Title: "From AI Adoption to AI Usage Quality: Human–AI Collaboration in Technical Report Writing among Malaysian Polytechnic Engineering Students"

Author: Noor Darliza Binti Mohamad Zamri

Abstract: Generative artificial intelligence (Gen AI) is increasingly transforming educational practices, particularly in academic and technical writing contexts. In engineering education, students are utilising AI-assisted tools such as ChatGPT to support idea generation, language refinement, and technical report development. While existing studies largely focus on technology adoption and acceptance, limited attention has been given to how the quality of AI usage influences academic performance outcomes. This study investigates human–AI collaboration in technical report writing among Malaysian polytechnic engineering students by examining the mediating role of generative AI usage quality. Drawing upon the Technology Acceptance Model (TAM) and self-efficacy theory, the study proposes that perceived usefulness, perceived ease of use, and AI writing self-efficacy influence technical report quality through students’ quality of engagement with Gen AI tools. The study adopts a quantitative cross-sectional design involving engineering students in a Malaysian polytechnic context, with data analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). Conceptually, the study extends technology acceptance research beyond adoption behaviour by introducing generative AI usage quality as a mediating construct that explains how students translate AI engagement into effective technical writing outcomes. The study contributes to ongoing discussions on human–AI collaboration, AI-assisted learning, and the role of effective AI usage in higher education environments.

Keywords: Generative artificial intelligence; Human–AI collaboration; Technical report writing; Technology Acceptance Model; AI usage quality; Engineering education; Polytechnic students