Learners selectively filtered and adopted AI-generated feedback while maintaining pedagogical agency¶
Claim · All claims
Evidence · 1 study · causalr?·q2
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Learners actively filtered AI suggestions, rejecting low-quality feedback and selectively incorporating high-quality suggestions. → Han Xue 2026
Evidence¶
Han Xue 2026¶
Han Xue, Cao Jing, Wang Zi, Luo Heng. (2026). The Effects of Agent Type and Feedback Style on Self-Directed Learning: A Mixed-Methods Study. Behavioral Sciences. https://doi.org/10.3390/bs16071069
q2 · i? · causal · r?
Thematic analysis of reflection reports and interviews. Of 114 codes related to critical rejection of feedback, Socratic groups showed relatively higher frequencies; of 446 complete-adoption codes, the C3 group accounted for 122 instances, described as highly task-oriented behavior.
"These data suggest that learners did not adopt AI-generated feedback passively; instead, they actively filtered and selectively incorporated suggestions into their revisions."
Discussion¶
Related Claims¶
- Students describe concrete verification behaviors for GenAI explanations, treating evaluation as an iterative filter rather than a single accept-or-reject judgment — related
- High-achieving students describe selective AI use for clarification, summarisation, and workflow support while verifying outputs and keeping them subordinate to their own judgment — a broader claim this one bears on
- Qualitative analysis found staged disclosure sometimes withheld inaccurate diagnostic details, but some errors still shaped student revisions — related
- Interviewed students adopted an iterative upload–review–revise workflow and perceived the AI feedback as fair and highly useful, though some disregarded recommendations conflicting with their design intent — related
- Participants simultaneously affirmed ChatGPT feedback as useful while rejecting it as final grader, treating feedback utility and evaluative authority as separate judgements — related
- Instructors engage LLM-generated suggestions as a controllable refinement process — related
- A student maintained critical distance from GenAI outputs through a paradoxical trust in their untrustworthiness, deriving practical value while staying sceptical — related
- Students described the verification label as a reason to engage with AI-generated material that did not end their own scrutiny of it — related
- Students exercise ethical agency in GenAI use through human-AI collaboration strategies, personal ethical frameworks, strategic calibration of use, and resistance to over-reliance — a broader claim this one bears on
- Hybridization of authorship blends human and AI-generated content, often without learners' explicit awareness of the boundary, normalizing formulaic rhetorical structures — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
learners-selectively-filter-ai-feedback-agency |
title |
Learners selectively filtered and adopted AI-generated feedback while maintaining pedagogical agency |
description |
Learners selectively filtered and adopted AI-generated feedback while maintaining pedagogical agency |
status |
review |
evidence_strength |
weak |
newest_evidence |
2026 |
source_checked |
2026-10-20 |
generated |
by process:wiki-ingest on 2026-10-12 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — han-xue-2026 |
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