Generative AI in language and literature: impact on argumentative writing and process‑based academic integrity in urban public general basic education (Guayaquil)
DOI:
https://doi.org/10.64747/sfe6qg28Keywords:
generative AI, argumentative writing, general basic education, academic integrity, GuayaquilAbstract
This study investigates the impact of a short, classroom‑embedded generative AI (GAI) intervention—strictly limited to metacognitive functions of ideation, diagnosis, and revision—on the quality of argumentative writing in Ecuador’s urban public General Basic Education (EGB). We implemented a cluster quasi‑experimental pretest–posttest design with an active control. The final sample comprised 8 classrooms (N = 236). The treatment group completed three GAI‑assisted sessions without machine‑generated final prose; the control followed comparable traditional practices. Products were double‑blind rated with a four‑dimension analytic rubric (thesis/focus, evidence/counterargumentation, cohesion‑coherence, conventions/voice). We also evaluated a process‑based academic integrity protocol requiring disclosure of use, traceable drafts, and documented prompts. ANCOVA with cluster‑robust errors and linear mixed models indicated a moderate advantage for the intervention on adjusted total scores (d ≈ 0.48; adjusted mean difference ≈ 1.26 points, 95% CI [0.68, 1.84]; p < .001). By dimension, the largest gains appeared in cohesion/coherence and evidence/counterargumentation. The group × baseline performance interaction was significant, with stronger benefits for the lowest tercile (d ≈ 0.62). Perceptually, the treatment reported lower cognitive load and higher perceived usefulness of feedback. Regarding integrity, the protocol correlated with a decrease in unattributed textual overlap at posttest (Δdiff ≈ −2.2 percentage points; p = .002) relative to control. We conclude that positioning GAI as a Socratic mentor that externalizes metacognition—without authoring students’ final text—enhances argumentative quality while supporting responsible authorship. For school systems with large classes, this approach is feasible and scalable when coupled with explicit rubrics and transparent process protocols.
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