Effective AI help-seeking is itself a skill unevenly distributed among students, and access to an assistant is not equivalent to effective help-seeking¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r1 · q2
Currency · newest study 2026 · checked against its sources 2026-10-17 (automated, 2 of 2 entries) · not reviewed by a person · recheck by 2031: baselines and conditions change

Subclaims¶

q2 i? In an ACE 2024 survey of 47 computing students with eight interviews, GenAI had not displaced traditional help resources; preferences varied with task, quality, latency, and trustworthiness. → Qusay H. Mahmoud 2026 q2 i? In a 12-week deployment with 52 students, most of more than 2,500 queries sought immediate assignment help and students often supplied minimal contextual information. → Qusay H. Mahmoud 2026 (2)

Evidence¶

Qusay H. Mahmoud 2026¶

Qusay H. Mahmoud. (2026). Judgment-Centred Software Engineering Education: A Post-Hype Review and Framework for AI-Augmented Learning. Preprint, September 2026. https://arxiv.org/abs/2609.29473 doi:10.48550/arxiv.2609.29473

q2 · i? · review · r2

The review reports Hou et al.'s ACE 2024 study combining a survey of 47 computing students with eight interviews: "GenAI had not displaced traditional help resources", with preliminary evidence that effective AI help-seeking is itself a skill unevenly distributed.

"Hou et al. [2024a] found that GenAI had not displaced traditional help resources; preferences varied with the task and with perceived quality, latency, and trustworthiness."

Qusay H. Mahmoud 2026 (2)¶

Qusay H. Mahmoud. (2026). Judgment-Centred Software Engineering Education: A Post-Hype Review and Framework for AI-Augmented Learning. Preprint, September 2026. https://arxiv.org/abs/2609.29473

q2 · i? · associational · r1

The review reports Sheese et al.'s 12-week deployment with 52 students and more than 2,500 queries: most sought immediate assignment help, fewer pursued conceptual understanding, and "students often supplied minimal contextual information".

"A complementary 12-week deployment of an LLM-powered programming assistant with 52 students captured more than 2,500 queries: most sought immediate help with programming assignments, fewer pursued broader conceptual understanding, and students often supplied minimal contextual information"

Studies bearing on this claim¶

Do learners ask for on-demand help when they need it, and can that be taught? This table gathers the studies behind this claim and the 4 narrower claims that bear on it: 5 distinct studies (2 associational, 2 causal, 1 review), q2–q3, 1 of 5 with an effect size coded. One row per study, codes as each entry records them; no effect is averaged and no verdict is drawn. Built by scripts/synthesize_claim.py from eval/synthesis/; a row that looks wrong is fixed on the claim it comes from.

Discussion¶

Related Claims¶

• Post-training GenAI use patterns were unevenly distributed: efficient collaborators were the largest profile (47.5%), followed by over-revisers (26.2%) and mixed users (21.2%) — related
• Students shifted AI usage toward debugging and concept explanation over the semester, with 31 of 89 reporting more debugging use — related
• Student-agent interaction produced far more help-seeking but a non-uniform cognitive distribution dominated by lower-order engagement (54.52%), unlike the balanced distribution of student-instructor interaction — related
• More than half of participants' ChatGPT queries sought knowledge or advice, with methodological knowledge and classroom management the largest prompt categories
• HelpCoach led to greater knowledge retention one week later than pre-task help-seeking training alone — related
• Not requesting any hints in at least one problem is related to greater wheel-spinning, consistent with help avoidance — related
• The Coach's hint budget was managed poorly: students split into early-spenders and savers, and within the Coach condition only those with a deliberate hint strategy scored higher — related
• Students' critical examination of AI-generated outputs increased across learning cycles, though unevenly, with some participants continuing to seek confirmation from AI — related
• Undergraduate computing students report help-seeking avoidance and anxiety, with 62.5% sometimes avoiding asking for help even when needed and 75% reporting anxiety when not understanding a topic — related