ego ize he s a emen s in o h ee dis inc g oups: Posi i e
(a i ma i e), Unce ain y (possibili y o unce ain y), and Nega i e (nega i e).
e. Fo mula e neu osophic p obabilis ic hypo heses
De e mine he easons o each ca ego y o cons uc he neu osophic p obabili y hypo hesis (T, I,
F), whe e T deno es he u h alue, I ep esen s inde e minacy, and F indica es alsi y.
. Calcula e he pli hogenic neu osophic p obabili y (PNP)
Using he neu osophic p obabili ies assigned o each ques ion, he uni a ia e neu osophic p oba-
bili y (UNP) is calcula ed o assess he s eng h o he o e all hypo hesis. This p ocess in ol es combin-
ing he sepa a e p obabili ies o p o ide a comp ehensi e assessmen o he o e all hypo hesis.
𝑈𝑁𝑃(𝑣1, 𝑣2,..., 𝑣𝑛)= (𝑀𝑖𝑛(𝑡1, 𝑡𝑛,…,𝑡𝑛), 𝑀𝑎𝑥(𝑖1, 𝑖𝑛,…,𝑖𝑛), 𝑀𝑎𝑥(𝑓1, 𝑓𝑛,…,𝑓𝑛)) (3)
Whe e:
𝑇1, 𝑇2,…,𝑇𝑛: a e he u h p obabili y alues o each ques ion.
𝐼1, 𝐼2,…,𝐼𝑛: a e he p obabili y alues o inde e minacy o each ques ion.
𝐹1, 𝐹2,…,𝐹𝑛: a e he p obabili y alues o alsehood o each ques ion
g. Analyze he alidi y o he gene al hypo hesis.
In his case, he nega ion o NPH is ep esen ed as [28]:
(𝑇,𝐼,𝐹) = (𝐹,𝐼,𝑇) (4)
This s ep in ol es analyzing he nega ed neu osophic p obabili ies o assess he o e all s eng h
and eliabili y o he gene al hypo hesis. By e alua ing he le els o alsi y, unce ain y, and e aci y,
one can de e mine he deg ee o which he hypo hesis is alid, ambiguous, o inco ec acco ding o he
scien i ic li e a u e.
3. Case s udy.
In he con ex o esea ch on he op imiza ion o li e acy ins uc ion in mul ig ade se ings, a me h-
odological app oach based on neu osophic logic is applied o e alua e a complex hypo hesis. This
me hod add esses he agueness, unce ain y, and con adic ion inhe en in pedagogical da a, p o id-
ing a quan i a i e and quali a i e assessmen o he hypo hesis's alidi y.
a. Fo mula ion o he Hypo hesis
The cen al hypo hesis o his s udy is ha he in eg a ion o non-p og amming a i icial in elligence
(AI) ools o he au oma ed alida ion o neu osophic pli hogenic models signi ican ly imp o es
he e ec i eness o li e acy eaching s a egies in mul ig ade class ooms. This app oach allows o
mo e p ecise iden i ica ion o key ac o s ha in luence lea ning, educes subjec i i y in assessmen , and
acili a es as e and mo e in o med pedagogical adjus men s.
b. Iden i ica ion o Key Va iables
• Independen Va iable: Applica ion o a me hodological amewo k ha combines au oma ed
alida ion wi h AI and neu osophic pli hogenic models.
• Dependen Va iable: E ec i eness and accu acy in assessing he impac o pedagogical s a e-
gies on imp o ing li e acy in mul ig ade se ings.
Neu osophy in A i icial In elligence: Ad ances and Applica ions om he Join Con e ences o BARNA Managemen
School (Dominican Republic) and Uni e sidad del T abajo del U uguay (Augus 6–8, 2025), Vol. 92, 2025
Débo a Lucia Ponce Ri e a, Ma yu i Y onne Guale Gómez, Rudy Ga cia Cobas,Ca los Robe o Humanan e Cab e a, Isaac
Roge Ma ínez. . Au oma ed Valida ion wi h AI o Neu osophic Pli hogenic Hypo heses in he Imp o emen o Mul ig ade
Li e acy Imp o emen .
635
c. Fo mula ion o Speci ic Resea ch Ques ions
To b eak down he gene al hypo hesis, he ollowing esea ch ques ions a e posed:
1. Q1: Do inno a i e pedagogical s a egies di ec ly imp o e li e acy skills in mul ig ade se ings?
2. Q2: Does he he e ogenei y o academic le els in a mul ig ade class oom ep esen a signi ican
obs acle o s anda dized assessmen o li e acy?
3. Q3: A e no-code AI ools obus enough o eliably alida e complex educa ional hypo heses?
4. Q4: Do neu osophic pli hogenic models adequa ely cap u e he unce ain y and con adic ions
inhe en in da a om eal-li e educa ional se ings?
5. Q5: Does sys ema ic, da a-d i en assessmen lead o mo e e ec i e and imely pedagogical ad-
jus men s by eache s?
d. Sen imen Analysis on Scien i ic Li e a u e
A simula ed sen imen analysis was conduc ed on he scien i ic li e a u e ele an o each esea ch ques-
ion. Using a ca ego iza ion algo i hm, he s udies' posi ions we e classi ied as Posi i e (Yes) , Unce ain
(Possibili y/Unce ain y) , and Nega i e (No) . The esul s a e summa ized below.
Table 1: Sen imen Analysis and Assigned Neu osophic P obabili ies
Cha 1: Dis ibu ion o neu osophic p obabili ies by esea ch ques ion
Ask
Posi i e (T)
Inde e minacy (I)
Nega i e (F)
P obabili y (T, I, F)
Q1
0.80
0.20
0.00
(0.80, 0.20, 0.00)
Q2
0.85
0.10
0.05
(0.85, 0.10, 0.05)
Q3
0.65
0.25
0.10
(0.65, 0.25, 0.10)
Q4
0.70
0.30
0.00
(0.70, 0.30, 0.00)
Q5
0.90
0.05
0.05
(0.90, 0.05, 0.05)

Neu osophy in A i icial In elligence: Ad ances and Applica ions om he Join Con e ences o BARNA Managemen
School (Dominican Republic) and Uni e sidad del T abajo del U uguay (Augus 6–8, 2025), Vol. 92, 2025
Débo a Lucia Ponce Ri e a, Ma yu i Y onne Guale Gómez, Rudy Ga cia Cobas,Ca los Robe o Humanan e Cab e a, Isaac
Roge Ma ínez. . Au oma ed Valida ion wi h AI o Neu osophic Pli hogenic Hypo heses in he Imp o emen o Mul ig ade
Li e acy Imp o emen .
636
e. and . Calcula ion o he Pli hogenic Neu osophic P obabili y (PNP)
To ob ain a uni ied assessmen o he gene al hypo hesis, he Uni a ia e Neu osophic P obabili y
(UNP) is calcula ed om he p obabili ies o each ques ion. The neu osophic pli hogenic conjunc ion
ope a o is used, de ined by he o mula:
𝑼𝑵𝑷(𝑸₁,...,𝑸ₙ) = (𝒎𝒊𝒏(𝑻₁,...,𝑻ₙ),𝒎𝒂𝒙(𝑰₁,...,𝑰ₙ),𝒎𝒂𝒙(𝑭₁,...,𝑭ₙ))
The calcula ion is de ailed s ep by s ep below.
S ep 1: Calcula ing he Deg ee o T u h (T)
The deg ee o u h o he UNP is he minimum o he deg ees o u h o all he ques ions.
• Values o T u h: {𝑇₁ = 0.80,𝑇₂ = 0.85,𝑇₃ = 0.65,𝑇₄ = 0.70,𝑇₅ = 0.90}
• Calcula ion:𝑇𝑈𝑁𝑃 = min(0.80,0.85,0.65,0.70,0.90)
• T ue Resul (𝑻): 𝟎.𝟔𝟓
S ep 2: Calcula ion o he Deg ee o Inde e minacy (I)
The deg ee o inde e minacy o he UNP is he maximum o he deg ees o inde e minacy o all he
ques ions.
• Inde e minacy Values:{𝐼₁ = 0.20,𝐼₂ = 0.10,𝐼₃ = 0.25,𝐼₄ = 0.30,𝐼₅ = 0.05}
• Calcula ion:𝐼𝑈𝑁𝑃 = max(0.20,0.10,0.25,0.30,0.05)
• Inde e minacy Resul (𝑰): 𝟎.𝟑𝟎
S ep 3: Calcula ing he Deg ee o Falsehood (F)
The deg ee o alsi y o he UNP is he maximum o he deg ees o alsi y o all he ques ions.
• Falsehood Values:{𝐹₁ = 0.00,𝐹₂ = 0.05,𝐹₃ = 0.10,𝐹₄ = 0.00,𝐹₅ = 0.05}
• Calcula ion:𝐹𝑈𝑁𝑃 = max(0.00,0.05,0.10,0.00,0.05)
• Falsehood Resul (𝑭): 𝟎.𝟏𝟎
Final Resul o he UNP
The Uni a ia e Neu osophic P obabili y (UNP) o he gene al hypo hesis is:
𝑼𝑵𝑷 = (𝟎.𝟔𝟓,𝟎.𝟑𝟎,𝟎.𝟏𝟎)
Table 2: Summa y o Neu osophic Calcula ions
Componen
Ope a ion
Inpu Values
Resul
T u h (T)
Minimum
0.80, 0.85, 0.65, 0.70, 0.90
0.65
Inde e minacy (I)
Maximum
0.20, 0.10, 0.25, 0.30, 0.05
0.30
Falsehood (F)
Maximum
0.00, 0.05, 0.10, 0.00, 0.05
0.10
Neu osophy in A i icial In elligence: Ad ances and Applica ions om he Join Con e ences o BARNA Managemen
School (Dominican Republic) and Uni e sidad del T abajo del U uguay (Augus 6–8, 2025), Vol. 92, 2025
Débo a Lucia Ponce Ri e a, Ma yu i Y onne Guale Gómez, Rudy Ga cia Cobas,Ca los Robe o Humanan e Cab e a, Isaac
Roge Ma ínez. . Au oma ed Valida ion wi h AI o Neu osophic Pli hogenic Hypo heses in he Imp o emen o Mul ig ade
Li e acy Imp o emen .
637
Cha 2: Visualiza ion o he componen s o he inal UNP
g. Analysis o he Validi y o he Gene al Hypo hesis
The esul 𝑈𝑁𝑃 = (0.65,0.30,0.10)is in e p e ed as ollows:
• Deg ee o T u h (T = 0.65): The e is 65% e idence o consensus in he scien i ic li e a u e sup-
po ing he hypo hesis. This is a mode a ely high alue, sugges ing ha he hypo hesis is plau-
sible and well- ounded.
• Deg ee o Inde e minacy (I = 0.30): The e is 30% unce ain y, ambigui y, o lack o consensus.
This indica es ha he e a e aspec s o he hypo hesis ha a e no ully esol ed o o which
he e idence is inconclusi e.
• Deg ee o Falsehood (F = 0.10): The e is 10% e idence ha con adic s he hypo hesis. This is a
low alue, indica ing ha he e a e ew di ec objec ions o e u a ions o he gene al p oposi-
ion.
The hypo hesis is conside ed o be mo e ue han alse and mo e inde e mina e han alse . The
signi ican p esence o inde e minacy equi es u he analysis o iden i y i s sou ces.
4. Discussion
The esul s 𝑈𝑁𝑃 = (0.65,0.30,0.10),o e a nuanced iew o he easibili y and challenges o apply-
ing AI and neu osophic models o mul ig ade li e acy pedagogy. The 65% deg ee o u h alida es
he undamen al p emise: he in eg a ion o sys ema ic, au oma ed da a analysis has conside able po-
en ial o imp o e eaching. This aligns wi h cu en ends in educa ion ha ad oca e o e idence-
based p ac ices.
Howe e , he mos e ealing componen o his analysis is he high deg ee o inde e minacy (30%)
. T acing i s o igin, we obse e ha his alue comes om he ques ion𝑄4 (𝐼₄ = 0.30): Do neu osophic
pli hogenic models adequa ely cap u e he unce ain y o educa ional en i onmen s? This sugges s ha he
main sou ce o doub lies no in he ul ima e goal (imp o ing eaching), bu in he sui abili y and
Neu osophy in A i icial In elligence: Ad ances and Applica ions om he Join Con e ences o BARNA Managemen
School (Dominican Republic) and Uni e sidad del T abajo del U uguay (Augus 6–8, 2025), Vol. 92, 2025
Débo a Lucia Ponce Ri e a, Ma yu i Y onne Guale Gómez, Rudy Ga cia Cobas,Ca los Robe o Humanan e Cab e a, Isaac
Roge Ma ínez. . Au oma ed Valida ion wi h AI o Neu osophic Pli hogenic Hypo heses in he Imp o emen o Mul ig ade
Li e acy Imp o emen .
638
accep ance o he speci ic me hodological ool (neu osophic models). The academic and pedagogical
communi y may ha bo ese a ions o simply lack su icien s udies o alida e he applica ion o his
highly specialized heo e ical amewo k in he ield o educa ion.
10% alsi y a e , while low, s ems om he ques ion 𝑄3 (𝐹₃ = 0.10): A e no-code AI ools obus enough o
alida e complex educa ional hypo heses? This e lec s a mino i y bu exis ing skep icism abou whe he "no-
code " pla o ms possess he necessa y igo o scien i ic esea ch, compa ed o solu ions ha equi e
expe p og amming and cus omiza ion.
Taken oge he , he esul s do no e u e he hypo hesis, bu a he quali y i . They show ha , while he
di ec ion is p omising, he pa h in ol es na iga ing conside able me hodological unce ain y and mild
echnological skep icism. Fo educa o s o educa ional policymake s, his means ha adop ing hese
ools can be bene icial, bu i mus be done wi h a c i ical eye, ecognizing ha he alida ion o hese
me hods in he educa ional ield is s ill unde way.
5. Conclusion
The pli hogenic neu osophic likelihood analysis de e mined ha he gene al hypo hesis abou im-
p o ing mul ig ade li e acy ins uc ion h ough AI and neu osophic models has a uni a ia e likelihood
o (𝑇 = 0.65,𝐼 = 0.30,𝐹 = 0.10).This esul indica es majo i y suppo o he hypo hesis, bu highligh s
an impo an a ea o unce ain y ha needs o be add essed.
P ac ical Implica ions
The indings sugges ha educa o s and adminis a o s ha e a solid ounda ion o explo e he use
o au oma ed alida ion ools. The high p obabili y o accu acy (65%) jus i ies in es men in pilo p o-
jec s. Howe e , he 30% unce ain y cau ions agains unc i ical implemen a ion, poin ing o he need
o eache aining and ongoing e alua ion o he me hod's e ec i eness.
Con ibu ions and Limi a ions
This s udy success ully demons a es how he pli hogenic amewo k can quan i y he alidi y o a
complex pedagogical hypo hesis, explici ly add essing unce ain y. I s main con ibu ion is o o e a
model ha goes beyond a simple accep ance o ejec ion, p o iding a de ailed map o poin s o consen-
sus, doub , and dissen . The main limi a ion, inhe en o he simula ion, is ha he inpu da a a e based
on a hypo he ical sen imen analysis. Fu he mo e, he high deg ee o inde e minacy e lec s po en ial
gaps in he cu en academic li e a u e ha he me hod i sel helps o highligh .
Recommenda ions o Fu u e Resea ch
I is ecommended o ocus u u e esea ch on he a eas ha gene a e he mos unce ain y and al-
si y. Speci ically, empi ical s udies a e needed ha compa e he e ec i eness o neu osophic models
wi h o he unce ain y managemen me hods in educa ional con ex s. Likewise, i is c ucial o conduc
compa a i e analyses on he eliabili y o "no- code " AI pla o ms e sus p og ammable ools in peda-
gogical esea ch. De eloping clea e and mo e accessible me hodological amewo ks will be key o
educing unce ain y and maximizing he posi i e impac o echnological inno a ion in educa ion.
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