Reaching Higher
Assessing the Impact of Generative AI Integration on Student Learning and Educator Approaches in Higher Education
Paper Presentation in a Themed Session Jemima Sarfo, Michael Lin
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.
Investigating Open-Source Large Language Models in Digital Pedagogies
Paper Presentation in a Themed Session Saeed Saffari, Jeeho Ryoo, Oscar Lin, Michael Lin
As higher education continues to embrace digital pedagogies, large language models (LLMs) present opportunities for improving student-centered learning. Open-source LLMs make advanced AI technology available to a wide range of researchers, developers, and organizations and can be adapted and fine-tuned for specific tasks or domains, allowing for more specialized and resource-efficient educational applications. Higher education often involves more self-directed and autonomous learning, especially in online and distributed learning environments. Additionally, since higher education incorporates more advanced and specialized topics, AI systems must support complex subject matter and facilitate interdisciplinary connections. This study examines the integration of open-source LLMs into digital pedagogical frameworks to promote critical thinking, collaboration, and self-regulation in higher education contexts. The study employs a scoping review approach to map existing literature and current implementations, identifying key concepts and gaps in the research community. Furthermore, it investigates effective training methods for educators to ensure these tools are implemented to their fullest pedagogical potential. Through the analysis of case studies and current practices, the study demonstrates the transformative impact of LLMs in creating more inclusive and responsive educational experiences. The findings from this study provide key insights into how higher education institutions can better integrate LLMs, offering a framework for future research on the development of AI-driven educational tools.
From Passive Reading to Active Learning: Leveraging Annotation Tools in Asynchronous Online Instruction View Digital Media
Paper Presentation in a Themed Session Rena Rockwell
This paper explores how annotation tools can transform passive reading into active learning in asynchronous online instruction. By allowing students to interact with digital content through comments, highlights, and collaborative annotations, these tools promote engagement and critical thinking, even outside of real-time interactions. The review examines how annotation tools enhance comprehension, foster collaboration, and support knowledge retention, making them valuable assets in an asynchronous learning environment. Key challenges and best practices for implementing these tools are discussed, along with insights into their impact on student motivation and participation. The findings underscore the potential of annotation tools to bridge the gap between passive and active learning, offering instructors effective methods to engage students asynchronously and improve educational outcomes in online courses.
