Research flow navigator (RFN): An AI-integrated visual pedagogical framework for enhancing research proposal development in higher education

Abstract

Research methodology education plays a critical role in developing students’ research competency, critical thinking, and evidence-based decision-making skills in higher education. Despite its importance, many undergraduate and postgraduate students continue to encounter substantial difficulties in developing coherent research proposals. Common challenges include identifying research problems, establishing research gaps, formulating research objectives and questions, synthesizing literature, and understanding the logical relationships among different proposal components. Although various instructional approaches and digital technologies have been introduced to improve research methodology teaching, many existing practices emphasize isolated proposal components rather than providing an integrated learning framework that supports students throughout the entire proposal development process. Furthermore, the rapid advancement of generative artificial intelligence (AI) has created new opportunities to enhance research learning; however, its educational effectiveness depends on sound pedagogical integration rather than technology alone. This conceptual paper proposes the Research Flow Navigator (RFN), an AI-integrated visual pedagogical framework designed to enhance research proposal development in higher education. The proposed framework synthesizes principles of visual learning, instructional scaffolding, constructive alignment, self-regulated learning, and responsible AI-assisted learning to provide students with a structured and systematic pathway for understanding the interrelationships among research proposal components. Unlike conventional instructional approaches, RFN conceptualizes proposal development as an interconnected learning journey supported by visual mapping, guided learning activities, and ethical AI assistance. It is anticipated that the proposed framework will strengthen students’ conceptual understanding, improve the coherence of research proposal development, and support lecturers in delivering research methodology courses through an innovative and evidence-informed pedagogical approach. The paper concludes by discussing the theoretical and practical contributions of RFN and proposes directions for future empirical validation in higher education.