Abstract
In this study, we examine the adoption and integration of generative AI into educational pedagogy, focusing on its impact on student academic performance and educator perspectives in higher education. Moving beyond commonly discussed challenges and opportunities, this research explores underexamined areas, such as discipline-specific applications of AI in education, long-term impacts on student engagement, and the ethical considerations of AI adoption. The study identifies the types of AI technologies integrated into teaching and learning in six institutions in Eastern Canada. Mixed methods are employed, including simple random sampling from private and public higher education institutions as well as thematic analysis. Data is collected through surveys and analyzed using statistical software to assess the impacts of generative AI on both the academic outcomes of students and the practical implications for educators. This research is subject to limitations such as the relatively small sample size and the potential for institutional bias, which may affect the generalizability of the findings to other contexts. The findings will contribute to a deeper understanding of how generative AI can be effectively incorporated across different academic disciplines to enhance student learning and educators’ teaching practices in higher education.
Presenters
Jemima SarfoStudent, Masters, Mount Saint Vincent University, Nova Scotia, Canada Michael Lin
Assistant Professor, Faculty of Education, Mount Saint Vincent University, Nova Scotia, Canada
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
Considering Digital Pedagogies
KEYWORDS
GENERATIVE ARTIFICIAL INTELLIGENCE, HIGHER EDUCATION PEDAGOGY, STUDENT ACADEMIC PERFORMANCE
