A reasoning-oriented LLM outperformed a standard LLM on scenario quality and feasibility of generated design problems, with no differences on alignment, cognitive complexity, or clarity¶

Claim · All claims
Evidence · 1 study · design r2 · q2 · i3 large
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i3 The reasoning-oriented LLM scored significantly higher than the standard LLM on scenario quality (0.97 vs. 0.77, p<0.001, d=0.88) and feasibility (1.00 vs. 0.94, p=0.018, d=0.55). → Ahmad D. Suleiman 2026 q2 i? No statistically significant differences between the two LLMs were observed for alignment, cognitive complexity, or clarity/specificity (p>0.05). → Ahmad D. Suleiman 2026

Evidence¶

Ahmad D. Suleiman 2026¶

Ahmad D. Suleiman, Daqing Hou, and Maliha Noushin Raida. (2026). LLM-Generated Design Problems for Assessing Higher-Order Thinking in Project-Based Learning. arXiv preprint arXiv:2607.11032. https://arxiv.org/abs/2607.11032 doi:10.48550/arxiv.2607.11032

q2 · i3 · design · r2

Expert evaluation of 80 DPs (10 per project from GPT-4o and Gemini 2.5 Pro) using two-tailed t-tests and Cohen's d found the reasoning model "significantly outperformed the standard LLM onscenario quality" with d=0.88 and higher feasibility with d=0.55.

"the reasoning-oriented LLM significantly outperformed the standard LLM onscenario quality(SQ: 0.97 vs. 0.77, 𝑝< 0.001,𝑑= 0.88), indicating more realistic and contextually rich scenarios supporting transfer. It also achieved higherfeasibility(FS: 1.00 vs. 0.94, 𝑝= 0.018,𝑑= 0.55)"

Discussion¶

Related Claims¶

• Students rated DPs positively, did not distinguish the two LLMs, and disagreed with experts on the same DPs, rating feasibility significantly lower — related
• Half of LLM-generated design problems received perfect expert ratings, though common failure modes included scenarios too similar to the original project and ambiguous scope — related
• Ablating expert-informed structural components lowered LLM-judge scores on all dimensions, with largest drops in challenge quality, intent alignment, and domain grounding — related