Adzikirani
Politeknik Negeri Malang

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

Rethinking Academic Writing Assessment in the Generative AI Era: EFL Lecturers’ Perceptions and the Shift Towards Process-Based Assessment Fitrotul Maulidiyah; Rendi Yoga Saputra; Adzikirani; Rizka Rahmawati; Rachmat Arnanda
Journal of Language Development and Linguistics Vol. 5 No. 2 (2026): September 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/jldl.v5i2.19018

Abstract

Generative artificial intelligence (GenAI) has changed how students approach academic writing, creating new possibilities for language support while also challenging conventional approaches to assessment. When AI tools can generate, revise, and refine academic texts, evaluating only the final written product provides limited evidence of students’ actual learning and authorship. This study investigates EFL lecturers’ perceptions of the benefits and risks of GenAI in academic writing and examines how assessment practices can be adapted to maintain academic integrity and authenticity. A convergent parallel mixed-methods design was employed. Quantitative data were collected through a questionnaire completed by 84 EFL lecturers, while qualitative insights were obtained from open-ended responses and follow-up interviews. The findings indicate that lecturers recognise the potential of GenAI to improve grammatical accuracy, organise ideas, and support students’ confidence in writing. At the same time, plagiarism, over-reliance on AI, reduced critical engagement, and concerns about linguistic standardisation were identified as major challenges. Although 62% of respondents supported giving greater emphasis to the writing process, only 29% reported implementing process-focused assessment in their courses. The findings suggest a substantial gap between lecturers’ perceptions of the need for assessment reform and their current practices. To address this gap, the study proposes a process-oriented approach that combines multiple drafts, revision records, reflective commentary, AI-use disclosure, and opportunities for oral explanation. The study argues that effective assessment in the GenAI era should not focus primarily on detecting AI-generated text, but on generating evidence of students’ thinking, decision-making, and engagement throughout the writing process.