Learner agency in GenAI-mediated learning is enacted through prompting, questioning, verification, comparison, revision, and disciplinary judgment¶
Claim · All claims
Evidence · 1 study · theoreticalr3·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? The article theorizes that prompting, questioning, verification, comparison, revision, and disciplinary judgment are central to knowledge construction in human-AI interaction, strengthening the theorization of learner agency. → Moganadas SR 2026
Evidence¶
Moganadas SR 2026¶
Moganadas SR, Marín-González F, Nun SH and Gan CL (2026) Generative AI as a didactic-pedagogical mediator: rethinking human roles and pedagogical design in higher education. Front. Educ. 11:1856839. https://doi.org/10.3389/feduc.2026.1856839
q2 · i? · theoretical · r3
This is the article's own statement of its second contribution, derived from a focused integrative interdisciplinary synthesis rather than empirical measurement. The synthesis draws on literature including Chi and Wylie's active, constructive, and interactive engagement framework. No effect size is reported; the support is conceptual argument.
"Second, it strengthens the theorization of learner agency by showing that prompting, questioning, verification, comparison, revision, and disciplinary judgment are central to knowledge construction in human-AI interaction."
Discussion¶
Related Claims¶
- Assessment design functions as a pedagogical condition shaping how students engage with GenAI during learning — related
- Students, not AI, are the actors associated with knowing, judging and verifying in GenAI higher-education abstracts — related
- In interviews with 25 engineering students at one Swedish university, students described GenAI as acceptable when it stimulates reflection and directly copying its outputs as cheating, and one used its fallibility as a prompt to double-check — related
- Human guidance remains essential for ethical grounding when integrating AI with situated learning, including resisting complacency toward generative AI outputs — 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 — related
- AI's role in shaping interaction, epistemic agency, and shared regulation in collaborative learning remains under-theorized — a broader claim this one bears on
- Detection-oriented integrity approaches are increasingly misaligned with GenAI-augmented assessment (theoretical argument) — related
- Judgement is theorized as the evaluative locus of educational integrity within GenAI-augmented authorship — related
- Students describe concrete verification behaviors for GenAI explanations, treating evaluation as an iterative filter rather than a single accept-or-reject judgment — a narrower finding that bears on this claim
- Active engagement with AI outputs (cross-verifying, refining prompts, revising) supports productive learning and depends on learners' prior knowledge — a narrower finding that bears on this claim
- AI intuition emphasizes epistemic judgment rather than output optimization, distinguishing it from prompt engineering — related
- The paper argues authenticity in AI-mediated assessment is achieved through design rather than detection — related
- Despite a student's claims of purely human authorial agency, posthumanist analysis of his AI-mediated revision shows meaning co-produced through human-AI entanglement — related
- Non-expert students exhibit intuitive behaviors towards LLM-based AI systems when prompt engineering — related
- AI-supported design learning demands integrative capabilities, judgement, and learner agency rather than a single new skill — related
- AI-supported design iteration consists of generation, selection, evaluation, and revision rather than one-off generation — a narrower finding that bears on this claim
- Verification behavior lagged rising AI use: minimal or conditional verification more than doubled (5 to 12) over the semester — a narrower finding that bears on this claim
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
learner-agency-genai-verification-revision |
title |
Learner agency in GenAI-mediated learning is enacted through prompting, questioning, verification, comparison, revision, and disciplinary judgment |
description |
Learner agency in GenAI-mediated learning is enacted through prompting, questioning, verification, comparison, revision, and disciplinary judgment |
status |
review |
evidence_strength |
weak |
newest_evidence |
2026 |
source_checked |
2026-10-20 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — moganadas-sr-2026 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.