LLMs help bridge the gap between theoretical curricula and real-world skills by creating simulated scenarios, cases and problem contexts that are hard to reproduce in traditional classrooms¶
Claim · All claims
Evidence · 1 study (2 entries) · reviewr2·q2
Currency · newest study 2025 · checked against its sources 2026-10-13 (automated, 2 of 2 entries) · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? The review reports that LLM-generated simulations let students apply theoretical concepts safely in varied situations, including health-science innovation projects with ethical dilemmas and professional standards. → Martínez-Peláez 2025
q2 i? In STEM programs, LLMs assist students in testing, debugging and improving technical setups or prototypes during project-based or inquiry-driven activities, with instant alternative solutions enabling continuous iteration. → Martínez-Peláez 2025 (2)
Evidence¶
Martínez-Peláez 2025¶
Martínez-Peláez, R.; Mena, L.J.; Toral-Cruz, H.; Ochoa-Brust, A.; Potes, A.G.; Flores, V.; Ostos, R.; Ramírez Pacheco, J.C.; Félix, R.A.; Félix, V.G. (2025). Can Large Language Models Foster Critical Thinking, Teamwork, and Problem-Solving Skills in Higher Education?: A Literature Review. Systems 2025, 13, 1013. https://doi.org/10.3390/systems13111013
q2 · i? · review · r2
The review's RQ2 synthesis states these simulations enable students to "apply theoretical concepts safely and effectively in various situations", citing health sciences examples where learners develop innovation projects including ethical dilemmas. No effect sizes are printed.
"One of their strongest features is the ability to create simulated scenarios, cases, and problem contexts that are hard to reproduce in traditional classrooms"
Martínez-Peláez 2025 (2)¶
Martínez-Peláez, R.; Mena, L.J.; Toral-Cruz, H.; Ochoa-Brust, A.; Potes, A.G.; Flores, V.; Ostos, R.; Ramírez Pacheco, J.C.; Félix, R.A.; Félix, V.G. (2025). Can Large Language Models Foster Critical Thinking, Teamwork, and Problem-Solving Skills in Higher Education?: A Literature Review. Systems 2025, 13, 1013. https://doi.org/10.3390/systems13111013
q2 · i? · review · r2
The review's RQ2 synthesis reports that when mistakes happen, models "can provide alternative solutions instantly, allowing continuous iteration and refinement", which the authors say accelerates learning cycles and enhances problem-solving efficiency. No effect sizes are printed.
"In STEM programs, LLMs assist students in testing, debugging, and improving technical setups or prototypes during project-based or inquiry-driven activities"
Discussion¶
Related Claims¶
- The article argues that memorization-centered management education faces three specific challenges: theory-practice disconnection, textbook lag, and simulation-reality divide — related
- LLMs support the shift from memorization-focused to competency-based assessment by generating rubrics, varied assessment items and authentic scenario-based tasks — related
- Theoretical quality of both theory-based and non-theory agents remains stable across Easy, Medium, and Hard scenario difficulty levels — related
- The evidence supporting curricular interventions to promote reflection in health professional education remains largely theoretical — related
- Large language models can support learning and teaching across education levels, from reading and writing support for elementary students to professional training — a broader claim this one bears on
- Student participation in validating and iteratively refining AI-generated simulations promotes physics understanding and problem-solving skills — related
- An internal program review found absence of a consistent pedagogical framework and need for active case-based materials — related
- LLM-based simulated students surpass rule-based systems in natural language generation and situational flexibility, but concerns about bias and evaluation reliability persist — related
- The closer training content is to authentic clinical scenarios, the smoother its translation into practice — a narrower finding that bears on this claim
- Training-data contamination motivates newly designed cases: models can reproduce popular tabular records that frequently appear in public notebooks — related
- The article argues that widespread adoption of generative AI creates a viable means of addressing management education's memorization-centered challenges — a broader claim this one bears on
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
llm-simulations-bridge-theory-practice-gap |
title |
LLMs help bridge the gap between theoretical curricula and real-world skills by creating simulated scenarios, cases and problem contexts that are hard to reproduce in traditional classrooms |
description |
LLMs help bridge the gap between theoretical curricula and real-world skills by creating simulated scenarios, cases and problem contexts that are hard to reproduce in traditional classrooms |
status |
review |
evidence_strength |
weak |
newest_evidence |
2025 |
source_checked |
2026-10-13 |
generated |
by process:wiki-ingest on 2026-10-12 |
trust |
unverified — no one has confirmed this page's content |
sources |
2 — martínez-peláez-2025, martínez-peláez-2025-2 |
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