AI Integration in Higher Education: A Content Analysis on AI Sophistication and Student Outcomes/Skill Development as Reported in Empirical Studies (2019-2024)
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Authors
Mutiga, Anne N.
Issue Date
2024
Type
Dissertation
Language
en_US
Keywords
AI Applications , AI Complexity , AI Integration , Artificial Intelligence , Educational Technology , Student Outcomes
Alternative Title
Abstract
This study investigated the integration of Artificial Intelligence (AI) in higher education to enhance student outcomes, focusing on AI complexity, student outcomes, and effect sizes. Through a content analysis of 47 peer-reviewed empirical articles from 2019 to Mid 2024, the research identified various AI applications, including chatbots, ChatGPT, Intelligent Tutoring Systems (ITS), virtual reality, augmented reality, language bots, writing bots, and recommender and search engines. The significant distribution of effect sizes across the four categories (small, medium, large, not reported) suggests that the impact of AI on learner outcomes is diverse, indicating that while some AI applications have a substantial impact, others have a more modest effect. The lack of a strong association between AI complexity and student outcomes underscores the necessity for a comprehensive approach to AI integration in education, where the focus is not just on the technology but on how it is used to support teaching and learning practice. The study concluded that contextual factors may significantly impact the relationship between AI complexity and student outcomes. These factors include (a) AI-related elements such as the user interface and adaptability, (b) teacher-related aspects like AI literacy and training, (c) student-related components including prior knowledge and motivation, and (d) technology-related factors such as infrastructure, resources, and support systems. Understanding these factors is essential for assessing the impact of AI complexity on student outcomes and informing effective strategies for AI integration in education. A balanced, quality-driven approach to AI integration is essential for optimizing its benefits in all learning modalities.