Cognitive offloading and metacognitive calibration in generative AI-mediated doctoral learning: a grounded theory study of accountable reconstruction
Lingyun Gao, Feng Zhang · 2026
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Description
Doctoral students now lean on generative AI to explain, summarize, and draft, and the leaning reshapes the learning itself. Lingyun Gao and Feng Zhang interview twenty-eight doctoral students in 2026, gathering their AI artifacts and walking them back through their choices. A grounded theory model of five linked parts emerges from the coding.
Offloading helps when it lowers the entry cost of a hard task while the student still reads, checks, and rebuilds the output. It harms when fluent answers are mistaken for understanding, a calibration crisis the model names. Students recover by verifying sources, explaining ideas back, and arguing against the machine. The authors call that work accountable reconstruction, and say it keeps judgment with the learner.
Catalog record
| CREATOR | Lingyun Gao, Feng Zhang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 78 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |