Artificial Intelligence Consumption and the State of Learning Engagement among Grade 11 Senior High School Learners in Malaybalay City

Authors

Celia B. Capio

Master of Arts in Teaching (MAT) Valencia Colleges (Bukidnon), Inc. Hagkol, Valencia City, Bukidnon (Philippines)

Article Information

DOI: 10.47772/IJRISS.2026.1026EDU0099

Subject Category: Social science

Volume/Issue: 10/26 | Page No: 1175-1185

Publication Timeline

Submitted: 2026-02-13

Accepted: 2026-02-19

Published: 2026-02-27

Abstract

This study examined the relationship between Artificial Intelligence (AI) consumption and learning engagement among Grade 11 senior high school learners in selected public secondary schools in Malaybalay City. Using a quantitative cross-sectional design, data were collected from 120 respondents through a structured survey consisting of an AI Usage Questionnaire and Hart’s Learning Engagement Tool. Instruments underwent content validation and internal consistency reliability testing prior to data collection.
Descriptive findings indicated that learners sometimes used AI-powered educational tools and reported moderate perceptions of both the benefits and challenges of AI-driven learning. In contrast, learners demonstrated high levels of emotional, behavioral, and cognitive engagement. Pearson product-moment correlation analysis revealed statistically significant but low-to-moderate positive associations between AI tool usage (r = .291, p = .001), perceived AI benefits and challenges (r = .280, p = .002), and overall learning engagement. Effect size estimates suggest that AI consumption accounts for a modest proportion of variance in engagement outcomes.
The findings indicate that AI use functions as a supplementary learning support rather than a primary determinant of student engagement. Results underscore the importance of structured, guided, and context sensitive AI integration to maximize educational benefits while mitigating risks such as overreliance and reduced critical engagement. Future research employing longitudinal or mixed-methods designs is recommended to clarify causal pathways and examine long-term academic outcomes.

Keywords

Artificial Intelligence, Learning Engagement, Senior High School, Educational Technology, Student Perception

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