THEORETICAL FRAMEWORK FOR AN AI-ENHANCED PEDAGOGICAL TECHNOLOGY TO DEVELOP SELF-EDITING SKILLS OF DOCTORAL STUDENTS

Abstract

Self-editing skills are crucial for academic career of a future researcher though doctoral students frequently lack proficient self-editing skills as these skills are at the periphery of academic writing courses. The rapid integration of AI-enhanced writing tools into academia presents both transformative potential and significant risks highlighting a gap in theoretical frameworks for their responsible use. This study aims to design a theoretical framework for an AI-enhanced pedagogical technology specifically targeted at developing the self-editing skills of doctoral students, ensuring a balance between leveraging AI's efficiency and preserving critical academic rigor and authorship. Using integrative literature review the authors defined core self-editing subskills and assessment criteria. A systematic evaluation of over 70 AI writing tools was conducted using a rubric based on efficiency, accessibility, and feedback features. The pedagogical framework was constructed by synthesizing system and activity-based methodologies with reflective, collaborative, and self-directed learning approaches. The authors defined three stages of self-editing skills training with corresponding skills, knowledge criteria, and proficiency levels. The study mapped 15 selected AI tools to each editing stage based on functional alignment. The core outcome is a structured AI-enhanced pedagogical technology, defined by key principles (learner autonomy, optimal AI support, critical reflection, personalized feedback) and implemented through four operational stages: preparatory, information, training, and reflexive. This work provides a novel, reproducible theoretical framework that systematically integrates AI-enhanced writing tools into doctoral education. It moves beyond tool-centric adoption by embedding AI within a pedagogical structure that promotes critical generative artificial intelligence (GAI) literacy. The framework can be used for higher education institutions and doctoral training programs.