Generative AI in Teaching Technical Report Writing to Engineering Students: A Case Study on Technology Acceptance and Writing Self-Efficacy

Abstract

The growing incorporation of generative artificial intelligence (GAI) in educational settings is transforming the teaching of technical writing in engineering education. However, there is little evidence on how students adopt these technologies in the development of technical reports, a key transversal skill in their future professional practice. This case study analyzes the relationship between GAI acceptance and self-efficacy in technical report writing among 158 engineering students at a national university in Peru. A quantitative, correlational approach and a non-experimental design were used. The results indicate that most students show moderate to high levels of technological acceptance and self-efficacy in writing technical reports, with a clear predominance of positive attitudes towards the use of GAI. Significant positive correlations were found between the dimensions of perceived use, ease of use, and intention to use GAI with the key stages of planning, drafting, and reviewing technical reports. It is concluded that the effective integration of GAI improves academic and professional engineering education by strengthening students’ confidence and skills in specialized writing. Finally, it is recommended that future research incorporate variables such as intrinsic motivation and critical thinking, considering their application in different branches of engineering.

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