Access to artificial intelligence alone does not guarantee effective learning, leaving working, part-time, and remote students at the highest risk of being left behind as Philippine education shifts from basic tech adoption to institutional enablement.
According to a new study by Instructure, the maker of Canvas Learning Management System (LMS), in collaboration with local research firm The Fourth Wall, titled The Great Divide of AI in Lifelong Learning, the country’s next AI divide is not between those who use AI and those who do not but between learners with institutional support and those left to navigate the technology independently.
The research surveyed 207 education practitioners across higher education and technical-vocational institutions in Metro Manila, Metro Cebu, and Metro Davao. The findings were unveiled at CanvasCon Philippines 2026 at Conrad Manila, where over 500 educators gathered to discuss operationalizing AI readiness.
The Emerging Support Divide for Non-Traditional Learners
On a 5-point scale, educators rated their confidence that remote, part-time, and working learners will be equally supported as AI reshapes learning at just 2.66—one of the lowest readiness scores in the study.
While AI can make education more flexible, the students who depend most on that flexibility may lack adequate institutional support. Educators named work and livelihood responsibilities (67%) and cost constraints (65%) as the primary barriers to lifelong learning, warning that AI could either ease or compound these frictions depending on whether institutional support becomes as flexible as the learning itself.
“AI adoption is already underway, but institutionalization has yet to catch up. Encouragement isn’t the same as structure, clarity, and guidance. If AI isn’t connected to a coherent learning strategy, both learners and educators will be left floundering, creating a clear divide between supported and unsupported learners,” said Darren Read, Managing Director, APAC at Instructure.
Compounding this challenge, educators currently lack visibility into whether AI’s benefits are reaching students equitably, rating their institution’s tracking of resource distribution at a low 2.74 out of 5.
“Equity is not only an access problem; it is also a measurement problem. Institutions cannot meaningfully address an AI divide if they cannot see who has access, who is participating, and who is actually benefiting,” said John Brylle L. Bae, Research Director at The Fourth Wall.
AI Implementation Gains Momentum Beyond Experimentation
The survey suggests that AI activity in Philippine higher education is moving beyond early exploration. Half (50%) of surveyed institutions currently offer AI literacy or digital-skills programs for students, while only 18% report still being in the exploration phase without a formal strategy. Additionally, institutions are deploying practical tools, including staff-facing AI systems to support teaching and course design (41%), AI tools integrated directly into course delivery (40%), and AI-powered tutoring assistants (38%).
Despite this momentum, institutional readiness lags behind tool deployment. Educators rated confidence in their institution having a clear AI strategy within lifelong-learning pathways at 3.08 out of 5, while confidence in faculty and staff training fell to 2.87 out of 5. Overall, 43% of respondents cited limited access to devices or paid tools as a primary barrier, while 38% highlighted a lack of formal training and guidance
Regional Bottlenecks and Policy Context
While overall readiness levels did not differ significantly across geographical regions, immediate resource constraints varied. Technology and digital infrastructure emerged as the primary bottleneck in Metro Manila (46%), whereas funding constraints were more prominent in Metro Cebu (45%) and Metro Davao (55%). Metro Cebu respondents also highlighted faculty training as a pressing concern (36%).
To bridge these gaps, the report outlines five core priorities for institutional enablement: expanding physical access to devices and tools; setting clear rules for ethical use; building faculty and learner capability; integrating AI into curricula and lifelong learning pathways; and establishing equity tracking metrics.
The urgency to build institutional readiness comes as the Commission on Higher Education (CHED) issued national guidelines for responsible AI use in higher education. The guidelines require transparency in students’ use of AI and stronger human oversight in consequential areas such as grading, admissions, scholarships, student progression, and faculty evaluation. Against that backdrop, the study’s low readiness scores for faculty training (2.87 out of 5) and equity tracking (2.74 out of 5) suggest many institutions will need dedicated support to put those expectations into practice.
The survey findings reflect the perceptions of education practitioners and provide an indicative view of institutional priorities, practices, and readiness, rather than a nationally representative sample of all Philippine education institutions.