Novice AI users showed a Trust-but-Can't-Verify pattern: accepting unfamiliar constructs they could not assess, then symptom-level debugging¶

Claim · All claims
Evidence · 1 study · qualitative r3 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Less experienced students possessed enough knowledge to know the AI was fallible but lacked the expertise to assess its correctness, accepting unfamiliar code and debugging symptoms when errors arose. → Liu 2026

Evidence¶

Liu 2026¶

Liu, D., Fan, G., & Pan, L. (2026). Tool, tutor, or crutch?: A grounded theory of cognitive scaffolding and offloading in AI-assisted programming education. International Journal of STEM Education. https://doi.org/10.1186/s40594-025-00592-w

q2 · i? · qualitative · r3

Interview and log analysis of self-identified beginners in the AI-enabled section. Logs showed novices accepting multi-line AI code using unfamiliar APIs and later conducting long debugging sessions with commit messages like "another fix". Novices in the human-pair contrast group instead engaged in direct clarification-seeking with partners.

"These students possessed enough knowledge to understand the AI was fallible but lacked the deeper expertise to confidently assess its correctness or efficiency."

Discussion¶

Related Claims¶

• More experienced AI users showed a Boilerplate Blindspot: uncritically accepting 'trivial' AI-generated code in which later defects originated — related
• AI-section students framed the AI as a collaborative partner even when logs showed one-shot imperatives and rapid acceptance (Illusion of Dialogue) — related
• Reviewed literature reports risks of overreliance, AI errors in complex tasks, and students' uncritical acceptance of AI-generated outputs — a broader claim this one bears on
• Students hold cautious trust in the course-specific RAG system, expressing some level of trust but not treating it as an authoritative source and indicating they would verify important information — related
• Working with imperfect AI outputs under mentor supervision was followed by students developing skepticism and independent debugging skills — related
• Student dialogue prompted educators to reconstruct teaching beliefs about assessment, debugging instruction, and active learning — related
• Motivation, affect, and learner beliefs shape whether AI evidence is accepted, resisted, or used in singing training — related
• ChatGPT users differentiated information less (never by credibility) and rarely verified information compared to Google users — related