Active engagement with AI outputs (cross-verifying, refining prompts, revising) supports productive learning and depends on learners' prior knowledge¶

Claim · All claims
Evidence · 1 study (2 entries) · theoretical r2–r3 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? The more experienced learners are in academic writing, the more thoroughly they revise an AI-generated text (as reported from Radtke & Rummel, 2025). → Rummel 2026 q2 i? Students who practiced refining prompts showed descriptively higher AI self-efficacy and significantly greater knowledge of AI concepts (as reported from Woo et al.). → Rummel 2026 (2)

Evidence¶

Rummel 2026¶

Rummel, N., Nachtigall, V., & Panadero, E. (2026). “With a Thermomix You Lose the Ability to Cook”: A Kitchen Machine Analogy for Applications of Generative AI in Education. https://arxiv.org/abs/2609.09856 doi:10.48550/arxiv.2609.09856

q2 · i? · theoretical · r3

Second-hand finding reported in the active-use section: the article attributes to Radtke and Rummel (2025) that writing experience relates positively to depth of revision of AI-produced text. The reviewed study's design is not described here; no effect size is printed.

"As demonstrated in a study by Radtke and Rummel (2025), the more experienced learners are in academic writing, the more thoroughly they revise an AI-generated text."

Rummel 2026 (2)¶

Rummel, N., Nachtigall, V., & Panadero, E. (2026). “With a Thermomix You Lose the Ability to Cook”: A Kitchen Machine Analogy for Applications of Generative AI in Education. https://arxiv.org/abs/2609.09856. doi:10.48550/arxiv.2609.09856

q2 · i? · theoretical · r2

Second-hand intervention finding reported in the active-use section: the article attributes to Woo et al. a prompt-engineering intervention in which prompt-refining practice was followed by higher self-efficacy (descriptive) and significantly greater AI-concept knowledge. No statistics are printed in this article.

"Woo et al. (2025), who implemented a prompt-engineering intervention and found that students who practiced refining prompts showed descriptively higher self-efficacy in using AI tools for academic work and reported significantly greater knowledge of AI concepts"

Discussion¶

Related Claims¶

• Learner agency in GenAI-mediated learning is enacted through prompting, questioning, verification, comparison, revision, and disciplinary judgment — a broader claim this one bears on
• GenAI use involves substantial hidden labor of prompting, verification, and correction that remains invisible in productivity evaluation — related
• AI-supported design iteration consists of generation, selection, evaluation, and revision rather than one-off generation — related
• Low comment rewriting does not imply a smooth process: students describe the main effort as verifying generated code rather than revising prompts — related
• Qualitatively, AI-peer integrated feedback was associated with a broader repertoire of revision operations and more agentive cognitive strategies than peer feedback alone — related
• AI intuition emphasizes epistemic judgment rather than output optimization, distinguishing it from prompt engineering — related
• Students describe concrete verification behaviors for GenAI explanations, treating evaluation as an iterative filter rather than a single accept-or-reject judgment — related
• AI-supported project-based learning at YCHS produced authentic student-designed products through iterative prompting and refinement — a narrower finding that bears on this claim
• Graduate seminars with hands-on generative AI use and reflection increased students' comfort with AI and ability to critically assess AI outputs — a narrower finding that bears on this claim