The inevitability of AI: a study of undergraduate students’ perceptions of AI tools in their future careers

Authors

  • Mónica Colón-Aguirre University of South Carolina, United States of America
  • Kawanna Bright East Carolina University, United States

DOI:

https://doi.org/10.47989/ir30iConf47308

Keywords:

generative AI, undergraduate students perceptions of AI, higher education, information behavior

Abstract

Introduction. Artificial intelligence (AI) tools garner more attention every day, questions have arisen regarding their possible negative impacts in future job markets. Some predict a potential for massive job losses, especially in high-skilled jobs. This study seeks to explore undergraduate students’ perceptions of how these tools might affect their future careers.

Method. This study follows a case study design, employing phenomenological interviews as a research method. The data set was made up of interviews with 17 undergraduate students.

Analysis. Data were analysed by employing constant comparative analysis, with various rounds of coding including the creation of open, axial, and structural codes.

Results. Students saw AI tools as an inevitable part of their future work. Participants expressed their intention to learn how to optimize their use of various tools, which they see as having the potential to positively benefit them in their future careers. They do not perceive AI to be a viable substitute for their skills, especially in terms of identifying misinformation and emotions.

Conclusions. Academic institutions must provide curricular spaces which allow students to harness the power of AI tools. While employers should also make efforts to train employees to make the most of AI tools.

Published

2025-03-11

How to Cite

Colón-Aguirre, M., & Bright, K. (2025). The inevitability of AI: a study of undergraduate students’ perceptions of AI tools in their future careers. Information Research an International Electronic Journal, 30(iConf), 443–449. https://doi.org/10.47989/ir30iConf47308

Issue

Section

Peer-reviewed papers

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