John Paul P. Miranda

John Paul P. Miranda, Rex P. Bringula, Laharni S. Simpao et al.

This study investigates the challenges in designing, data collection, and implementation of a web-based Tutoring System (TS) for teaching linear equations within a developing country context. Originally designed as an Android app, the system was redeveloped as a web application to facilitate cross-platform access and data collection. This redesign enabled enhanced tracking through interaction logs and included features like problem skipping, hints, difficulty-based problem sequencing, and game modes with adaptable progression (e.g., easy-to-hard, hard-to-easy). The main objective was to document the design and data collection challenges encountered in data collection for the development of a model capable of detecting learned helplessness in students' behaviors while using a web application for solving linear equation. Challenges included outdated devices, unreliable internet, and logistical constraints such as limited session durations and delays in obtaining approvals. Environmental disruptions like class cancellations and curriculum gaps further complicated the process, with only 118 out of 410 students eligible and actively participating. These obstacles highlight the complexities of collecting interaction data for detecting learned helplessness in real-world, resource-constrained educational settings.

John Paul P. Miranda, Emmanuel B. Parreño, Jovita G. Rivera

The integration of AI tools in academic settings has introduced a distinct form of strain that existing frameworks like technostress and digital fatigue have not yet fully addressed. This study develops a conceptual model and identifies the dimensions that define AI fatigue as a form of strain arising from sustained academic use of AI tools. Using grounded theory analysis of open-ended responses from 1,054 university students across three universities in the Philippines, the study examined the cognitive, motivational, emotional, physical, and attentional pressures students experienced during AI-supported academic work. Analysis produced five dimensions of AI fatigue, namely Cognitive Overload, Motivational Disengagement, Moral Unease, Physical Strain, and Attentional Drift, each consisting of two indicators grounded in participant accounts. The findings also yielded the AI Fatigue Model, a stage-based framework that explains how these pressures accumulate and reinforce one another across repeated AI interaction in academic tasks. These contributions establish a conceptual and exploratory foundation for AI fatigue as a distinct construct and provide a basis for future instrument validation, scale development, and cross-contextual inquiry in academic settings where AI now mediates student learning.

Vanessa B. Sibug, Maria Anna D. Cruz, Vicky P. Vital et al.

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.

Maria Anna D. Cruz, Jeaneth D. Serna, Lloyd D. Feliciano et al.

This study examined the barriers and enablers of online instruction in hospitality education. A sequential exploratory design was implemented with hospitality teachers from both public and private higher educational institutions in the Philippines. Thematic analysis of interviews identified four key themes: technological barriers, pedagogical challenges, institutional and personal support, and integration of artificial intelligence (AI). These themes were transformed into survey constructs and tested for reliability. Pedagogical challenges, including difficulties in teaching hands-on subjects and sustaining student engagement, emerged as the most critical concerns. Technological barriers such as unstable internet and limited devices were moderately rated, while institutional and personal support received mixed evaluations. Teachers viewed AI integration as helpful but also expressed caution and emphasized the need for training. Reliability analysis showed acceptable to good internal consistency across constructs. The findings highlight the importance of strengthening pedagogical training, providing clear institutional support, and fostering responsible competence in AI use. Future studies should validate these results with larger and more diverse samples.

Vanessa B. Sibug, Jan Henry B. Sunga, Emerson Q. Fernando et al.

This study examines the factors that influence the adoption of TikTok as a learning tool for physical education (PE)-related content among tertiary students in the Philippines. The study applies the Technology Acceptance Model (TAM) and Uses and Gratification Theory (UGT) to assess Information Seeking, Personal Identity, Social Interaction, Entertainment, Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Intention to Use (IU). A cross-sectional design and Structural Equation Modeling (SEM) were employed. The sample included 1,075 regular TikTok users with an average age of 19 years, the majority of whom were female. The analysis revealed that PU and PEOU were the strongest predictors of IU TikTok for PE related content. The results indicate that TikTok provides an engaging and accessible medium that supports active learning and participation in PE. The study offers empirical evidence from the Philippines and contributes to the academic discussion on the role of short-form video platforms in PE.

John Paul P. Miranda, Rhiziel P. Manalese, Mark Anthony A. Castro et al.

This study examined how moral disengagement influences Filipino college students' intention to use ChatGPT in academic writing. The model tested five mechanisms: moral justification, euphemistic labeling, displacement of responsibility, minimizing consequences, and attribution of blame. These mechanisms were analyzed as predictors of attitudes, subjective norms, and perceived behavioral control, which then predicted behavioral intention. A total of 418 students with ChatGPT experience participated. The results showed that several moral disengagement mechanisms influenced students' attitudes and sense of control. Among the predictors, attribution of blame had the strongest influence, while attitudes had the highest impact on behavioral intention. The model explained more than half of the variation in intention. These results suggest that students often rely on institutional gaps and peer behavior to justify AI use. Many believe it is acceptable to use ChatGPT for learning or when rules are unclear. This shows a need for clear academic integrity policies, ethical guidance, and classroom support. The study also recognizes that intention-based models may not fully explain student behavior. Emotional factors, peer influence, and convenience can also affect decisions. The results provide useful insights for schools that aim to support responsible and informed AI use in higher education.

John Paul P. Miranda, Rhiziel P. Manalese, Sheila M. Geronimo et al.

This study examined how student interns in Philippine higher education use intelligent tools during their OJT. Data were collected from 384 respondents using a structured questionnaire that asked about AI tool usage, task-specific applications, and perceptions of confidence, ethics, and support. Analysis of task-based usage identified four main purposes: productivity and report writing, communication and content drafting, technical assistance and code support, and independent task completion. ChatGPT was the most commonly used AI tool, followed by Quillbot, Canva AI, and Grammarly. Students reported moderate confidence in using AI and applied these tools selectively and ethically during OJT tasks. This indicate that AI tools assist student interns in various OJT activities related to work-readiness. The study suggests that higher education programs include AI literacy and onboarding. Clear policies and fair access to AI tools are important to support responsible use and prepare students for future careers.

John Paul P. Miranda, Rhiziel P. Manalese, Ivan G. Liwanag et al.

This study examined how behavioral, emotional, and contextual factors influence Filipino students' willingness to use artificial intelligence (AI) for mental health support. Results showed that habit had the strongest effect on willingness, followed by comfort, emotional benefit, facilitating conditions, and perceived usefulness. Students who used AI tools regularly felt more confident and open to relying on them for emotional support. Empathy, privacy, and accessibility also increased comfort and trust in AI systems. The findings highlight that emotional safety and routine use are essential in promoting willingness. The study recommends AI literacy programs, empathic design, and ethical policies that support responsible and culturally sensitive use of AI for student mental health care.

Juvy C. Grume, John Paul P. Miranda, Aileen P. De Leon et al.

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.

Vanessa B. Sibug, Emerson Q. Fernando, Almer B. Gamboa et al.

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.

Almer B. Gamboa, Erika M. Pineda, Rhiziel P. Manalese et al.

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.

Emerson Q. Fernando, Julius Ceazar G. Tolentino, Maria Anna D. Cruz et al.

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.

John Paul P. Miranda

This study applied the Apriori algorithm to analyze behavioral interaction patterns associated with learned helplessness (LH) in mathematics tutoring system logs. Interaction data were examined across three dimensions: LH level (low vs. high), system-based intervention (with vs. without), and problem-solving outcomes (solved vs. unsolved). The analysis of the complete dataset showed that skipping problems without using hints was the most frequent pattern linked to unsolved outcomes, while persistence behaviors such as not skipping were less dominant overall. Comparisons by LH level showed that low-LH students had stronger links between problem solving and not skipping, as well as positive associations between hint use and solved outcomes. High-LH students showed more avoidance patterns, with skipping strongly tied to unsolved outcomes. In the comparison of system-based intervention conditions, students without intervention had the highest lift for persistence-success links, while the with-intervention group had stronger patterns involving skipping behaviors leading to unsolved outcomes. Outcome-specific analysis showed that not skipping was consistently associated with solved problems across all groups, while skipping without hints predicted unsolved outcomes. Practical implications and recommendations are discussed.

John Paul P. Miranda

The dataset was collected to examine and identify possible key topics within these texts. Data preparation such as data cleaning, transformation, tokenization, removal of stop words from both English and Filipino, and word stemming was employed in the dataset before feeding it to sentiment analysis and the LDA model. The topmost occurring word within the dataset is "development" and there are three (3) likely topics from the speeches of Philippine presidents: economic development, enhancement of public services, and addressing challenges. The dataset was able to provide valuable insights contained among official documents. While the study showed that presidents have used their annual address to express their visions for the country. It also presented that the presidents from 1935 to 2016 faced the same problems during their term. Future researchers may collect other speeches made by presidents during their term; combine them to the dataset used in this study to further investigate these important texts by subjecting them to the same methodology used in this study. The dataset may be requested from the authors and it is recommended for further analysis. For example, determine how the speeches of the president reflect the preamble or foundations of the Philippine constitution.