In the advent of the AI revolution, university students’ adoption of artificial intelligence was predominantly concentrated on a few specific programs, most notably Grammarly and ChatGPT. These tools were employed mainly for two purposes: rectifying linguistic errors and generating ideas or structural frameworks for academic endeavours. While the use of such programs streamlined certain processes, it inadvertently fostered a homogenization of thought patterns, leading to a conspicuous lack of diversity and creativity in the output. This narrow use of AI shifted the academic approach from being data-driven—where students first gather and interpret data before forming arguments—to one where students retrofitted data to suit the pre-constructed structures and narratives provided by AI. This phenomenon raises significant questions concerning the future of education, its impact on fostering creativity and critical thinking among students, and its implications for a labour market that increasingly values data-driven and critically skilled individuals. In this paper, we delve deeper into these challenges, exploring potential remedies for the restrictive reliance on limited AI tools.