In higher education, artificial intelligence (AI) tools offer significant potential that include personalized learning experiences, research support, student support services, and administrative efficiencies. For example, these tools can provide access to virtual tutoring, automatic grading, and enhanced accessibility through multilingual support and adaptive learning technologies. However, the use of AI in academics has raised ethical considerations such as data privacy, algorithmic bias, increased instances of plagiarism, and the potential for replacing human interaction. Although universities report concerns integrating AI into its curriculum, there are many potential positive student impacts including preparing them for a wider range of career pathways, cultivating essential academic and research skills, and promoting innovation. Furthermore, through these experiences, students are equipped to navigate and contribute meaningfully to an increasingly AI-driven world.

Hands-on experience with AI fosters critical thinking and problem-solving skills. Students learn to analyze AI-generated outputs, evaluate their accuracy and relevance, and make informed decisions about when and how to leverage AI tools effectively. Engaging students with AI-driven assignments within their curriculum also enhances their research skills. For example, students who utilize AI for qualitative data analyses acquire a range of valuable skills that enhance their research capabilities. Specifically, students enhance their ability to interpret and critique AI-generated themes and patterns allowing them to identify discrepancies and refine their coding schemas. Additionally, the use of AI visualization tools further develops students' ability to create and interpret visual data representations (e.g., word clouds, thematic maps). These skills are crucial for identifying connections and patterns within the data that might not be immediately apparent.

As a result of the importance of this trending topic in higher education, this proposed presentation will focus on the implementation of AI into qualitative research university classes. Specific assignment examples will be shared that are centered on students’ independent data analysis processes of developing codes and themes for qualitative findings and their experiences of utilizing AI to compare its output to their own coding procedures. Implementing AI into qualitative research university classes serves multiple purposes, particularly in preparing students for their thesis and dissertation research. This assignment not only familiarizes students with advanced technological tools but also bridges the gap between traditional qualitative methods and modern analytical techniques. By incorporating AI into university curriculum, students gain hands-on experience in using cutting-edge software to develop codes and themes for qualitative data. Additionally, integrating AI into the curriculum reflects the evolving landscape of academic research, where technological proficiency is increasingly valued. Essentially, by exposing students to AI early in their academic careers, universities can ensure that their graduates are well-prepared for the demands of contemporary research environments. This results in improvement of student employability and contributes to the advancement of the field by encouraging innovative approaches to qualitative research.

Keywords: Artificial intelligence, higher education, research, qualitative research, curriculum development.