Page 265 - RAQAMLI TRANSFORMATSIYA DAVRIDA PEDAGOGIK TA’LIMNI RIVOJLANTIRISH ISTIQBOLLARI

advanced practice. Encouraging students to “stay human in the loop” is often
recommended.
Building AI literacy and critical appraisal skills are necessary. Using AI tools
under supervision helps students understand strengths, limitations, biases, and
when not to rely on AI—critical competencies for professionals. In table 1, we can view
comparative features of AI tools vs. traditional manual methods, and practical
implication for students.

Table 1. Comparative features of AI tools vs. traditional manual methods

Traditional Manual Practical Implication for
Feature AI-augmented Methods
Methods Students
Speed Slower, labor-intensive Faster, automated Frees time for critical thinking

Limited by human Allows more varied case
Scalability Scales to many cases
capacity exposure

Feedback Delayed (instructor) Instant or near-instant Supports iterative learning
latency

Adaptability Fixed content Adaptive responses Enables personalized scaffolding

AI-assisted, but needs Teaches oversight and critical
Error checking Human-only
review review
Innovation Low flexibility Enables “what-if” Encourages exploration
potential simulations

Innovation and research opportunities
Facilitating student research should be the most supported area using AI tools.
Students undertaking research or capstone projects can leverage AI for literature
reviews, data mining, and hypothesis generation—augmenting their productivity
and creativity.
Encouraging exploration of new AI-driven nutrition solutions will give many
business opportunities for not only students or also for society. Engaging students
with AI early may spark innovation: new apps, digital services, or algorithmic nutrition
models. This fosters a more forward-looking cohort of dietitians. In table 2, several
distinctive nutrition information sites can assist dietetics major students.

Table 2. Representative AI dietetics education and practice support site

Site Name Usage of Example Actual Students Practice

Dietary Image recognition of food intake Students validate AI-predicted nutrient
assessment intake
AI-generated menus based on
Meal planning Students critique and adapt menus
constraints
Predictive Risk prediction for diet-related Students evaluate model output vs.
modeling disease literature 263

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