Generative Artificial Intelligence in Higher Education: A Mixed-Methods Study on Faculty Usage and Perceived Impacts

Abstract

This study investigates the diverse practices and impacts of generative artificial intelligence (AI) in higher education, focusing on faculty usage of AI tools. Employing a rigorous mixed-methods approach, the research combines a structured quantitative questionnaire exploring multiple dimensions of AI integration including pedagogical, scientific, institutional, and ethical uses with a qualitative experimental phase where faculty engage with standardized AI prompts to generate and analyze AI-produced content. The study aims to map declared usage patterns, evaluate perceived effects on teaching effectiveness and workload, and analyze disciplinary and seniority-based variations in adoption. Results reveal higher engagement and satisfaction among early-career faculty and those in engineering disciplines, alongside stronger ethical awareness, while more experienced faculty and those in literature fields report lower adoption. Quantitative analyses include descriptive statistics, ANOVA, factor analysis, and regression modeling, while qualitative data undergo thematic coding and natural language processing to identify recurrent usage themes and assess response quality. This research contributes to an interdisciplinary framework combining educational sciences, technological and pedagogical analysis, offering actionable insights for policy development and responsible, equitable AI integration in academia.