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Optimizing Generative AI Integration in Higher Education: A Framework for Enhanced Student Engagement and Learning Outcomes
As higher education institutions adapt to changing teaching and learning, Generative Artificial Intelligence (AI) technologies may boost student engagement and learning outcomes. This study aims to help higher education institutions determine the best ways to integrate Generative AI, an urgent issue. A thorough analysis of Generative AI integration and its different effects on education underpins the research framework. This qualitative study analyses the complex relationships between Generative AI, pedagogical approaches, institutional technological preparedness, student characteristics, and educational environment mediators. This study examines Generative AI integration, pedagogical alignment, technology acceptability, and material customization. This study examines how Generative AI might boost student engagement and learning. It evaluates Generative AI's performance through pedagogical methods and relevant feedback. The study underlines the importance of technical adoption and topic matter in AI integration efficacy. This paper closes with a complete framework that gives educators, institutions, and policymakers practical advice for optimising Generative AI integration in higher education. This approach equips stakeholders with the skills and knowledge to navigate AI-enhanced education's complex landscape. It strives to establish a dynamic, student-centred learning environment. To achieve these goals, the framework uses Generative AI technology's revolutionary power.
Optimizing Generative AI Integration in Higher Education: A Framework for Enhanced Student Engagement and Learning Outcomes
As higher education institutions adapt to changing teaching and learning, Generative Artificial Intelligence (AI) technologies may boost student engagement and learning outcomes. This study aims to help higher education institutions determine the best ways to integrate Generative AI, an urgent issue. A thorough analysis of Generative AI integration and its different effects on education underpins the research framework. This qualitative study analyses the complex relationships between Generative AI, pedagogical approaches, institutional technological preparedness, student characteristics, and educational environment mediators. This study examines Generative AI integration, pedagogical alignment, technology acceptability, and material customization. This study examines how Generative AI might boost student engagement and learning. It evaluates Generative AI's performance through pedagogical methods and relevant feedback. The study underlines the importance of technical adoption and topic matter in AI integration efficacy. This paper closes with a complete framework that gives educators, institutions, and policymakers practical advice for optimising Generative AI integration in higher education. This approach equips stakeholders with the skills and knowledge to navigate AI-enhanced education's complex landscape. It strives to establish a dynamic, student-centred learning environment. To achieve these goals, the framework uses Generative AI technology's revolutionary power.
Optimizing Generative AI Integration in Higher Education: A Framework for Enhanced Student Engagement and Learning Outcomes
Riaz, Sadia (author) / Mushtaq, Arif (author)
2024-06-03
1288088 byte
Conference paper
Electronic Resource
English
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