Date of Defense
12-6-2026 9:00 AM
Location
Room: H1-0002
Document Type
Dissertation Defense
Degree Name
Doctor of Philosophy (PhD)
College
College of Education
Department
Learning and Educational Leadership
First Advisor
Ali Ibrahim
Keywords
Artificial Intelligence, AI Use, Critical Thinking, Critical Thinking Performance, Higher Education, United Arab Emirates
Abstract
The rapid integration of Artificial Intelligence (AI) into higher education has reshaped how students engage with learning, raising critical questions about its influence on higher-order cognitive development. This topic is particularly prominent in the United Arab Emirates (UAE), where national priorities emphasize technological dominance and the development of a knowledge-based economy that demands the cultivation of graduates’ critical thinking competencies. Despite widespread AI adoption, empirical evidence linking patterns of AI use to critical thinking performance remains scarce. This dissertation fulfils this gap by examining the relationship between AI use and critical thinking among UAE higher education students, with particular attention to the moderating role of their demographic factors. A quantitative, cross-sectional design was employed, drawing on survey data from 176 higher education students. AI use was conceptualized across frequency, duration, and purpose, while critical thinking was assessed through the RED model of Critical Thinking. Statistical analyses included descriptive statistics, correlation analysis, and multiple regression to examine both direct and moderating effects. The results indicate that AI use is both widespread and sustained; however, its cognitive impact is not uniform. Among the examined dimensions, only duration of AI use demonstrated a statistically significant positive relationship with critical thinking performance, suggesting that prolonged engagement, rather than frequency or type of use alone, is the primary driver of cognitive outcomes. Moderation analysis further revealed that this relationship varies across age and degree level. Younger and undergraduate students exhibit earlier adoption and longer engagement, strengthening the association between AI use and critical thinking. Their reliance on academically assistive uses of AI, such as concept explanation, research support, and revision, reflects more sustained cognitive engagement, whereas older and graduate students demonstrate shorter usage durations and therefore weakening the associations between AI use and critical thinking performance. The current study advances a nuanced, multidimensional understanding of AI use and its cognitive implications, introducing a three-pillar framework of effective AI engagement. The dissertation concludes that AI is most effective when used as an assistive tool, with students remaining the primary drivers of their thinking and analytical processes, while educators and educational systems act as the central agents in developing critical thinking by guiding AI use to ensure it enhances rather than substitutes critical thinking development.
Included in
The Association Between Generative Artificial Intelligence Use and Students' Critical Thinking Performance in UAE Higher Education
Room: H1-0002
The rapid integration of Artificial Intelligence (AI) into higher education has reshaped how students engage with learning, raising critical questions about its influence on higher-order cognitive development. This topic is particularly prominent in the United Arab Emirates (UAE), where national priorities emphasize technological dominance and the development of a knowledge-based economy that demands the cultivation of graduates’ critical thinking competencies. Despite widespread AI adoption, empirical evidence linking patterns of AI use to critical thinking performance remains scarce. This dissertation fulfils this gap by examining the relationship between AI use and critical thinking among UAE higher education students, with particular attention to the moderating role of their demographic factors. A quantitative, cross-sectional design was employed, drawing on survey data from 176 higher education students. AI use was conceptualized across frequency, duration, and purpose, while critical thinking was assessed through the RED model of Critical Thinking. Statistical analyses included descriptive statistics, correlation analysis, and multiple regression to examine both direct and moderating effects. The results indicate that AI use is both widespread and sustained; however, its cognitive impact is not uniform. Among the examined dimensions, only duration of AI use demonstrated a statistically significant positive relationship with critical thinking performance, suggesting that prolonged engagement, rather than frequency or type of use alone, is the primary driver of cognitive outcomes. Moderation analysis further revealed that this relationship varies across age and degree level. Younger and undergraduate students exhibit earlier adoption and longer engagement, strengthening the association between AI use and critical thinking. Their reliance on academically assistive uses of AI, such as concept explanation, research support, and revision, reflects more sustained cognitive engagement, whereas older and graduate students demonstrate shorter usage durations and therefore weakening the associations between AI use and critical thinking performance. The current study advances a nuanced, multidimensional understanding of AI use and its cognitive implications, introducing a three-pillar framework of effective AI engagement. The dissertation concludes that AI is most effective when used as an assistive tool, with students remaining the primary drivers of their thinking and analytical processes, while educators and educational systems act as the central agents in developing critical thinking by guiding AI use to ensure it enhances rather than substitutes critical thinking development.