Genelyn R. Baluyos, Joel Tapayan Aclao

The influence of AI adoption, research skills, and statistical knowledge on graduate students’ statistical productivity

Introduction

The influence of ai adoption, research skills, and statistical knowledge on graduate students’ statistical productivity. This study examines how AI adoption, research skills, and statistical knowledge influence graduate students' statistical productivity. Findings show positive correlations, boosting research quality.

Abstract

Ensuring statistical productivity is essential for enhancing research efficiency and quality. This study examined the correlation between Artificial Intelligence (AI) adoption, research skills, and statistical knowledge with statistical productivity among graduate students in Misamis Occidental during the academic year 2023–2024. Employing a descriptive-correlational design, 192 graduate students were selected through stratified random sampling. Data were collected using researcher-developed questionnaires and analysed using mean, standard deviation, Pearson correlation, and stepwise multiple regression analysis. The findings indicated high levels of AI adoption, research skills, and statistical knowledge, all of which were positively correlated with statistical productivity. AI engagement, research proficiency, and statistical expertise were significant predictors of statistical productivity. Active use of AI and a strong understanding of statistical concepts notably enhanced students’ ability to analyse and interpret data. To further improve productivity, it is recommended that school administrators establish research centres equipped with appropriate software and tools to support research and statistical analysis.

Review

This study addresses a highly pertinent and timely topic concerning the factors influencing statistical productivity among graduate students, particularly with the increasing integration of AI in academic research. The title and abstract clearly define the scope and objectives, making a compelling case for the study's relevance to enhancing research efficiency and quality in higher education. The descriptive-correlational design is appropriate for exploring the relationships between AI adoption, research skills, statistical knowledge, and statistical productivity. The methodology, including the use of stratified random sampling and robust statistical analyses (Pearson correlation, stepwise multiple regression), suggests a rigorous approach to data collection and interpretation, lending credibility to the findings. The findings reveal important insights, indicating high levels of AI adoption, research skills, and statistical knowledge among graduate students in Misamis Occidental, all positively correlating with statistical productivity. Crucially, AI engagement, research proficiency, and statistical expertise were identified as significant predictors, underscoring their collective and individual importance. The observation that active AI use and a strong understanding of statistical concepts notably enhance data analysis and interpretation abilities is a significant contribution, providing empirical support for the ongoing emphasis on these competencies in graduate curricula. The practical recommendation for school administrators to establish well-equipped research centres further enhances the study's utility and potential for direct impact. While the abstract provides a strong overview, a full paper would ideally elaborate on the specific types of AI tools being adopted and how they are integrated into statistical workflows to further clarify the "AI engagement" variable. Additionally, exploring potential mediating or moderating effects could offer a richer understanding of these complex relationships. Nevertheless, this study offers valuable empirical evidence supporting the synergistic role of technological adoption (AI), methodological skills, and foundational knowledge in fostering graduate students' statistical productivity. It provides a solid foundation for future interventions and policy recommendations aimed at optimizing research training and support systems in higher education.

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