Students showed bounded trust and uneven verification behavior: 23 of 837 student turns (about 2.7%) contained explicit risk-aware moves¶

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 2031: baselines and conditions change

Subclaims¶

q2 i? Across 837 student turns in logs B–K, 23 turns (about 2.7 percent) contained explicit risk-aware moves such as correcting AI assumptions, requesting evidence, or stating confusion; the distribution was uneven, with two logs containing none. → Sidorkin 2026 q2 i? Survey comments showed bounded trust: four of 24 respondents used language of dependence or reliance, and one explicitly named the need for teacher oversight. → Sidorkin 2026 (2)

Evidence¶

Sidorkin 2026¶

Sidorkin, A. M. (2026). From One-Size Texts to Tailored Readings: Student Experiences with AI-Generated Course Materials. Open Praxis, 18(3), 506–522. https://doi.org/10.55982/openpraxis.18.3.1141

q2 · i? · design · r2

Turn-level coding of student prompts in logs B–K for risk-aware moves. The article reports "23 turns (about 2.7 percent) contained explicit risk-aware moves", with an uneven distribution, indicating a critical stance was present but not uniform.

"Across 837 student turns extracted from logs B–K, 23 turns (about 2.7 percent) contained explicit risk-aware moves using conservative markers (corrections of AI assumptions, requests for evidence or cases, statements of confusion functioning as comprehension checks)."

Sidorkin 2026 (2)¶

Sidorkin, A. M. (2026). From One-Size Texts to Tailored Readings: Student Experiences with AI-Generated Course Materials. Open Praxis, 18(3), 506–522. https://doi.org/10.55982/openpraxis.18.3.1141

q2 · i? · qualitative · r2

Qualitative thematic coding of open-ended survey responses on risk awareness and boundaries. The article reports "Four of 24 respondents used language of dependence or reliance", supporting a bounded credibility profile rather than blanket acceptance.

"Four of 24 respondents used language of dependence or reliance, and one explicitly named the need for “teacher oversight” to ensure accuracy."

Discussion¶

Related Claims¶

• AI hallucination can be turned into a pedagogical resource by making verification and collaborative fact evaluation integral to AI-supported learning — a broader claim this one bears on
• Student reflections identify speed and scaffolding as the dominant theme, with verification burden, overreliance worries, and prompt precision also prevalent — related
• GenAI use involves substantial hidden labor of prompting, verification, and correction that remains invisible in productivity evaluation — related
• The top of the student-discourse distribution contains essentially no categories of explicit conceptual reasoning, prediction, comparison to similar problems, or critical engagement with the tutor's suggestions — related
• Interest tailoring was structurally present in AI-generated readings: logs averaged 52.24 tailoring markers per 10,000 words and showed moderate divergence (cosine similarity 0.50–0.61) — related
• AI-generated readings lacked internal traceability: only about 0.80% of pages contained APA-style citations, URLs appeared on about 0.53%, and DOI strings were essentially absent — related
• Coded prompt logs show different prompting strategies foreground different pedagogical themes, with technical prompts the largest strategy total (398 instances) — related
• Students describe concrete verification behaviors for GenAI explanations, treating evaluation as an iterative filter rather than a single accept-or-reject judgment — related