This study investigates the evolving perceptions, attitudes, and understanding of Generative Artificial Intelligence (GenAI) technologies among first-year undergraduate Computer Science students, in the context of enhancing GenAI literacy in Higher Education. The study was conducted during the academic years 2023–24 and 2024–25 as part of a first-semester introductory module on Artificial Intelligence.

Participants completed two online surveys, one prior to and one following exposure to two GenAI-focused lab tasks. The first involved a practical adaptation of the Turing Test, where students collaborated in designing prompts and distinguishing between GenAI-generated and human responses. The second required students to evaluate a set of imagined GenAI use cases in Higher Education from both student and educator perspectives, drawing on relevant institutional policies and guidance. These activities aimed to support students in developing critical awareness of GenAI’s capabilities, limitations, ethical implications, and applications in academic settings.

Analysis of matched pre- and post-intervention responses (n=33) reveals a number of significant changes. While initial enthusiasm for GenAI remained high, post-intervention responses indicated increased awareness of the tools’ limitations, particularly in handling complex tasks, and improved confidence in identifying AI-generated content. Participants demonstrated a more nuanced understanding of ethical considerations, including privacy and the risk of overreliance, and showed greater critical engagement with GenAI-related challenges. Students’ willingness to use GenAI in their academic work increased, and many viewed its anticipated impact on the job market as a transformation with new opportunities, rather than a threat.

Although limited by small sample size and the absence of a control group, the findings suggest that early, structured engagement with GenAI within the curriculum can promote more informed and balanced student perspectives. The intervention supported greater confidence in using GenAI tools and contributed to the development of critical GenAI literacy. These insights will inform future iterations of the module and contribute to broader pedagogical approaches to integrating GenAI in Higher Education.

Keywords: AI literacy, Computer Science education, Higher Education, student perceptions, generative AI.