Students use self-initiated strategies of verification, skill improvement, post-editing, pre-editing and reliance management to cope with GenAI challenges¶

Claim · All claims
Evidence · 1 study (2 entries) · qualitative r3 · q2
Currency · newest study 2025 · checked against its sources 2026-10-20 (automated, 2 of 2 entries) · not reviewed by a person

Subclaims¶

q2 i? A substantial share of students verify GenAI-generated information via cross-checking with search engines. → Wenkang Zhang 2025 q2 i? Students report post-editing GenAI outputs and, in some cases, pre-editing source texts before input to GenAI to improve results. → Wenkang Zhang 2025 (2)

Evidence¶

Wenkang Zhang 2025¶

Wenkang Zhang, Albert W. Li, Chenze Wu. (2025). University students' perceptions of using generative AI in translation practices. Instructional Science. https://doi.org/10.1007/s11251-025-09705-y

q2 · i? · qualitative · r3

Thematic analysis of questionnaire responses: 21 of 63 students emphasized verifying GenAI-generated information by cross-checking through search engines, e.g. re-searching ChatGPT-provided terminologies on Google.

"21 out of 63 students emphasized the importance of verifying GenAI-generated information, often by conducting a cross-check through search engines such as Google."

Wenkang Zhang 2025 (2)¶

Wenkang Zhang, Albert W. Li, Chenze Wu. (2025). University students' perceptions of using generative AI in translation practices. Instructional Science. https://doi.org/10.1007/s11251-025-09705-y

q2 · i? · qualitative · r3

15 of 63 participants described post-editing GenAI outputs (revising logical errors, removing high-sounding expressions); one interviewee reported pre-editing — segmenting long sentences, adjusting word order, simplifying metaphorical language — yielding "significant improvements" for Chinese-to-English non-literary texts.

"15 out of 63 participants underscored the necessity of post-editing to refine GenAI outputs."

Discussion¶

Related Claims¶

• Students perceive GenAI-assisted translation as challenged by inadequate outputs, limited prompt engineering, and risks to creativity and employment
• GenAI use involves substantial hidden labor of prompting, verification, and correction that remains invisible in productivity evaluation — a broader claim this one bears on
• Boundary conditions on verification and reliance include prior knowledge, learner characteristics, task stakes, verifiability, costs, accountability, AI system configuration, and multidimensional AI literacy — a broader claim this one bears on
• Low comment rewriting does not imply a smooth process: students describe the main effort as verifying generated code rather than revising prompts — 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
• Design students report low trust in GenAI outputs and systematically modify and verify them, with inaccuracy the main deterrent — 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