Human-AI Collaboration in Scientific Writing Training: A Quantitative Evaluation of Learning Effectiveness and Academic Integrity - Eko Risdianto; Nanik Setyowati; Mohammad Qaiz Rezvani; M. Abdul Jamal; Mageswaran Sanmugam; M. Esad Kuloğlu (CV Media Inti Teknologi, 2026)
Background: The rapid advancement of Artificial Intelligence (AI) has significantly transformed academic writing practices, offering both opportunities and ethical challenges, particularly related to plagiarism and over-reliance on automated tools.Objectives: This study aims to evaluate the effectiveness of AI-assisted scientific writing training in enhancing participants’ writing skills, academic integrity awareness, and writing efficiency through a Human-AI Collaboration approach.Methods: A quantitative descriptive method was employed using a structured questionnaire consisting of 20 Likert-scale items administered to 85 participants. The instrument measured five dimensions: relevance and conceptual understanding, instructional quality, writing skill improvement, academic integrity awareness, and perceived impact. Data were analyzed using descriptive statistics, including mean scores and percentage distributions.Results: The findings reveal that the training achieved a very high level of effectiveness, with an overall mean score of 3.59. All variables were categorized as very high, with academic integrity awareness obtaining the highest mean score (3.70), indicating strong improvement in ethical understanding. Writing skill improvement showed relatively lower scores, suggesting the need for continuous practice.Conclusion: AI-assisted scientific writing training based on the Human-AI Collaboration framework is highly effective in improving scientific writing competence and promoting ethical awareness. This approach provides a balanced model integrating technical skills and academic integrity in AI-supported writing practices.