Introduction Generative AI weakens the correspondence between the final text of an assignment and the argumentative work involved in producing it. This study examined how task structure and AI-use instructions shape evidence use, engagement with objections, and revision in university ideological and political education. Methods An LLM simulation crossed three task formats—position essays, evidence-integration memoranda, and follow-up-and-revision defense tasks—with three AI-use scenarios—simulated no-AI, responsible collaboration, and concealed ghostwriting. Two raters coded 148 generated texts with the Textual Response Artifact Scoring Index (TRASI), and an anonymous questionnaire collected 268 evaluations of 27 selected texts. Results Responsible collaboration produced the highest overall rubric score ( M = 3.32). Evidence-integration memoranda ( M = 3.16) and follow-up-and-revision defenses ( M = 3.10) exceeded position essays ( M = 2.63). Position essays varied most across the three AI-use scenarios, whereas the memorandum and defense showed narrower differences because their instructions already required source explanation or response and revision. Questionnaire participants distinguished the same task functions: memoranda were rated highest for source–claim linkage, and defenses for counterargument response and revision explanation. Open-ended responses associated evidence integration, comparison of reasons, and revision with thinking, while formulaic structure, over-polished expression, and generic abstraction were associated with AI-like writing. Discussion Task structure and AI-use instructions were functionally complementary. Structured tasks elicited source explanation or challenge and revision directly, so responsible collaboration added its largest contribution in the general position essay. GenAI-mediated assessment can therefore be designed around the argumentative decisions that writers are required to document.
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Liu et al. (2026) studied this question.
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