Prompting-based student simulators maintain artificially high pass rates (~80%) from the first submission step, while fine-tuned models track real students' low-start, sharp-increase trajectories¶

Claim · All claims
Evidence · 1 study (2 entries) · design r2 · q2
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 i? Real students start with low pass rates and improve sharply near final steps; prompting-based models maintain high pass rates from the beginning, whereas fine-tuned models closely track this trajectory. → Rose Niousha 2026 q2 i? On test_OP_1, the three lowest-MAE models against student pass-rate trajectories are all INSIDE models (0.094, 0.098, 0.113). → Rose Niousha 2026 (2)

Evidence¶

Rose Niousha 2026¶

Rose Niousha, Minwoo Kang, Narges Norouzi. (2026). INSIDE the Student's Mind: Jointly Modeling Latent Reasoning and Action in LLM Student Simulators. Published as a conference paper at COLM 2026. https://arxiv.org/abs/2608.10492 doi:10.48550/arxiv.2608.10492

q2 · i? · design · r2

Pass-rate trajectory analysis over submission steps (Figure 2) on representative problems test_OP_1 and test_NP_1, comparing fine-tuned and prompted models to real students. The article reports real students "start with low pass rates and exhibit a sharp increase near the final steps" while prompted models stay near 80% throughout.

"In contrast, prompting-based models maintain relatively high pass rates ( ≈ 80%) from the beginning, with little variation across steps."

Rose Niousha 2026 (2)¶

Rose Niousha, Minwoo Kang, Narges Norouzi. (2026). INSIDE the Student's Mind: Jointly Modeling Latent Reasoning and Action in LLM Student Simulators. Published as a conference paper at COLM 2026. https://arxiv.org/abs/2608.10492

q2 · i? · design · r2

Mean Absolute Error between model and student pass rates averaged over steps on test_OP_1 (n=968 submissions). The article reports the top three models are all INSIDE variants; on test_NP_1 the closest models are Qwen2.5-Coder-7B-INSIDE (0.162), Qwen2.5-7B-SFT (0.181), and Qwen2.5-7B-INSIDE (0.182).

"Ontest_OP_1, the top three models are Qwen3-8B-INSIDE (0.094), Qwen2.5-Coder-7B-INSIDE (0.098), and Qwen2.5-7B-INSIDE (0.113)."

Discussion¶

Related Claims¶

• INSIDE fine-tuning achieves the highest alignment between generated internal dialogue and ground-truth student code edits, up to 57.9% — related
• Fine-tuning with INSIDE internal dialogue achieves the lowest Wasserstein distances to real student code distributions on test_OP across all action-fidelity metrics — related
• Differences in student pass-rate distributions between test splits explain the smaller INSIDE-over-SFT improvement on test_NP — related
• ProfileRL outperforms prompting and fine-tuning baselines on turn-level student simulation, with statistically significant gains on Acts, Correctness, and Errors — related
• Immediate post-feedback improvement is near zero on average and does not differ significantly across feedback conditions — related
• GPT-generated feedback passes simulated-student helpfulness evaluations only about 35% of the time on real student data — related
• High self-consistency between a simulator's reasoning and its own generated code does not imply alignment with real student reasoning or behavior — related