Page 264 - RAQAMLI TRANSFORMATSIYA DAVRIDA PEDAGOGIK TA’LIMNI RIVOJLANTIRISH ISTIQBOLLARI

E+DIETing Lab uses AI avatars to let students practice counseling before interacting
with real clients.
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Current attitudes and readiness among dietetics professionals
Surveys of dietitians and dietetic students show interest and cautious optimism.
In one study, dietetic students believed that ethical use of AI would help professionals
work more efficiently and expand scope. Among practicing registered dietitian
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nutritionists (RDNs), many express interest in AI adoption but cite barriers such as
cost, technical expertise, and trustworthiness of algorithms. Meanwhile, AI in
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nutrition practice is framed as a future direction, with recognition of both promise
and risks. Given this context, guiding students early to use AI responsibly in their
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training can help bridge the gap from theoretical enthusiasm to practical
competence.
Practical Reasons to Encourage AI Use in Dietitian Education
Below, it can be categorized the principal practical reasons into themes:
pedagogical enhancement, efficiency and workflow support, professional
preparedness, and innovation & research.
Pedagogical enhancement
For personalized learning and scaffolding, AI tools can adapt to individual
students’ pace, offer hints, ask Socratic questions, or generate supplementary
explanatory material targeted to weaker areas. This scaffolding helps differentiate
instruction in heterogeneous cohorts.
In terms of Immediate feedback and formative assessment, using AI, students
can receive almost instantaneous feedback on exercises, quizzes, or draft
assignments. This immediate loop aids reflection and correction before summative
assessment. To enhance comprehension of complex data, dietetic education often
requires interpreting tables, statistical outputs, and research literature. AI tools (e.g.
LLMs) can help students parse and explain complex results, thereby lowering
comprehension barriers.
Efficiency and workflow support
For time-saving on administrative or repetitive tasks, students frequently spend
time on literature searches, summarization, formatting citations, or drafting baseline
passages. AI can assist or accelerate these tasks, freeing time for deeper thinking. AI
also can support in diet plan drafting and scenario generation; when working on case
studies, students can ask AI to generate menu options, nutrient analyses, or “what-if”
modifications, which they can then critically review. This encourages exploration of
alternatives more quickly.
Assisting with data analytics and modeling can ne another option for students.
Some dietetics coursework involves analyzing datasets (e.g. nutrient databases,
survey data). AI/machine-learning tools can help students preprocess, visualize, or
run predictions, allowing more time for interpretation.
Professional preparedness
Aligning training with future practice can be tough job for students and for
faculty staffs. As AI tools become more common in clinical or public health nutrition,
students familiar with such tools will be better prepared for real practice settings.
Encouraging an evidence-based, analytics mindset is extremely important for
dietetics students. AI usage can foster a mindset of exploring data, verifying
algorithmic outputs, and maintaining human oversight—a habit crucial for 262

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