Exploring the Integration of Generative Artificial Intelligence in Higher Education Through the SAMR Model (79485)
Saturday, 15 June 2024 17:35
Session: Session 6 (Poster Session)
Room: Salle 201
Presentation Type:Poster Presentation
Generative artificial intelligence (GenAI) is a type of artificial intelligence that can produce various types of content in different formats such as text, data, images, coding, and audio based on user prompts. The impact of GenAI has wide-ranging implications that are still emerging. However, it has already proven to be a transformative force, introducing both opportunities and challenges in the field of higher education. The Educause Horizon Report (2023) identified GenAI as a technology that can have a significant impact on teaching and learning as it can open up opportunities for students to focus more on higher-order thinking such as analysis and evaluation of information. However, there are concerns related to overreliance on the technology which could stifle motivation for some students to develop their skills (Warschauer et al., 2023) and other concerns over biases and inaccuracies (Lubowitz, 2023). Similar to the advent of the internet, there is a need to revisit learning theories and frameworks to see how and when GenAI fits in the learning environment. This proposal advocates for a closer look at the Substitution, Augmentation, Modification, and Redefinition (SAMR) model as a framework for instructors to strategically incorporate GenAI into their teaching methodologies. Puentedura's (2006) SAMR model, known for guiding technology integration, offers a nuanced approach, ranging from simple substitution to transformative redefinition. The purpose of this poster session is to explore how each tier of the SAMR model aligns with specific learning activities, providing instructors with actionable strategies for integrating GenAI across different fields.
Authors:
Brooke Marton McGowin, Virginia Tech, United States
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