Causes, Potential Risks, and Preventive Mechanisms of AI-Induced Template Writing in Undergraduate Course Papers

Abstract

The widespread application of generative artificial intelligence is significantly reshaping the writing practices of undergraduate course papers and has given rise to a phenomenon referred to as AI-induced template writing. Such papers are often structurally well organized and linguistically fluent, yet highly homogeneous in content and weak in argumentation. This study aims to systematically examine the causes and potential risks of this phenomenon and to propose preventive mechanisms at the course level. The analysis suggests that AI-induced template writing is rooted not only in the technological characteristics of generative AI, but more fundamentally in the result-oriented positioning of course papers, evaluation practices that prioritize formal features, and insufficient training in academic writing. The high accessibility and efficiency of generative AI further reduce the cost of template-based writing and accelerate its diffusion. To a certain extent, this phenomenon compresses students’ engagement in reading, thinking, and revising, leading to distorted evaluation signals, superficial content, and weakened development of academic abilities. To address these challenges, this paper argues that course papers should be reoriented toward process-based academic training. By redesigning assignments, strengthening process-oriented assessment, and clearly defining the boundaries for AI use, generative AI can be guided to function as a tool that supports thinking rather than replaces learning, thereby preserving the educational value of undergraduate course papers.