Generative AI and the Nuance of Panitikang Filipino: A Narrative Review of Technology-Mediated Teaching in Philippine Language Education

Abstract

Background: Generative artificial intelligence has entered language classrooms faster than the evidence base has matured, with most published work centered on English and second-language learning. Filipino language and literature instruction presents a distinct problem because linguistic form, code-switching, historical reference, and culturally embedded meaning can expose weaknesses in large language models. Objective: This narrative review examined evidence relevant to the use of generative AI, digital translation tools, and e-learning platforms in Filipino instruction, with emphasis on academic writing, reading comprehension, digital literacy, academic integrity, and culturo-linguistic preservation. Methods: Literature published from 2019 to 2025 was identified through structured searches of multidisciplinary education databases, publisher platforms, UNESCO resources, and the ACL Anthology. Evidence was organized by pedagogical function, learner outcome, teacher competence, integrity risk, and Filipino-specific linguistic or cultural performance. Results: GenAI research reported frequent use for content generation, feedback, explanation, and writing support, but direct evidence from Filipino private-school classrooms was absent. Filipino benchmarks documented substantial limitations in cultural knowledge, reading comprehension, translation, morphology, and generation. Philippine digital-learning studies showed both instructional potential and persistent access, design, and teacher-readiness constraints. Conclusion: GenAI can support Filipino instruction when teachers treat model output as material for critique, revision, and comparison rather than as authoritative language evidence. Filipino-specific validation, transparent AI-use rules, and culturally accountable teacher mediation remain essential.