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Hilene E. Hernandez

Publications and source records attributed to Hilene E. Hernandez.

5 recordsLinked to original sources

Pedagogical Promise and Peril of AI: A Text Mining Analysis of ChatGPT Research Discussions in Programming Education

GenAI systems such as ChatGPT are increasingly discussed in programming education, but the ways in which the research literature conceptualizes and frames their role remain unclear. This chapter applies text mining to publications indexed in a leading academic database to map scholarly discourse on ChatGPT in programming education. Term frequency analysis, phrase pattern extraction, and topic modeling reveal four dominant themes: pedagogical implementation, student-centered learning and engagement, AI infrastructure and human-AI collaboration, and assessment, prompting, and model evaluation. The literature prioritizes classroom practice and learner interaction, with comparatively limited attention to assessment design and institutional governance. Across studies, ChatGPT is positioned both as a learning aid that supports explanation, feedback, and efficiency and as a pedagogical risk linked to overreliance, unreliable outputs, and academic integrity concerns. These findings support responsible integration and highlight the need for stronger assessment and governance mechanisms.

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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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Trust, Usefulness, and Dependency on AI in Programming: A Hierarchical Clustering Approach

While AI tools are transforming programming education, their adoption in underrepresented countries remains insufficiently studied. Understanding students' trust, perceived usefulness, and dependency on AI tools is essential to improving their integration into education. For these purposes, this study surveyed 508 first-year programming students in Pampanga, Philippines and analyzed their perceptions using hierarchical clustering. Results showed four unique student profiles with varying in trust and usage intensity. While students acknowledged AI tools' benefits, dependency remained low due to limited infrastructure and insufficient exposure. High-frequency users did not necessarily report greater trust or usefulness which may indicates a complex relationship between usage patterns and perception. This study recommends that to maximize AI's educational impact, targeted interventions such as infrastructure development, training programs, and curriculum integration are necessary. This study provides empirical insights to support equitable and effective AI adoption in programming education within developing regions.

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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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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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