Strengthening Student Research Capacity through Practice-Based Artificial Intelligence Training: A Community of Practice Model at the American Corner Ambon

Abstract

This study investigates the effectiveness of a practice-based Artificial Intelligence training model in strengthening student research capacity through a Community of Practice approach at American Corner Ambon. Despite the growing role of AI in higher education, students in peripheral regions continue to face limited digital literacy, restricted access to research support, and weak collaborative learning ecosystems. This study addresses these challenges by integrating AI-based learning with community-engaged capacity development. A mixed-methods sequential explanatory design was employed. Quantitative data were collected through pre-test and post-test surveys involving student volunteers and analyzed using descriptive statistics, paired sample t-tests, and effect size analysis. Qualitative data were obtained through interviews and focus group discussions and analyzed using thematic coding and triangulation. The findings show that practice-based AI training significantly improved students’ competencies in literature searching, citation management, abstract writing, research design, and statistical analysis. The Community of Practice approach also fostered peer learning, collaborative knowledge exchange, and sustainable digital research networks. This study proposes a five-stage intervention model that integrates training, mentoring, and sustainability. It contributes to advancing digital literacy and collaborative learning frameworks for strengthening research ecosystems in underrepresented higher education contexts.