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Artificial Intelligence Knowledge Development

<p style="text-align: justify;"><span style="font-size: 12pt; font-family: 'Cambria', serif; color: black;">Integrated reading-to-write (RTW) tasks in English for Academic Purposes (EAP) ask students to work carefully with sources. Writers must keep the original meaning, connect claims to evidence, and manage stance and voice when using several texts. Generative AI can make revision easier, but it may also produce wording that sounds convincing without enough evidence. This qualitative longitudinal multiple-case study examined how EFL university writers used conscious-access routines during AI-assisted revision and whether these routines later appeared in no-AI writing. Twenty-four third- and fourth-year English majors in a 16-week EAP course completed four RTW cycles. Each cycle included no-AI drafting and a 60-minute Gemini-supported revision session. Two revision conditions kept tool access and time-on-task the same. The checkpointed condition required uptake decisions, short reasons, and confidence ratings. The non-checkpointed condition allowed students to evaluate suggestions as they wished. Six data sources produced 384 coded revision episodes. The findings also showed clear differences between the two conditions. Evidence-first routines increased across cycles, while fluency-first routines decreased. No-AI milestone writing showed more evidence-first instances in the checkpointed condition at Weeks 8 and 12. Structured checkpointing may therefore help students use AI more responsibly by checking sources and evidence before accepting fluent suggestions.</span></p>

ltrq.2026.56.05.pdf

Original Research 

ltrq.2026.56.05.xml

Checkpointed Evaluation in AI-Assisted Integrated Reading-to-Write Revision: Conscious-Access Routines and No-AI Transfer