Challenges and Strategies of College Teachers in Validating AI-Generated Student Submissions

Generative artificial intelligence (AI) tools shape how students draft, revise, and submit academic work.This shift creates a practical dilemma for higher education.Teachers must judge learning evidence when authorship cues weaken and detection tools remain imperfect.This study examined the challenges college teachers face when they validate AI-assisted or AI-generated student submissions and the strategies they apply to protect academic integrity while sustaining meaningful learning.The inquiry used a descriptive qualitative design and drew on semi-structured interviews with ten college teachers from Iligan Medical Center College, selected through purposive sampling, with data collection conducted from February to May 2025.Interview audio was recorded and transcribed, then analyzed through a thematic approach.Results show three main challenge clusters: AI outputs often appear sufficiently fluent to obscure authorship cues; teachers face ethical strain when they suspect AI use without firm proof; and conventional task formats invite over reliance on AI tools, which complicates judgment of students' own thinking.Teachers responded through layered strategies that include transparent rules on acceptable AI use, assessment redesign toward authentic and oral tasks, stronger requirements for citation and disclosure, increased reliance on familiarity with a student's capability, and stricter control of device use during in class tasks.