A Framework for Effective AI Integration in Higher Education: A Case Study of Nile University /Laura Labib

Supervisor:
Prof. Dr. Alaaeldin Idris
Dr. Elhassan El Sabry

Thesis (M.A.)—Nile University, Egypt, 2025 .

"Includes bibliographical references"

Contents:
Table of Contents
Certificate of Approval.....................................................................................................ii
Copyright.........................................................................................................................iii
Dedication & Acknowledgement ....................................................................................iv
Declaration ....................................................................................................................... v
Abstract ...........................................................................................................................vi
List of Abbreviations........................................................................................................ x
List of Figures .................................................................................................................xi
List of Tables..................................................................................................................xii
Chapter 1: Introduction .................................................................................................... 1
1.1 Dynamics and debates regarding the integration of Artificial Intelligence ............ 1
1.2 Navigating the landscape of AI in higher education ......................................... 3
Chapter 2: Literature Review ........................................................................................... 7
1.1 Defining AI: Historical and foundational perspectives.......................................... 7
2.2 Categories of AI: Narrow AI, AGI, and ASI................................................... 10
2.3 Core technologies in today’s AI ........................................................................... 10
2.4 AI governance and ethical considerations............................................................ 13
2.4.1 The EU AI Act ............................................................................................... 14
2.5 AI use cases in higher education .......................................................................... 15
2.5.1 Curriculum design and content development (the WHAT)........................... 18
2.5.2 Pedagogical strategies and learning environment (the HOW)....................... 21
2.5.3 Evaluating mechanisms.................................................................................. 29
2.5.4 Streamlining processes, and enhancing efficiency ........................................ 31
2.5.5 Learning analytics.......................................................................................... 31
2.5.6 Academic research and ideation .................................................................... 33
2.5.7 Student support and services.......................................................................... 34
2.6 The psychology of technology adoption............................................................... 35
viii
2.7 Research gap......................................................................................................... 43
2.8 Problem statement ................................................................................................ 43
2.9 Research questions................................................................................................ 44
2.10 Research aim & objectives ................................................................................. 44
2.11 Research significance ......................................................................................... 45
Chapter 3: Research Methodology................................................................................. 46
3.1 Research design ............................................................................................... 46
3.2 Case study selection......................................................................................... 48
3.3 Participants ...................................................................................................... 49
3.4 Data collection instrument............................................................................... 49
3.4.1 Quantitative data collection instrument.................................................... 49
3.4.2 Qualitative data collection instrument...................................................... 49
3.5 Data analysis plan ............................................................................................ 50
3.5.1 Hypotheses development ............................................................................... 50
3.5.2 Quantitative analysis ................................................................................ 53
3.5.3 Qualitative analysis .................................................................................. 54
3.6 Procedure ......................................................................................................... 54
3.7 Validity and reliability..................................................................................... 54
3.8 Ethical considerations...................................................................................... 55
3.9 Limitations....................................................................................................... 55
Chapter 4: Results and Discussion ................................................................................. 56
4.1. Demographic overview of survey respondents................................................ 56
4.2. Faculty AI usage .............................................................................................. 58
4.3. Perceived student AI usage.............................................................................. 60
4.4. Hypotheses testing faculty and perceived student AI usage............................ 64
4.4.1 Analysis by Role ...................................................................................... 64
4.4.2 Analysis by School Affiliation ................................................................. 65
ix
4.4.3 Analysis by Familiarity with AI............................................................... 67
4.4.4 Analysis by Belief in AI’s potential......................................................... 68
4.4.5 Analysis by Comfort with AI technology ................................................ 70
4.4.6 Analysis by Training in AI tools.............................................................. 72
4.5. Identifying significant influences on faculty AI usage: a multiple regression analysis
74
4.6. Disconnect between perception and practice................................................... 77
4.7. Synthesis of quantitative and qualitative findings........................................... 80
4.7.1 Quantitative findings summary ................................................................ 80
4.7.2 Qualitative themes from interviews ......................................................... 81
4.7.3 Institutional support as a barrier to faculty AI usage: evidence from quantitative and
qualitative data ........................................................................................................ 83
4.8 Addressing the research questions................................................................... 83
Chapter 5: Conclusions and Recommendations............................................................. 86
5.1 Summary of key findings...................................................................................... 86
5.2 Recommendations............................................................................................ 87
5.2.1 Linking findings to research objectives: a proposed framework for AI integration
................................................................................................................................. 93
5.3 Future research...................................................................................................... 95
5.4 Strategic outlook and reflection............................................................................ 96
References.................................................................................................................... 100
Appendix: Exploring the potential of Artificial Intelligence (AI) in Higher Education_ A survey
for Nile University teachers and staff.pdf ................

Abstract:
Artificial intelligence (AI) is revolutionizing education by serving as a transformative foundation
for innovation across key domains: curriculum design and content development, pedagogical
strategies and learning environments, evaluation mechanisms and feedback systems, process
streamlining and efficiency enhancement, learning analytics, academic research and ideation, and
student support and services.
This thesis investigates how to adopt and integrate AI effectively in higher education, taken Nile
University as a case study. It seeks the factors influencing faculty adoption. It implements, as a
theoretical framework; the Theory of Planned Behavior (TPB), a widely recognized framework
in technology adoption studies. The thesis addresses three key research questions. Namely, the
faculty attitudes toward AI integration in their teaching and learning practices, how do subjective
norms, such as social influence and social norms, affect faculty AI adoption; and the factors
contribute to perceived behavioral control over AI usage. To address these questions, the research
is guided by three primary objectives: to assess how faculty members use AI tools and perceive
students' use of AI; to examine and analyze the influence of role, school affiliation, familiarity
with AI, belief in its potential, comfort with AI technology, and impact of training on AI adoption;
and to identify barriers and supports for AI integration into teaching and learning practices.
A mixed-methods approach was used, combining faculty surveys for quantitative data analyzed
using ANOVA and multiple regression with SPSS v.26, and semi-structured interviews for
qualitative insights analyzed through thematic techniques.
The findings reveal that faculty generally hold positive attitudes toward AI, particularly its
potential to enhance academic tasks like research, proofreading, and content analysis. However,
AI integration into teaching remains limited due to insufficient training and lack of institutional
guidelines. While faculty view students' AI usage positively, this has little impact on their own
adoption behaviors, with the absence of institutional norms being a more significant barrier.
Training emerged as the key factor in driving perceived behavioral control. Moreover, welltrained faculty reported greater comfort and willingness to adopt AI. Faculty role and school
affiliation showed no significant impact on adoption rates.
Building on these insights, the research concludes with proposing a framework for effective AI
integration in higher education. Institutions should prioritize leadership and governance, such as
appointing an AI officer and establishing an AI committee. Alongside, developing
comprehensive strategies, policies, and targeted training programs, ensuring academic integrity
and ethical use is a fundamental pillar of the proposed framework for responsible AI adoption in
higher education.
Keywords: Artificial Intelligence (AI), Higher Education, Framework for AI Integration,
Theory of Planned Behavior, AI In Teaching and Learning, Educational Technology

Text in English, abstracts in English and Arabic