Fauziyyah Mufida Suni, Rizka Maulidya, Annisa Wahyu Dwi Putri

SECONDARY SCHOOL TEACHERS’ PERSPECTIVES ON AUTHORSHIP ANALYSIS IN THE CONTEXT OF AI-PLAGIARISM ON STUDENTS’ WORKS IN ENGLISH LESSONS

Introduction

Secondary school teachers’ perspectives on authorship analysis in the context of ai-plagiarism on students’ works in english lessons. Secondary English teachers share perspectives on authorship analysis for detecting AI plagiarism in student work. Insights into challenges, resources, and strategies for academic integrity in AI education.

Abstract

The rapid integration of Artificial Intelligence (AI) into educational environments has created new challenges for maintaining academic integrity, particularly concerning AI-assisted student work. As traditional plagiarism-detection tools prove insufficient against sophisticated AI-generated content, there is a growing need to explore teacher-driven detection strategies. This descriptive qualitative study addresses this gap by investigating teachers’ views on using authorship analysis as a method to identify AI-generated content in student assignments. The research utilised semi-structured interviews with ten secondary school teachers specialising in the English language. Data were analysed using thematic analysis. The findings revealed three primary themes: (1) The ELT Vulnerability to AI, highlighting an increasing concern about AI-assisted plagiarism in English language teaching; (2) The Resource Gap, where teachers express a lack of sufficient training and institutional resources to address the challenge effectively; and (3) The suggestion to combine AI detection tools, manual authorship analysis, and conversations with students as a strategy. The study highlights how institutional policies and the specific local educational context influence teachers' attitudes toward AI and academic integrity. There is a need to establish clear AI policies and promote AI literacy to students.

Review

This timely and pertinent study addresses a critical and evolving challenge within contemporary education: the detection of AI-generated content in student assignments. Focusing specifically on secondary school English language teachers, the research employs a descriptive qualitative methodology to explore their perspectives on authorship analysis as a strategy against AI-plagiarism. By foregrounding teacher experiences and insights, the paper effectively fills a crucial gap, given the inadequacy of traditional plagiarism detection tools against sophisticated AI-generated text. The chosen methodology, involving semi-structured interviews with ten subject-specialist teachers and subsequent thematic analysis, is appropriate for capturing nuanced qualitative data on this complex issue. The study yields three significant findings that collectively paint a comprehensive picture of the current landscape. Firstly, "The ELT Vulnerability to AI" aptly highlights the specific susceptibility of English language teaching to AI-assisted plagiarism, underscoring the urgency for targeted interventions. Secondly, "The Resource Gap" reveals a critical systemic issue, with teachers expressing a palpable lack of adequate training and institutional support to effectively navigate this new pedagogical challenge. Finally, the suggestion to combine AI detection tools, manual authorship analysis, and direct conversations with students offers a pragmatic and multi-faceted strategy, acknowledging the complexity of the problem beyond technological solutions alone. These themes provide valuable insights into both the problems faced and potential pathways forward. While the abstract outlines a compelling case and robust findings, a full review of the paper would benefit from deeper elaboration on certain aspects. The sample size of ten teachers, while appropriate for an in-depth qualitative exploration, inherently limits the generalizability of the findings, prompting consideration for broader studies in the future. Furthermore, while the proposed combined strategy is insightful, a more detailed discussion within the full paper on practical frameworks for implementing effective manual authorship analysis or structuring productive student conversations would enhance its utility for practitioners. Nevertheless, the study makes a significant contribution by drawing attention to the urgent need for clear institutional AI policies, enhanced teacher professional development, and greater AI literacy for students, offering a vital foundation for institutions grappling with the implications of AI on academic integrity.

Full Text

Comments

You need to be logged in to post a comment.