arXiv subjects

Erika M. Pineda

Publications and source records attributed to Erika M. Pineda.

3 recordsLinked to original sources

arXiv2026

Profiles of AI Dependency: A Latent Class Analysis of Filipino Students' Academic Competencies

The increasing dependency among Filipino college students on artificial intelligence (AI) poses concerns about the potential decline of fundamental academic competencies. This study examines the extent of AI dependency and its perceived effects on students' critical thinking, writing skills, learning independence, research skills, and academic engagement. Using a cross-sectional research design, data was collected from 651 students enrolled in higher education institutions (HEIs) in Pampanga, Philippines accredited by the Commission on Higher Education. The survey data was analyzed using Latent Class Analysis (LCA) to identify AI dependency patterns. Findings indicated that students show moderate to high AI dependency, specifically in research and writing tasks. LCA identified four distinct profiles: highly engaged independent learners, selective AI users, moderate AI users, and AI-dependent learners. Notably, AI-dependent learners demonstrated the weakest academic competencies, with significant dependency on AI-generated outputs. The study highlights the need to foster educational policies that integrate AI literacy while preserving essential academic skills. HEIs must also balance technological advancements with curriculum adaptations to promote critical thinking and ethical use of AI. Future research may explore the longitudinal impacts and intervention strategies to mitigate academic skill erosion caused by AI dependency.

cs.CY↗

arXiv2026

Bibliometric Mapping of AI-Supported Social Presence in Online Learning Environments: Trends, Collaboration, and Thematic Directions

This study examines the development, influence, and collaboration patterns in AI-supported social presence research within online learning environments. Utilizing 59 open-access empirical studies from Scopus, the study applies citation analysis, co-authorship mapping, institutional analysis, and keyword clustering using Python-based bibliometric tools. Findings reveal an upward trend in publications since 2020, with research focusing on engagement, AI tools, instructional design, and ethical issues. While countries such as the United States and Brazil are leading contributors, international collaboration remains limited. Ethical concerns related to trust and fairness are emerging but underexplored. The study highlights the importance of ethical integration, interdisciplinary collaboration, and learner-centered AI applications in education.

cs.CY↗

arXiv2025

Teachers' Perspectives on the Use of AI Detection Tools: Insights from Ridge Regression Analysis

This study explores the perceptions of 213 Filipino teachers toward AI detection tools in academic settings. It focuses on the factors that influence teachers' trust, concerns, and decision-making regarding these tools. The research investigates how teachers' trust in AI detection tools affects their perceptions of fairness and decision-making in evaluating student outputs. It also explores how concerns about AI tools and social norms influence the relationship between trust and decision-making. Ridge Regression analysis was used to examine the relationships between the predictors and the dependent variable. The results revealed that trust in AI detection tools is the most significant predictor of perceived fairness and decision-making among teachers. Concerns about AI tools and social norms have weaker effects on teachers' perceptions. The study emphasized critical role of trust in shaping teachers' perceptions of AI detection tools. Teachers who trust these tools are more likely to view them as fair and effective. In contrast, concerns and social norms have a limited influence on perceptions and decision-making. For recommendations, training and institutional guidelines should emphasize how these tools work, their limitations, and best practices for their use. Striking a balance between policy enforcement and educator support is essential for fostering trust in AI detection technologies. Encouraging experienced users to share insights through communities of practice could enhance the adoption and effective use of AI detection tools in educational settings..

cs.CY↗