Integration of a Collaborative Artificial Intelligence in Improving Student Learning Outcomes in Data Analytics

Abstract Generative Artificial Intelligence (GenAI) tools commonly used in higher education predominantly support one-to-one interactions between learners and technology, with limited emphasis on collaborative learning. This non-equivalent quasi-experimental study investigated the effects of integrating collaborative GenAI on students’ learning outcomes in a data analytics course within a Philippine higher education institution. Using a free and open-source statistical software, pre- and post-assessments were administered to evaluate students’ academic performance. Findings from a one-way analysis of covariance indicated a significant improvement in students’ levels of mastery across the experimental groups following the implementation of the collaborative GenAI-assisted intervention. Further analysis using one-way analysis of variance revealed significant gains in students’ problem-solving, critical thinking, and written communication skills. The results provide empirical evidence supporting the pedagogical value of collaborative GenAI in enhancing higher-order learning outcomes. The study offers implications for curriculum design, instructional practices, faculty development, and policy formulation in higher education amid ongoing digital transformation.