Abstract: Assessing the technological application value inherent in papers is important for understanding their role in the innovation chain. This study focuses on the field of two-dimensional (2D) materials science, a domain witnessing widespread Artificial Intelligence (AI) adoption, to empirically evaluate the impact of AI technology use within the research process on the technological application value of these papers. To identify AI adoption in research, we referenced established practices, performing precise keyword searches within paper abstracts to determine if AI techniques were utilized. To define this technological application value, we first constructed a corpus of paper abstracts in the field of 2D materials. We then examined the tendency towards technological application manifested in the abstract content, defining this tendency as the paper's technological application value. This involves leveraging text analysis techniques to quantify the prominence of technological application-oriented themes within each paper’s abstract, thereby constructing an index of its inherent technological application value. Our empirical findings provide evidence for the impact of AI use on the technological application value of papers. Firstly, using AI technology significantly enhances the technological application value of papers in the field of 2D materials. Secondly, mediation analysis suggests that using AI technology enhances the potential for technological application of papers. This enhancement is achieved by improving research efficiency (e.g., via high-throughput screening or simulation), better focusing on technological demands, and strengthening application-oriented objectives (e.g., via inverse design targeting specific properties). Thirdly, while the overall technological maturity of AI employed significantly and positively moderates its impact on the technological application value of research, this moderation exhibits a bidirectional nature when analyzed across different developmental stages. Specifically, during phases of accelerated AI technological maturation, the enhancing effect on application value becomes progressively stronger with advancing years, indicating greater value gains from more advanced AI applications. Conversely, in the early adoption stages of AI, a different trend emerges: the positive impact on application value tends to diminish over successive years, potentially due to the trial-and-error costs and uncertainties associated with nascent technologies. Furthermore, employing the H-index as a proxy for researchers’ academic influence and experience, our statistical analysis did not find a significant moderating effect of this factor on the relationship between AI adoption and the enhancement of papers’ technological application value. This finding suggests that while AI might potentially impact research productivity differently across different researchers as suggested by other studies, its capacity to strengthen the technological application value of papers in the 2D materials field appears less contingent on the researchers’ established academic influence and experience in this specific context. By defining and quantifying the technological application value of papers based on their abstract content, this study empirically reveals the specific pathways and moderating mechanisms through which AI technology influences this value. The findings indicate that AI not only impact research productivity but also significantly enhance the technological application value of papers.