arXiv subjects

Almer B. Gamboa

Publications and source records attributed to Almer B. Gamboa.

6 recordsLinked to original sources

arXiv2026

AI Adoption Among Teachers: Insights on Concerns, Support, Confidence, and Attitudes

The study examines the adoption of artificial intelligence (AI) tools in education by analyzing the roles of institutional support, teacher confidence, and teacher concerns. It aims to determine whether teacher concerns moderate the relationship between institutional support and two outcomes: teacher confidence and attitudes toward AI adoption. The sample included 260 teachers from the Philippines. Composite scores were calculated for institutional support, confidence, concerns, and attitudes. Moderated multiple regression analysis showed that institutional support significantly predicted both teacher confidence and attitudes toward AI. However, teacher concerns did not significantly moderate these relationships. A follow-up mediation analysis tested whether confidence explains the effect of institutional support on attitudes. Results showed full mediation. The indirect effect was significant based on the Sobel test, and the direct effect became non-significant when confidence was included in the model. This shows that institutional support improves teacher attitudes by increasing their confidence. The study recommends that institutions provide structured and ongoing support to strengthen teacher confidence. Professional development, mentoring, and AI integration in teacher education programs can increase readiness and support effective AI adoption.

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arXiv2026

Exploring the Adoption Intention in Using AI-Enabled Educational Tools Among Preservice Teachers in the Philippines: A Partial-Least Square Modeling

This study examines the factors influencing pre-service teachers' behavioral intention to use AI-enabled educational tools during their practicum, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as the theoretical framework. The model includes the core UTAUT2 constructs such as performance expectancy, effort expectancy, hedonic motivation, social influence, facilitating conditions, price value, and habit. It also incorporates additional predictors including computer self-efficacy, computer anxiety, and computer playfulness. Data were collected from 563 pre-service teachers using a structured questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that performance expectancy and hedonic motivation are the strongest predictors of behavioral intention. Computer self-efficacy, computer anxiety, and computer playfulness significantly influenced effort expectancy, although effort expectancy did not directly predict behavioral intention. Performance expectancy was significantly predicted by extrinsic motivation, job fit, relative advantage, and outcome expectations. Constructs such as social influence and facilitating conditions showed limited or inverse effects. These findings suggest that internal motivational, cognitive, and emotional factors are more influential than external or institutional factors in shaping the adoption of AI-enabled tools. The study highlights the importance of promoting personal relevance, confidence, and enjoyment in teacher preparation programs to encourage technology integration.

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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.

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arXiv2025

Teachers' Perspectives on Integrating AI tools in Classrooms: Insights from the Philippines

This study explores the attitudes, reservations, readiness, openness, and general perceptions of Filipino teachers in terms of integrating Al in their classrooms. Results shows that teachers express positive attitude towards integrating Al tools in their classrooms. Despite reporting high level of reservations, teachers believed they are ready and very open in complementing traditional teaching methods with these kinds of technologies. Teachers are very much aware with the potential benefits Al tools can offer to their individual student learning needs. Additionally, teachers in this study reported high level of support from their institutions. Recommendations are offered.

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arXiv2025

Prevalence, Devices Used, Reasons for Use, Trust, Barriers, and Challenges in Utilizing Generative AI among Tertiary Students

This study examined generative AI usage among Philippine college students particularly on frequency, devices, reasons, knowledge, trust, perceptions, and challenges. Most students used free AI tools on smartphones due to financial constraints. They used it primarily for homework, idea generation, and research. Less than half felt confident with AI and expressed mixed feelings about its accuracy. Barriers included limited access, lack of teacher support, difficulty understanding outputs, and financial constraints. The study highlighted the need for better access, support, training, and ethical guidelines. Broader concerns included impacts on learning, academic standards, job loss, and privacy. Students viewed AI positively due to peer support. Recommendations are discussed.

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arXiv2025

Dependency on Meta AI Chatbot in Messenger Among STEM and Non-STEM Students in Higher Education

To understand the potential dependency of tertiary students regarding Meta AI in the academic context. This descriptive cross-sectional study surveyed 872 tertiary students from public and private institutions in Luzon, Philippines. Demographic information and perceptions on Meta AI dependency based on existing literature were collected. Descriptive statistics were used to summarize the data and differences between STEM and non-STEM students were analyzed using the Mann-Whitney U test. The results indicate a nuanced perspective on Meta AI chatbot use among students. While there is general disagreement with heavy reliance on the chatbot for academic tasks, psychological support, and social factors, there is moderate agreement on its technological benefits and academic utility. Students value the Meta AI convenience, availability, and problem-solving assistance, but prefer traditional resources and human interaction for academic and social support. Concerns about dependency risks and impacts on critical thinking are acknowledged, particularly among STEM students, who rely more on chatbots for academic purposes. This suggests that while Meta AI is a valuable resource, its role is complementary rather than transformative in educational contexts, with institutional encouragement and individual preferences influencing usage patterns. Students generally hesitate to rely heavily on meta-AI chatbots. This reflects a preference for traditional resources and independent problem-solving. While students acknowledge AI chatbots academic benefits and technological convenience, concerns about overreliance and its impact on critical thinking persist, particularly among STEM students, who appear more inclined to integrate these tools into their studies.

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