PROMPTING BEHAVIOR GENERATIVE AI BY POSTGRADUATE STUDENTS OF STATE UNIVERSITY OF MALANG IN WRITING SCIENTIFIC ARTICLES - Nuraini, Elshinta Gita; Ansor, Sokhibul; Safii, Moh; Abdul Rahman, Abd. Latif (Progam Studi Ilmu Perpustakaan UIN Sumatera Utara Medan, 2025)
This study aims to identify patterns of prompting behavior of graduate students of State University of Malang in utilizing Generative AI, especially ChatGPT, for scientific article writing. Using a qualitative approach based on grounded theory, data was collected through in-depth interviews with two main informants. The results showed that students applied a systematic and iterative prompting approach in five main aspects: input semantics, output customization, error identification, prompt improvement, and interaction & context control. Analysis through open, axial, and selective coding stages resulted in the SURE Framework (Strategic Prompt Design, Utilization & Output Optimization, Refinement & Iterative Interaction, Evaluation & Academic Validation), which describes the practice of active and reflective use of GenAI in an academic context.[LI1] This research contributes to a deeper understanding of human-GenAI interaction in academic contexts. It underscores the importance of prompting literacy as a critical skill to enhance the effectiveness and integrity of AI-assisted scientific writing. Furthermore, the findings highlight the need for developing digital and information literacy among students and academics, in line with the demands of the digital era. Specifically, within the field of Library and Information Science, this study provides a foundational perspective for designing adaptive curricula that integrate digital technologies into learning, research, and information management. Such an adaptive curriculum is expected to equip students with the ability to use AI tools more effectively and ethically in academic settings. Additionally, the study offers practical insights for improving academic information management systems, thereby promoting more efficient and relevant learning and research processes. Overall, this research bridges the gap between emerging AI technologies and pedagogical innovation in higher education.