Assessing academic integrity patterns among pre-service teachers using AI-based plagiarism detection - Irish Arroyo; Sittie Nor Aisha Rusiana; Daniere Maryje Tabada; Kim Silva; Carla Marie Rubio; Esmeraldo Sarad; Cathy Mae Toquero (Journal of Artificial Intelligence in Education & Learning Innovation, 2026)
Background: The advent of artificial intelligence has intensified concerns about academic dishonesty among students, particularly in written outputs. Plagiarism, a common form of misconduct, involves using others’ ideas without proper attribution. Objectives: This study aimed to determine the degree and patterns of academic integrity in the pre-service teachers’ book reviews. Methods: Employing a descriptive research design through document analysis, the study used purposive sampling to collect 40 book reviews, applying set inclusion and exclusion criteria. An AI-based plagiarism detection tool, Grammarly, was used to identify instances of plagiarism and assess the level of academic integrity reflected in the outputs. Descriptive statistical methods were applied to examine plagiarism levels across different sections of the book reviews. Results: Results showed that the majority of pre-service teachers demonstrated a very high level of academic integrity in their book reviews, scoring 97% or interpreted as students committing 3% plagiarism. Furthermore, sectional analysis showed that the introduction and conclusion exhibited higher integrity, while the body contained the most instances of plagiarism. This suggests that students struggled more with sections requiring critical thinking, original insights, and proper citation. Most plagiarism cases were linked to failure to cite sources and unintentional misuse of references. Conclusions: Teacher Education Institutions integrate AI-supported evaluation tools and plagiarism detection systems into instruction and assessment. Embedding academic integrity modules and discussions on AI ethics is also encouraged. Future research should involve larger and more diverse samples and utilize multiple AI detection tools to enhance the reliability and validity of findings through cross-verification.