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I SHO‘BA:
Si a li a’lim – ba qa o a aqqiyo ka ola i: xo ijiy aj iba a mahalliy amaliyo
PRACTICAL REASONS TO ENCOURAGE STUDENTS IN DIETITIAN
EDUCATION PROGRAMS TO USE AI TOOLS
Au ho s: SUHHYUN KIM, PhD 1
A ilia ion: Head o Depa men o Die e ics and Nu i ion Bucheon Uni e si y in Tashken ,
Uzbekis an 1
DOI: h ps://doi.o g/10.5281/zenodo.17331803
ABSTRACT
In con empo a y heal h sciences educa ion, a i icial in elligence (AI) ools a e eme ging as
aluable assis an s. Fo s uden s in die e ics educa ion p og ams, encou aging judicious use
o AI can os e deepe lea ning, e iciency in asks, and p epa a ion o u u e p o essional
en i onmen s. This a icle ou lines p ac ical easons o suppo AI adop ion in die e ic
cu icula, add esses po en ial isks, and p oposes s a egies o implemen a ion. Th ee
illus a i e ables p esen compa a i e ea u es, applica ion domains, and a ecommended
in eg a ion oadmap. The a icle concludes ha wi h app op ia e guidance and e hical
aming, AI ools can become powe ul adjunc s in die i ian aining.
Keywo ds: A i icial In elligence, Die e ics Educa ion, AI Tools, Nu i ion S uden s, AI
Adop ion, Pedagogy.
INTRODUCTION
In ecen yea s, AI ools—pa icula ly gene a i e la ge language models,
machine lea ning sys ems, and image- ecogni ion so wa e—ha e begun o eshape
many domains, including heal h and nu i ion sciences. Wi hin die i ian educa ion,
hese echnologies o e oppo uni ies o enhance s uden lea ning, s eamline
ou ine asks, and p epa e lea ne s o AI-augmen ed p o essional p ac ice.1 Ye
in eg a ion emains limi ed and cau ious due o conce ns abou accu acy, e hics, and
dependency. 2 This a icle a gues ha encou aging die e ics s uden s o use AI
ools—unde s uc u ed guidance—yields mul iple conc e e bene i s. We i s e iew
AI in nu i ion/die e ics, hen p esen p ac ical a ionales, conside challenges, and
p opose in eg a i e s a egies.
Backg ound: AI in Nu i ion and Die e ics
O e iew o AI applica ions in nu i ion
A i icial in elligence has been applied in mul iple nu i ion domains—die a y
assessmen , ood image ecogni ion, pe sonalized die ecommenda ion, p edic i e
modeling, and emo e moni o ing.3 Fo example, AI-assis ed die a y assessmen
ools (image-based o senso -based) ha e achie ed accu acy compa able o o
some imes exceeding adi ional sel - epo me hods. AI sys ems ha e also been
deployed o p edic die a y pa e ns, es ima e nu ien in ake, o gene a e meal
plans adap ed o indi idual pa ien da a. 4 Wi hin die e ics educa ion speci ically,
some e o s a e unde way: o example, he ATLAS pla o m p o ides oice- o-cha
i ual pa ien s o aining communica ion skills in die e ic cu icula. 1 Also, he
“RAQAMLI TRANSFORMATSIYA DAVRIDA
PEDAGOGIK TA’LIMNI RIVOJLANTIRISH
ISTIQBOLLARI”
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I SHO‘BA:
Si a li a’lim – ba qa o a aqqiyo ka ola i: xo ijiy aj iba a mahalliy amaliyo
E+DIETing Lab uses AI a a a s o le s uden s p ac ice counseling be o e in e ac ing
wi h eal clien s.5
Cu en a i udes and eadiness among die e ics p o essionals
Su eys o die i ians and die e ic s uden s show in e es and cau ious op imism.
In one s udy, die e ic s uden s belie ed ha e hical use o AI would help p o essionals
wo k mo e e icien ly and expand scope.6 Among p ac icing egis e ed die i ian
nu i ionis s (RDNs), many exp ess in e es in AI adop ion bu ci e ba ie s such as
cos , echnical expe ise, and us wo hiness o algo i hms.7 Meanwhile, AI in
nu i ion p ac ice is amed as a u u e di ec ion, wi h ecogni ion o bo h p omise
and isks.8 Gi en his con ex , guiding s uden s ea ly o use AI esponsibly in hei
aining can help b idge he gap om heo e ical en husiasm o p ac ical
compe ence.
P ac ical Reasons o Encou age AI Use in Die i ian Educa ion
Below, i can be ca ego ized he p incipal p ac ical easons in o hemes:
pedagogical enhancemen , e iciency and wo k low suppo , p o essional
p epa edness, and inno a ion & esea ch.
Pedagogical enhancemen
Fo pe sonalized lea ning and sca olding, AI ools can adap o indi idual
s uden s’ pace, o e hin s, ask Soc a ic ques ions, o gene a e supplemen a y
explana o y ma e ial a ge ed o weake a eas. This sca olding helps di e en ia e
ins uc ion in he e ogeneous coho s.
In e ms o Immedia e eedback and o ma i e assessmen , using AI, s uden s
can ecei e almos ins an aneous eedback on exe cises, quizzes, o d a
assignmen s. This immedia e loop aids e lec ion and co ec ion be o e summa i e
assessmen . To enhance comp ehension o complex da a, die e ic educa ion o en
equi es in e p e ing ables, s a is ical ou pu s, and esea ch li e a u e. AI ools (e.g.
LLMs) can help s uden s pa se and explain complex esul s, he eby lowe ing
comp ehension ba ie s.
E iciency and wo k low suppo
Fo ime-sa ing on adminis a i e o epe i i e asks, s uden s equen ly spend
ime on li e a u e sea ches, summa iza ion, o ma ing ci a ions, o d a ing baseline
passages. AI can assis o accele a e hese asks, eeing ime o deepe hinking. AI
also can suppo in die plan d a ing and scena io gene a ion; when wo king on case
s udies, s uden s can ask AI o gene a e menu op ions, nu ien analyses, o “wha -i ”
modi ica ions, which hey can hen c i ically e iew. This encou ages explo a ion o
al e na i es mo e quickly.
Assis ing wi h da a analy ics and modeling can ne ano he op ion o s uden s.
Some die e ics cou sewo k in ol es analyzing da ase s (e.g. nu ien da abases,
su ey da a). AI/machine-lea ning ools can help s uden s p ep ocess, isualize, o
un p edic ions, allowing mo e ime o in e p e a ion.
P o essional p epa edness
Aligning aining wi h u u e p ac ice can be ough job o s uden s and o
acul y s a s. As AI ools become mo e common in clinical o public heal h nu i ion,
s uden s amilia wi h such ools will be be e p epa ed o eal p ac ice se ings.
Encou aging an e idence-based, analy ics mindse is ex emely impo an o
die e ics s uden s. AI usage can os e a mindse o explo ing da a, e i ying
algo i hmic ou pu s, and main aining human o e sigh —a habi c ucial o
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I SHO‘BA:
Si a li a’lim – ba qa o a aqqiyo ka ola i: xo ijiy aj iba a mahalliy amaliyo
ad anced p ac ice. Encou aging s uden s o “s ay human in he loop” is o en
ecommended.
Building AI li e acy and c i ical app aisal skills a e necessa y. Using AI ools
unde supe ision helps s uden s unde s and s eng hs, limi a ions, biases, and
when no o ely on AI—c i ical compe encies o p o essionals. In able 1, we can iew
compa a i e ea u es o AI ools s. adi ional manual me hods, and p ac ical
implica ion o s uden s.
Table 1. Compa a i e ea u es o AI ools s. adi ional manual me hods
Fea u e
T adi ional Manual
Me hods
AI-augmen ed Me hods
P ac ical Implica ion o
S uden s
Speed
Slowe , labo -in ensi e
Fas e , au oma ed
F ees ime o c i ical hinking
Scalabili y
Limi ed by human
capaci y
Scales o many cases
Allows mo e a ied case
exposu e
Feedback
la ency
Delayed (ins uc o )
Ins an o nea -ins an
Suppo s i e a i e lea ning
Adap abili y
Fixed con en
Adap i e esponses
Enables pe sonalized sca olding
E o checking
Human-only
AI-assis ed, bu needs
e iew
Teaches o e sigh and c i ical
e iew
Inno a ion
po en ial
Low lexibili y
Enables “wha -i ”
simula ions
Encou ages explo a ion
Inno a ion and esea ch oppo uni ies
Facili a ing s uden esea ch should be he mos suppo ed a ea using AI ools.
S uden s unde aking esea ch o caps one p ojec s can le e age AI o li e a u e
e iews, da a mining, and hypo hesis gene a ion—augmen ing hei p oduc i i y
and c ea i i y.
Encou aging explo a ion o new AI-d i en nu i ion solu ions will gi e many
business oppo uni ies o no only s uden s o also o socie y. Engaging s uden s
wi h AI ea ly may spa k inno a ion: new apps, digi al se ices, o algo i hmic nu i ion
models. This os e s a mo e o wa d-looking coho o die i ians. In able 2, se e al
dis inc i e nu i ion in o ma ion si es can assis die e ics majo s uden s.
Table 2. Rep esen a i e AI die e ics educa ion and p ac ice suppo si e
Si e Name
Usage o Example
Ac ual S uden s P ac ice
Die a y
assessmen
Image ecogni ion o ood in ake
S uden s alida e AI-p edic ed nu ien
in ake
Meal planning
AI-gene a ed menus based on
cons ain s
S uden s c i ique and adap menus
P edic i e
modeling
Risk p edic ion o die - ela ed
disease
S uden s e alua e model ou pu s.
li e a u e
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I SHO‘BA:
Si a li a’lim – ba qa o a aqqiyo ka ola i: xo ijiy aj iba a mahalliy amaliyo
Da a analy ics
Nu ien da abase mining
S uden s pe o m au oma ed clus e ing
Simula ed
counseling
Vi ual pa ien ia cha bo
S uden s p ac ice in e iewing esponses
Challenges and Mi iga ion S a egies
While he ad an ages a e compelling, adop ing AI in educa ional se ings
en ails isks. Below is a discussion o key challenges and sugges ed mi iga ions.
Accu acy, hallucina ion, and misin o ma ion
AI sys ems may gene a e inaccu a e o ab ica ed con en (“hallucina ions”).
S uden s mus be augh o ac -check, c oss- alida e, and no accep AI ou pu s
unc i ically. We sugges mi iga ion as ollows; equi e s uden s o append e e ences,
compa e AI sugges ions wi h p ima y li e a u e, and anno a e whe e hey modi ied
AI con en .
O e eliance and e osion o analy ical skills
Excessi e dependence on AI could hampe de elopmen o s uden s’ own
p oblem-sol ing o easoning skills. Mi iga ion can be design assignmen s ha
equi e s uden s o e lec on AI’s limi a ions, o pa ially disable AI (e.g. “no-AI”
componen s).
E hical conside a ions, bias, and equi y
AI models may encode biases (e.g. socio-cul u al, ood-cul u e biases), and
access o AI ools may a o be e - esou ced s uden s. Mi iga ion: include modules
on algo i hmic bias, ensu e equi able access o ools, anonymize o andomize
assignmen s o educe ad an age bias.
P i acy and da a secu i y
Some AI ools use se e s, logs, o cloud s o age, aising conce ns abou s uden
da a p i acy. Mi iga ion measu es include using ools ha espec p i acy, equi ing
anonymiza ion, and emphasizing ins i u ional policies. P i acy p o ec ions should be
a policy o sys em de elopmen p io i y.
Facul y eadiness and ins i u ional suppo
Many ins uc o s lack amilia i y wi h AI ools, o esis change. Ins i u ional
policies may es ic AI use. Mi iga ion: in es in acul y p o essional de elopmen ,
pilo p ojec s, and clea ins i u ional policies p omo ing guided AI use. I is impo an
o p o ide guidelines o AI educa ion policy, ei he na ionally o h ough he Minis y
o Educa ion.
Implemen a ion Recommenda ions
He e a e ac ionable ecommenda ions o die e ics p og ams seeking o
encou age AI use among s uden s: Fis , de elop an AI li e acy module ea ly in he
cu iculum (co e ing ool ypes, biases, bes p ac ices). Secondly, use sca olded
assignmen s whe e ea ly asks guide p omp o mula ion and c i ique. In hi d,
model AI use in class (ins uc o s show how hey use AI ools and c i ique ou pu s).9
A ew mo e ex a ac i i y sugges ions a e as ollows; 10, 11, 12
• Requi e “human in he loop” e iew: s uden s mus alida e and anno a e AI
ou pu s.
• P omo e e lec i e p ac ice: s uden s w i e sho e lec ions on AI ool
s eng hs and ailu es.
• Ensu e equi y o access: p o ide ins i u ional subsc ip ions o ee ools o all
s uden s.
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I SHO‘BA:
Si a li a’lim – ba qa o a aqqiyo ka ola i: xo ijiy aj iba a mahalliy amaliyo
• Pe iodically e alua e ou comes: compa e pe o mance, sa is ac ion, and
c i ical hinking me ics be o e/a e AI in eg a ion.
• Encou age s uden -led inno a ion: allow s uden s o p opose AI-based mini-
p ojec s o ools as pa o caps one wo k.
By ollowing a phased, e lec i e, and policy-suppo ed app oach, die e ics
p og ams can ha ness AI bene i s while main aining educa ional in eg i y. In able 3,
we sugges p oposed oadmap o in eg a i e AI use o Die e ics P og am.
Table 3. P oposed oadmap o in eg a ing AI in o die e ic cu iculum
Phase
Ac i i ies
Suppo needed
E alua ion me ic
Awa eness &
aining
Wo kshops on AI li e acy,
ool demos
Facul y aining, pla o m
licenses
S uden su eys o
unde s anding
Guided
assignmen s
Sca olded assignmen s wi h
AI p omp s
Sample p omp s, gua d ails
Quali y o s uden AI-
augmen ed wo k
Independen
use
S uden s choose AI ools o
p ojec s
Suppo sessions, o e sigh
Impac on p ojec
quali y/ ime
Re lec ion &
c i ique
S uden s c i ique AI ou pu s
Re lec ion p omp s, pee
discussion
Dep h o c i ique in essays
Con inuous
imp o emen
Adjus ools & policies
Ins i u ional suppo
Longi udinal ou comes
(g ades, sa is ac ion)
CONCLUSION
In he dynamic landscape o nu i ion and heal h sciences, AI ools a e
inc easingly becoming pa o p o essional p ac ice. Fo die i ian educa ion
p og ams, encou aging s uden s o adop and c i ically engage wi h AI ools yields
mul iple p ac ical bene i s: pe sonalized lea ning, ime sa ings, enhanced analy ical
capaci y, and eadiness o AI-augmen ed p o essional en i onmen s.13 Al hough
challenges exis —accu acy, o e eliance, bias, acul y eadiness— hey can be
mi iga ed ia pedagogical design, e lec i e sca olding, and ins i u ional suppo .
Ul ima ely, in eg a ing AI in o die e ics educa ion can help cul i a e a gene a ion o
die i ians who a e no only nu i ion expe s bu also disce ning use s (and pe haps
c ea o s) o AI ools.
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