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Short Title

Integration of Gen AI in Research: Perceptions of Aspiring Leaders

Keywords

Gen AI in Education, Gen AI in higher education, Gen AI in Academic Research, Educational Leadership, Distributed Cognition Theory

Abstract

As aspiring educational leaders, principal candidates must understand the widespread impact of generative artificial intelligence (Gen AI) in education. This qualitative phenomenological study explored the perceptions of aspiring educational leaders regarding Gen AI in academic research, highlighting the ethical concerns and challenges they perceive, revealing potential opportunities for AI integration in education, and assessing how these perceptions may influence their future leadership practices. The theory guiding this study was the distributed cognition theory, as it shows the dynamics of human cognition and AI cognition, making it possible to understand the perceptions of aspiring educational leaders regarding Gen AI integration in academic research. By examining these aspects, the research provided insights into how aspiring educational leaders might approach and implement AI technologies in academic research and education contexts. The research was conducted through a reflection assignment at the end of an introductory research course. The reflection prompts included demographic and background information, perceptions of the ethical use of Gen AI tools, the role of Gen AI tools as research assistants, the effectiveness and challenges related to using Gen AI tools and leveraging Gen AI's potential in education as an aspiring educational leader. The archived reflection data were analyzed using a qualitative phenomenological design method. The study sought to provide insights that could inform professional development, strategies for technological integration, and institutional approaches to Gen AI adoption in academic research courses. The research highlighted how emerging educational leaders perceive Gen AI's role in academic research and educational settings, moving beyond surface-level assessments to understand the complex interplay of technological, pedagogical, and personal factors.

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