Ai-augmented Pedagogical Framework for Superior Literature Review Proficiency
Year: 2025
Type: article
Abstract: This study addresses the persistent challenge of inefficiency and limited precision in conducting literature reviews, which often hinder academic progress. To overcome these obstacles, the research aimed to develop and evaluate an AI-Augmented Pedagogical Framework for Superior Literature Review Proficiency, named CRAFT (Collecting, Reviewing, Analyzing, Framing, and Tailoring). Integrating advanced AI tools—such as SciSpace, Semantic Scholar, Google Scholar, Mendeley, Zotero, QuillBot, Grammarly, and Turnitin—CRAFT seeks to streamline repetitive tasks, accelerate data synthesis, and uphold academic integrity within the literature review process. A mixed-methods design was employed. Data were gathered through semi-structured interviews, online surveys, and pre-test/post-test assessments with 30 researchers from diverse disciplines. MANOVA results indicated significant improvements across five core skill areas: searching, reviewing, analyzing, synthesizing, and presenting. Participants achieved a 60.8% reduction in task completion time and attained a 92% precision rate. Qualitative thematic analysis reinforced these findings, underscoring enhanced critical thinking, deeper comprehension, and stronger interdisciplinary collaboration. Despite challenges, including initial costs for AI tools and the need for human oversight to address contextual limitations, the study concludes that CRAFT provides a flexible, accessible, and personalized framework adaptable to various research contexts. The framework holds potential to transform academic practices and promote scalable AI integration in education. Future research should focus on refining CRAFT’s adaptability, enhancing cost-effectiveness, and addressing ethical implications within diverse educational settings.
Author: Journal of Theoretical and Applied Information Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7082990730
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