TDNet Discover

The purpose of this mixed-method survey-based study addresses the underexplored area of undergraduate student perceptions of artificial intelligences (AI) impact in academia, specifically within the FCSEI in Romania. This research study will provide comprehensive findings on how AI can revolutionize teaching methodologies, learning processes, and evaluations to better prepare students with the skills needed for success in a technologically driven business landscape. The data-driven findings have the potential to enable strategic planning for incorporating AI into academic frameworks, ensuring alignment with corporate workforce demands and enhancing student preparedness for the challenges of the modern business landscape. The research utilized online surveys from 91 FCSEI undergraduate students and employed quantitative and qualitative data analysis methodologies. The study employs a mixed-methods approach, utilizing both quantitative and qualitative data. Constructivism and connectivism serve as the theoretical framework, guiding the exploration of AI's impact on teaching, learning, and evaluation practices. The research questions focus on the perceptions of students, faculty, and administrators regarding AI integration, the challenges and opportunities associated with AI adoption, and the strategies for aligning AIenhanced education with workforce demands. The data collection method will include online surveys, interviews, and focus groups involving undergraduate business students, faculty members, and administrators at FCSEI. By addressing the challenges and leveraging the opportunities associated with AI integration, this study seeks to bridge the gap between academia and industry, ensuring that undergraduate business education remains relevant and responsive to the workforce's evolving demands in the AI era. 2 The study reveals undergraduate students perceive AI integration in education positively, with students possessing higher AI knowledge recognizing more significant benefits of AI technology, underscoring the need for increased AI literacy to enhance student engagement and educational outcomes. Gender, academic major, and GPA did not significantly influence perceptions, suggesting widespread acceptance across diverse demographics. Future research should include longitudinal studies to track changes in AI perceptions over time, providing deeper insights into AI's long-term effects on education. Expanding the research to diverse populations from various institutions and cultural backgrounds will improve the generalizability of findings. Examining the effectiveness of specific AI tools and applications while addressing ethical implications like data privacy and algorithmic bias could help develop responsible and equitable AI integration strategies. Understanding faculty and administrative perspectives on AI could support comprehensive policy development and practical implementation in higher education.