MARC details
| 000 -LEADER |
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| fixed length control field |
12547nam a22002657a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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201210b2025 a|||f bm|| 00| 0 eng d |
| 024 7# - Author Identifier |
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| Standard number or code |
000 |
| Source of number or code |
ORCID |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
EG-CaNU |
| Transcribing agency |
EG-CaNU |
| 041 0# - Language Code |
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| Language code of text |
eng |
| Language code of abstract |
eng |
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ara |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
658.4 |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Laura Labib |
| 245 1# - TITLE STATEMENT |
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| Title |
A Framework for Effective AI Integration in Higher Education: |
| Remainder of title |
A Case Study of Nile University |
| Statement of responsibility, etc. |
/Laura Labib |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Date of publication, distribution, etc. |
2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
127 p. |
| Other physical details |
ill. |
| Dimensions |
21 cm. |
| 500 ## - GENERAL NOTE |
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| Materials specified |
Supervisor: <br/>Prof. Dr. Alaaeldin Idris<br/>Dr. Elhassan El Sabry |
| 502 ## - Dissertation Note |
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| Dissertation type |
Thesis (M.A.)—Nile University, Egypt, 2025 . |
| 504 ## - Bibliography |
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| Bibliography |
"Includes bibliographical references" |
| 505 0# - Contents |
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| Formatted contents note |
Contents:<br/>Table of Contents<br/>Certificate of Approval.....................................................................................................ii<br/>Copyright.........................................................................................................................iii<br/>Dedication & Acknowledgement ....................................................................................iv<br/>Declaration ....................................................................................................................... v<br/>Abstract ...........................................................................................................................vi<br/>List of Abbreviations........................................................................................................ x<br/>List of Figures .................................................................................................................xi<br/>List of Tables..................................................................................................................xii<br/>Chapter 1: Introduction .................................................................................................... 1<br/>1.1 Dynamics and debates regarding the integration of Artificial Intelligence ............ 1<br/>1.2 Navigating the landscape of AI in higher education ......................................... 3<br/>Chapter 2: Literature Review ........................................................................................... 7<br/>1.1 Defining AI: Historical and foundational perspectives.......................................... 7<br/>2.2 Categories of AI: Narrow AI, AGI, and ASI................................................... 10<br/>2.3 Core technologies in today’s AI ........................................................................... 10<br/>2.4 AI governance and ethical considerations............................................................ 13<br/>2.4.1 The EU AI Act ............................................................................................... 14<br/>2.5 AI use cases in higher education .......................................................................... 15<br/>2.5.1 Curriculum design and content development (the WHAT)........................... 18<br/>2.5.2 Pedagogical strategies and learning environment (the HOW)....................... 21<br/>2.5.3 Evaluating mechanisms.................................................................................. 29<br/>2.5.4 Streamlining processes, and enhancing efficiency ........................................ 31<br/>2.5.5 Learning analytics.......................................................................................... 31<br/>2.5.6 Academic research and ideation .................................................................... 33<br/>2.5.7 Student support and services.......................................................................... 34<br/>2.6 The psychology of technology adoption............................................................... 35<br/>viii<br/>2.7 Research gap......................................................................................................... 43<br/>2.8 Problem statement ................................................................................................ 43<br/>2.9 Research questions................................................................................................ 44<br/>2.10 Research aim & objectives ................................................................................. 44<br/>2.11 Research significance ......................................................................................... 45<br/>Chapter 3: Research Methodology................................................................................. 46<br/>3.1 Research design ............................................................................................... 46<br/>3.2 Case study selection......................................................................................... 48<br/>3.3 Participants ...................................................................................................... 49<br/>3.4 Data collection instrument............................................................................... 49<br/>3.4.1 Quantitative data collection instrument.................................................... 49<br/>3.4.2 Qualitative data collection instrument...................................................... 49<br/>3.5 Data analysis plan ............................................................................................ 50<br/>3.5.1 Hypotheses development ............................................................................... 50<br/>3.5.2 Quantitative analysis ................................................................................ 53<br/>3.5.3 Qualitative analysis .................................................................................. 54<br/>3.6 Procedure ......................................................................................................... 54<br/>3.7 Validity and reliability..................................................................................... 54<br/>3.8 Ethical considerations...................................................................................... 55<br/>3.9 Limitations....................................................................................................... 55<br/>Chapter 4: Results and Discussion ................................................................................. 56<br/>4.1. Demographic overview of survey respondents................................................ 56<br/>4.2. Faculty AI usage .............................................................................................. 58<br/>4.3. Perceived student AI usage.............................................................................. 60<br/>4.4. Hypotheses testing faculty and perceived student AI usage............................ 64<br/>4.4.1 Analysis by Role ...................................................................................... 64<br/>4.4.2 Analysis by School Affiliation ................................................................. 65<br/>ix<br/>4.4.3 Analysis by Familiarity with AI............................................................... 67<br/>4.4.4 Analysis by Belief in AI’s potential......................................................... 68<br/>4.4.5 Analysis by Comfort with AI technology ................................................ 70<br/>4.4.6 Analysis by Training in AI tools.............................................................. 72<br/>4.5. Identifying significant influences on faculty AI usage: a multiple regression analysis<br/>74<br/>4.6. Disconnect between perception and practice................................................... 77<br/>4.7. Synthesis of quantitative and qualitative findings........................................... 80<br/>4.7.1 Quantitative findings summary ................................................................ 80<br/>4.7.2 Qualitative themes from interviews ......................................................... 81<br/>4.7.3 Institutional support as a barrier to faculty AI usage: evidence from quantitative and <br/>qualitative data ........................................................................................................ 83<br/>4.8 Addressing the research questions................................................................... 83<br/>Chapter 5: Conclusions and Recommendations............................................................. 86<br/>5.1 Summary of key findings...................................................................................... 86<br/>5.2 Recommendations............................................................................................ 87<br/>5.2.1 Linking findings to research objectives: a proposed framework for AI integration<br/>................................................................................................................................. 93<br/>5.3 Future research...................................................................................................... 95<br/>5.4 Strategic outlook and reflection............................................................................ 96<br/>References.................................................................................................................... 100<br/>Appendix: Exploring the potential of Artificial Intelligence (AI) in Higher Education_ A survey <br/>for Nile University teachers and staff.pdf ................<br/> |
| 520 3# - Abstract |
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| Abstract |
Abstract:<br/>Artificial intelligence (AI) is revolutionizing education by serving as a transformative foundation <br/>for innovation across key domains: curriculum design and content development, pedagogical <br/>strategies and learning environments, evaluation mechanisms and feedback systems, process <br/>streamlining and efficiency enhancement, learning analytics, academic research and ideation, and <br/>student support and services.<br/>This thesis investigates how to adopt and integrate AI effectively in higher education, taken Nile <br/>University as a case study. It seeks the factors influencing faculty adoption. It implements, as a<br/>theoretical framework; the Theory of Planned Behavior (TPB), a widely recognized framework <br/>in technology adoption studies. The thesis addresses three key research questions. Namely, the <br/>faculty attitudes toward AI integration in their teaching and learning practices, how do subjective <br/>norms, such as social influence and social norms, affect faculty AI adoption; and the factors <br/>contribute to perceived behavioral control over AI usage. To address these questions, the research <br/>is guided by three primary objectives: to assess how faculty members use AI tools and perceive <br/>students' use of AI; to examine and analyze the influence of role, school affiliation, familiarity <br/>with AI, belief in its potential, comfort with AI technology, and impact of training on AI adoption; <br/>and to identify barriers and supports for AI integration into teaching and learning practices. <br/>A mixed-methods approach was used, combining faculty surveys for quantitative data analyzed <br/>using ANOVA and multiple regression with SPSS v.26, and semi-structured interviews for <br/>qualitative insights analyzed through thematic techniques.<br/>The findings reveal that faculty generally hold positive attitudes toward AI, particularly its <br/>potential to enhance academic tasks like research, proofreading, and content analysis. However, <br/>AI integration into teaching remains limited due to insufficient training and lack of institutional <br/>guidelines. While faculty view students' AI usage positively, this has little impact on their own <br/>adoption behaviors, with the absence of institutional norms being a more significant barrier. <br/>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 <br/>affiliation showed no significant impact on adoption rates. <br/>Building on these insights, the research concludes with proposing a framework for effective AI <br/>integration in higher education. Institutions should prioritize leadership and governance, such as <br/>appointing an AI officer and establishing an AI committee. Alongside, developing <br/>comprehensive strategies, policies, and targeted training programs, ensuring academic integrity <br/>and ethical use is a fundamental pillar of the proposed framework for responsible AI adoption in <br/>higher education.<br/>Keywords: Artificial Intelligence (AI), Higher Education, Framework for AI Integration, <br/>Theory of Planned Behavior, AI In Teaching and Learning, Educational Technology<br/> |
| 546 ## - Language Note |
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| Language Note |
Text in English, abstracts in English and Arabic |
| 650 #4 - Subject |
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MOT |
| 655 #7 - Index Term-Genre/Form |
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| Source of term |
NULIB |
| focus term |
Dissertation, Academic |
| 690 ## - Subject |
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| School |
MOT |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
| 650 #4 - Subject |
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| -- |
309 |
| 655 #7 - Index Term-Genre/Form |
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187 |
| 690 ## - Subject |
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309 |