Digital Transformation and AI Integration in Lebanese Higher Education: An Explanatory Sequential Mixed-Methods Study

Digital Transformation and AI Integration in Lebanese Higher Education: An Explanatory Sequential Mixed-Methods Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Digital Transformation and AI Integration in Lebanese Higher Education: An Explanatory Sequential Mixed-Methods Study Heba Kamal Chami, Faten Monzer Chami This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8061681/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the interplay between digital transformation initiatives, artificial intelligence (AI) integration, and student satisfaction in Lebanese higher education. An explanatory sequential mixed-methods approach was employed, with data collected from 300 undergraduates at three Lebanes University campuses. Quantitative findings revealed significant dissatisfaction with current digital systems (78%), together with substantial optimism concerning AI’s potential for personalized learning (85%). Regression analyses identified infrastructure quality (β = .42, p < .001), system reliability (β = .38, p < .001), and technical support (β = .31, p = .002) as significant predictors of satisfaction, accounting for 58% of the variance. Qualitative content analysis identified four main barriers: interface usability, functionality gaps, system integration challenges, and inadequate technical support. These results highlight the importance of human-centered, contextually appropriate digital transformation that prioritizes robust infrastructure, integrated systems, and sustainable AI implementation. digital transformation artificial intelligence student satisfaction mixed-methods research higher education Lebanon Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Digital transformation is a central agenda in global higher education, principally altering teaching, learning, and institutional procedures. This criteria extends beyond technology adoption and requires systemic reconfiguration of pedagogy, administration, and engagement (Henderson et al., 2017). In developing contexts such as Lebanon, digital transformation efforts are further influenced by unbalanced infrastructure, financial constraints, and unequal access (El Hage,2020). The Lebanese University, as the nation’s primary public institution, offers a distinctive context for examining the impact of digitalization and the integration of artificial intelligence (AI) on student experiences. Existing research has discovered technology acceptance and e-learning adoption, but few studies combine quantitative modeling with qualitative explanations to reveal how contextual variables influence satisfaction. This study addresses that gap through an explanatory sequential mixed-methods design to answer: To what extent do digital transformation initiatives and AI integration correlate with student satisfaction? Which qualitative issues, as expressed in student narratives, elucidate variations in satisfaction? Literature Review 2.1 Frameworks for Digital Transformation The Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003) and the Technology Acceptance Model (TAM; Davis, 1989) elucidate how apparent utility, accessibility, and approving conditions impact technology adoption. Empowering conditions, primarily institutional support and infrastructure, are significant variables in Lebanon. 2.2 AI in Higher Education Applications of AI have grown beyond administrative automation to embrace predictive analytics and adaptive learning (Zawacki-Richter et al., 2019). Research conducted in developed settings validates improved personalization and engagement (Holmes et al., 2019). Nevertheless, AI's efficacy is hindered in the Middle East due to a lack of digital infrastructure and literacy (Almarzooq et al., 2020). 2.3 Determinants of Student Satisfaction Different aspects including Usability, dependability, assistance, and instructional quality, impact student satisfaction (Sun et al., 2008). In contexts with limited resources, infrastructure dependability is critical (Gautreau, 2011). According to the Community of Inquiry paradigm (Garrison et al., 2000), meaningful learning experiences demand the maintenance of social, cognitive, and teaching presence. 2.4 Interventions for Transformation Strategic, staged approaches that match technology with institutional objectives are essential for effective digital transformation (Henderson et al., 2017). Success through capacity building and iterative feedback is established by regional data from Jordan and Oman (Al-Harthi & Al-Busaidi, 2021). Such prearranged frameworks are still lacking in Lebanon, underscoring the need for student-centered, evidence-based policy recommendations. Effective digital transformation necessitates strategic, phased approaches aligning technology with institutional goals (Henderson et al., 2017). Regional evidence from Oman and Jordan shows success through capacity building and iterative feedback (Al-Harthi & Al-Busaidi, 2021). Lebanon still lacks such structured frameworks, highlighting the need for evidence-based, student-centered policy guidance. Methodology 3.1 Research Design and Approach The design used was an explanatory sequential mixed-methods approach (Creswell & Plano Clark, 2017). While qualitative content research elucidated the contextual elements influencing those interactions, quantitative data established statistical relationships. 3.2 Sampling and Participants A stratified random sample of 300 undergraduate students from Lebanese University's Hadath, Achrafieh, and Aley campuses was chosen (58% female, 42% male; M = 21.2, SD = 2.1). Sampling ensured that all specialties were fairly represented. 3.3 Development and Validation of Instruments The survey comprised four sections: four open-ended qualitative questions, an 8-item AI Potential Inventory (α =.83), a 12-item Digital Tool Satisfaction Scale (α =.87), and demographics. Validity was confirmed by an expert evaluation and a pilot study comprising thirty students. Measurement integrity was validated by confirmatory factor analysis (CFI =.93, RMSEA =.06). 3.4 Ethics and Data Gathering Data has been collected in paper and online (via LMS) to maximize access equity. Confidentiality and informed consent were guaranteed, and participation was voluntary and IRB-approved. Confidentiality and informed consent were guaranteed. 3.5 Data Analysis Quantitative analysis was based on SPSS 28 analysis. Relationships between infrastructure, system reliability, and satisfaction were examined using descriptive statistics, correlations, and hierarchical regression. A six-phase theme analysis was performed on qualitative data (Braun & Clarke, 2006). Cohen's κ =.85 agreement was attained by two independent coders (Campbell et al., 2013). For triangulation, themes were then combined with quantitative results. Findings 4.1 Quantitative Results The quantitative results showed that 78% of students expressed unhappiness with the digital tools available today, compared to just 22% who expressed satisfaction. The multiple regression analysis showed Infrastructure quality (β = 0.42, p < 0.001), system dependability (β = 0.38, p < 0.001), and technical support (β = 0.31, p < 0.01) together explained 58% of the variance in student satisfaction (R2 = 0.58). With regard to learning personalization (85%), evaluation feedback (78%), administrative support (72%), and predictive analytics (68%), students expressed optimism about the potential of artificial intelligence (AI) in education. Overall satisfaction was significantly correlated with perceived AI potential (r = 0.65, p < 0.001). 4.2 Qualitative Findings: Content Analysis Four main themes about students' experiences with digital learning platforms emerged from the content analysis of their replies (Table 1). The most commonly cited concern (147 mentions) was interface usability challenges, signifying that many students have trouble with navigation, accessibility, and general user-friendliness. The requirement for capabilities that enable interactive and collaborative learning was brought to light by Functionality Gaps (112 mentions). The fragmentation of tools, where several programs are required for a single course without smooth interoperability, was signified in System Integration Deficiencies (89 mentions). Lastly, Technical Support Limitations (67 mentions) emphasized the importance of prompt and efficient support. Table 1. Major Themes Identified in Content Analysis Theme Frequency Representative Quote Interface Usability Challenges 147 “The platform feels like it was designed without considering how students actually learn online.” Functionality Gaps 112 “We lack basic features that would make collaborative learning possible.” System Integration Deficiencies 89 “I have to use five different apps for one course—none of them talk to each other.” Technical Support Limitations 67 “When issues arise, support is slow and rarely solves the real problem.” Discussion 5.1 Interpretation The results demonstrate that espousing technology by itself does not guarantee better learning opportunities (Selwyn, 2019). Deficits in functionality, integration, and interface usability directly affect user pleasure. On the other hand, students' desire for intelligent but human-centered systems is established by their high optimism for AI integration. 5.2 Contributions to Theory This study expands on TAM and UTAUT by showing how contextual implementation factors have significant effects on their vital constructs. Precisely, the relationship between technology provision and adoption results is directly enabled by usability (ease of use), integration (easing conditions), and assistance. Additionally, it upholds the CoI framework's tenet that persistent social and instructional presence is necessary for effective digital learning, which is harshly compromised by the identified functional and technical obstacles. 5.3 Practical Recommendation : Towards a Strategic Framework for Digital Transformation Institutional tactics must progress from ad hoc technology adoption to a comprehensive, learner-centric framework in order to fully attain the potential of digital transformation in higher education. Five fundamental pillars are indispensable for a digital transformation strategy to be effective. First, prioritize user-centered design by using a mobile-first strategy and iterative student testing. Second, create unified ecosystems with smooth data synchronization and single sign-on to guarantee technological integration. Third, switch to proactive technical support that addresses root-cause problems using data analytics . Lastly, assign resources to scalable hardware and essential network stability by advancing initial infrastructure. 5.4 Restrictions and Upcoming Projects The use of self-reported data and the single-institution sample limits the generalizability of our results. Longitudinal and multi-institutional investigations in the future are advised. Faculty viewpoints should be included in future studies, and the effectiveness of certain AI tools in curriculum delivery should be experimentally evaluated. 5.4 Limitations The generalizability of these findings is inadequate due to the single-institution sample and the use of self-reported data. Future multi-institutional and longitudinal studies are recommended. Further research should embrace faculty perspectives and empirically assess the efficacy of specific AI tools in curriculum delivery. Conclusion A strategic balance between technology innovation and human-centered design is essential for effective digital transformation in developing higher education contexts. Adequate infrastructure, usability, and technical support are central requirements, as this study demonstrates. Moreover, rather than seeing AI as a substitute for human education, students see it as a collaborative partner for personalized learning. Building cohesive systems, guaranteeing dependable networks, and—above all—maintaining continuous engagement with the learner voice are ultimately essential for lasting transformation. Declarations Author Contribution F.C. collected the data. H.C. conducted the data analysis and performed the content analysis. Both authors contributed to writing, revising, and approving the final manuscript. References Almarzooq, Z. I., Lopes, M., & Kochar, A. (2020). Virtual learning during COVID-19: A disruptive technology in graduate education. Journal of the American College of Cardiology, 75 (20), 2635–2638. https://doi.org/10.1016/j.jacc.2020.04.015 Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3 (2), 77–101. https://doi.org/10.1191/1478088706qp063oa Campbell, J. L., Quincy, C., Osserman, J., & Pedersen, O. K. (2013). Coding in-depth interviews: Problems of unitization and intercoder reliability. Sociological Methods & Research, 42 (3), 294–320. https://doi.org/10.1177/0049124113500475 Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). Sage Publications. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319–340. https://doi.org/10.2307/249008 El Hage, S. (2020). Digital transformation in Lebanese higher education: Opportunities and challenges. Journal of Educational Technology in the Arab World, 3 (2), 45–62. http://www.jetaw.org/index.php/jetaw/article/view/45 Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2 (2–3), 87–105. https://doi.org/10.1016/S1096-7516(00)00016-6 Gautreau, C. (2011). Motivational factors affecting LMS integration by faculty. Journal of Educators Online, 8 (1), 1–25. https://www.thejeo.com/archive/2011_8_1/gautreau Henderson, M., Selwyn, N., & Aston, R. (2017). What works and why? Studies in Higher Education, 42 (8), 1567–1579. https://doi.org/10.1080/03075079.2015.1007946 Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://curriculumredesign.org/wp-content/uploads/AI-in-Education-Promises-and-Implications-CCR.pdf l-Harthi, H., & Al-Busaidi, S. (2021). Digital transformation strategies in higher education: A case from Oman. Education and Information Technologies, 26 (3), 2331–2347. https://doi.org/10.1007/s10639-020-10355-5 Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press. Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives successful e-learning? Computers & Education, 50 (4), 1183–1202. https://doi.org/10.1016/j.compedu.2006.11.007 Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46 (2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926 Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3), 425–478. https://doi.org/10.2307/30036540 Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16 (1), 1–27. https://doi.org/10.1186/s41239-019-0171-0 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8061681","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":546194999,"identity":"725213cd-1595-4e71-bcff-d5d0af3e4487","order_by":0,"name":"Heba Kamal Chami","email":"","orcid":"","institution":"Sultan Qaboos University","correspondingAuthor":false,"prefix":"","firstName":"Heba","middleName":"Kamal","lastName":"Chami","suffix":""},{"id":546195000,"identity":"6d9b1d67-30a3-4d70-a20c-84f279d3bc0b","order_by":1,"name":"Faten Monzer Chami","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIie3PMWrDMBiG4V/8YC9yvMpLcoUULS24+Co2WRMo5AIGg7o4mRs65BAFzzaCZskBAtrT1cGLCR5qN4VQClLGDHoXfQg9gwBstjtsdDkeARDLsu6n4xqIczkYgCuS6m24wZsJ3XNJh20kbnZsXs5sPD3EUxmei8kIgdSnuYbQT/6+WTPek1gu1upBIGCwKTSExRy9nCXFIS7lIlekJw56OjL5an5JksqnXEVmwihH2vZkL0FCqxIzofMleinj0U5AtUrVTCDJtH/x3d0H0i4cB5nf1G2nnrevWVWfNOQnIv4MkhreD3X/hs1ms9mufQNxM0/Y5Q1ZGQAAAABJRU5ErkJggg==","orcid":"","institution":"Lebanese University","correspondingAuthor":true,"prefix":"","firstName":"Faten","middleName":"Monzer","lastName":"Chami","suffix":""}],"badges":[],"createdAt":"2025-11-08 06:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8061681/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8061681/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96888424,"identity":"e1c2bff2-d95a-45b5-93f3-aaa3e55543c7","added_by":"auto","created_at":"2025-11-27 08:52:22","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":52924,"visible":true,"origin":"","legend":"","description":"","filename":"HEBACHAMIARTICLE.docx2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8061681/v1/85b07faa841422927ad11457.docx"},{"id":96888422,"identity":"ad6c0712-ff20-4454-b829-b5c1ab57adfc","added_by":"auto","created_at":"2025-11-27 08:52:22","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3776,"visible":true,"origin":"","legend":"","description":"","filename":"1beb848deb314fc6887fe45c89c14cd5.json","url":"https://assets-eu.researchsquare.com/files/rs-8061681/v1/12d035f95baa801143c73386.json"},{"id":96919261,"identity":"cdcfd29d-0c3d-4b44-8952-9d3567fe547b","added_by":"auto","created_at":"2025-11-27 14:13:30","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41591,"visible":true,"origin":"","legend":"","description":"","filename":"1beb848deb314fc6887fe45c89c14cd51enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8061681/v1/27cccf5782c4638ec0df3396.xml"},{"id":96920723,"identity":"35db571a-0b31-451a-88fd-6123230e18fe","added_by":"auto","created_at":"2025-11-27 14:15:23","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40238,"visible":true,"origin":"","legend":"","description":"","filename":"1beb848deb314fc6887fe45c89c14cd51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8061681/v1/938a71cc8892b164df624b24.xml"},{"id":96888425,"identity":"578bae05-78a6-4139-b081-2ba4546ebd69","added_by":"auto","created_at":"2025-11-27 08:52:22","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":47100,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8061681/v1/f117a45de06f6f602a0d25ed.html"},{"id":96888418,"identity":"3e3e5cd4-9d19-47d4-bcfe-fb9094396126","added_by":"auto","created_at":"2025-11-27 08:52:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104352,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework of digital transformation, AI integration, and student satisfaction.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8061681/v1/749d6a9e98363b5626f3e5b3.png"},{"id":96888419,"identity":"3475a4ed-672c-4649-b4a8-078f8b826f1d","added_by":"auto","created_at":"2025-11-27 08:52:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50379,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1.\u003c/strong\u003e Student Satisfaction with Current Digital Tools\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8061681/v1/ada17a2dcd4e7a128dd3fe24.png"},{"id":96920125,"identity":"dafd9df6-825d-48ae-8520-90e8fd9c50f1","added_by":"auto","created_at":"2025-11-27 14:14:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":39035,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2. \u003c/strong\u003ePerceived Potential of AI Applications\u003c/p\u003e","description":"","filename":"22.png","url":"https://assets-eu.researchsquare.com/files/rs-8061681/v1/fae53945320eef1a1462c3be.png"},{"id":96888421,"identity":"bc52de73-797c-45de-b918-26273c4cbbf4","added_by":"auto","created_at":"2025-11-27 08:52:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":44983,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3: \u003c/strong\u003eFrequency Distribution of Content Analysis Themes\u003c/p\u003e","description":"","filename":"33.png","url":"https://assets-eu.researchsquare.com/files/rs-8061681/v1/cc8feae3ce59ed2488b9f205.png"},{"id":101646752,"identity":"2afbb0ff-f6f9-4e0d-a01a-078869468d1f","added_by":"auto","created_at":"2026-02-02 08:43:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":714727,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8061681/v1/da101ea1-8aa0-408f-b2a7-463f19bc1864.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digital Transformation and AI Integration in Lebanese Higher Education: An Explanatory Sequential Mixed-Methods Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDigital transformation is a central agenda in global higher education, principally altering teaching, learning, and institutional procedures. This criteria extends beyond technology adoption and requires systemic reconfiguration of pedagogy, administration, and engagement (Henderson et al., 2017). In developing contexts such as Lebanon, digital transformation efforts are further influenced by unbalanced infrastructure, financial constraints, and unequal access (El Hage,2020). The Lebanese University, as the nation\u0026rsquo;s primary public institution, offers a distinctive context for examining the impact of digitalization and the integration of artificial intelligence (AI) on student experiences.\u003c/p\u003e\n\u003cp\u003eExisting research has discovered technology acceptance and e-learning adoption, but few studies combine quantitative modeling with qualitative explanations to reveal how contextual variables influence satisfaction. This study addresses that gap through an explanatory sequential mixed-methods design to answer:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eTo what extent do digital transformation initiatives and AI integration correlate with student satisfaction?\u003c/li\u003e\n \u003cli\u003eWhich qualitative issues, as expressed in student narratives, elucidate variations in satisfaction?\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Literature Review","content":"\u003cp\u003e\u003cstrong\u003e2.1 Frameworks for Digital Transformation\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003) and the Technology Acceptance Model (TAM; Davis, 1989) elucidate how apparent utility, accessibility, and approving conditions impact technology adoption. Empowering conditions, primarily institutional support and infrastructure, are significant variables in Lebanon.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 AI in Higher Education\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Applications of AI have grown beyond administrative automation to embrace predictive analytics and adaptive learning (Zawacki-Richter et al., 2019). Research conducted in developed settings validates improved personalization and engagement (Holmes et al., 2019). Nevertheless, AI\u0026apos;s efficacy is hindered in the Middle East due to a lack of digital infrastructure and literacy (Almarzooq et al., 2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Determinants of Student Satisfaction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferent aspects including \u0026nbsp;Usability, dependability, assistance, and instructional quality, impact student satisfaction (Sun et al., 2008). In contexts with limited resources, infrastructure dependability is critical (Gautreau, 2011). According to the Community of Inquiry paradigm (Garrison et al., 2000), meaningful learning experiences demand the maintenance of social, cognitive, and teaching presence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Interventions for Transformation\u003c/strong\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;Strategic, staged approaches that match technology with institutional objectives are essential for effective digital transformation (Henderson et al., 2017). Success through capacity building and iterative feedback is established by regional data from Jordan and Oman (Al-Harthi \u0026amp; Al-Busaidi, 2021). Such prearranged frameworks are still lacking in Lebanon, underscoring the need for student-centered, evidence-based policy recommendations.\u003c/p\u003e\n\u003cp\u003eEffective digital transformation necessitates strategic, phased approaches aligning technology with institutional goals (Henderson et al., 2017). Regional evidence from Oman and Jordan shows success through capacity building and iterative feedback (Al-Harthi \u0026amp; Al-Busaidi, 2021). Lebanon still lacks such structured frameworks, highlighting the need for evidence-based, student-centered policy guidance.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003e3.1 Research Design and Approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe design used was an explanatory sequential mixed-methods approach (Creswell \u0026amp; Plano Clark, 2017). While qualitative content research elucidated the contextual elements influencing those interactions, quantitative data established statistical relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Sampling and Participants\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;A stratified random sample of 300 undergraduate students from Lebanese University\u0026apos;s Hadath, Achrafieh, and Aley campuses was chosen (58% female, 42% male; M = 21.2, SD = 2.1). Sampling ensured that all specialties were fairly represented.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Development and Validation of Instruments\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The survey comprised four sections: four open-ended qualitative questions, an 8-item AI Potential Inventory (\u0026alpha; =.83), a 12-item Digital Tool Satisfaction Scale (\u0026alpha; =.87), and demographics. Validity was confirmed by an expert evaluation and a pilot study comprising thirty students. Measurement integrity was validated by confirmatory factor analysis (CFI =.93, RMSEA =.06).\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003cstrong\u003e3.4 Ethics and Data Gathering\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData has been collected in paper and online (via LMS) to maximize access equity. Confidentiality and informed consent were guaranteed, and participation was voluntary and IRB-approved. Confidentiality and informed consent were guaranteed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantitative analysis was based on SPSS 28 analysis. Relationships between infrastructure, system reliability, and satisfaction were examined using descriptive statistics, correlations, and hierarchical regression. A six-phase theme analysis was performed on qualitative data (Braun \u0026amp; Clarke, 2006). Cohen\u0026apos;s \u0026kappa; =.85 agreement was attained by two independent coders (Campbell et al., 2013). For triangulation, themes were then combined with quantitative results.\u003c/p\u003e"},{"header":"Findings","content":"\u003cp\u003e\u003cstrong\u003e4.1 Quantitative Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quantitative results showed that 78% of students expressed unhappiness with the digital tools available today, compared to just 22% who expressed satisfaction.\u0026nbsp;\u003cbr\u003e\u0026nbsp;The multiple regression analysis showed Infrastructure quality (\u0026beta; = 0.42, p \u0026lt; 0.001), system dependability (\u0026beta; = 0.38, p \u0026lt; 0.001), and technical support (\u0026beta; = 0.31, p \u0026lt; 0.01) together explained 58% of the variance in student satisfaction (R2 = 0.58).\u0026nbsp;\u003cbr\u003e\u0026nbsp;With regard to learning personalization (85%), evaluation feedback (78%), administrative support (72%), and predictive analytics (68%), students expressed optimism about the potential of artificial intelligence (AI) in education. Overall satisfaction was significantly correlated with perceived AI potential (r = 0.65, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Qualitative Findings: Content Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour main themes about students\u0026apos; experiences with digital learning platforms emerged from the content analysis of their replies (Table 1). The most commonly cited concern (147 mentions) was interface usability challenges, signifying that many students have trouble with navigation, accessibility, and general user-friendliness. The requirement for capabilities that enable interactive and collaborative learning was brought to light by Functionality Gaps (112 mentions). The fragmentation of tools, where several programs are required for a single course without smooth interoperability, was signified in System Integration Deficiencies (89 mentions). Lastly, Technical Support Limitations (67 mentions) emphasized the importance of prompt and efficient support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Major Themes Identified in Content Analysis\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTheme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRepresentative Quote\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInterface Usability Challenges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;The platform feels like it was designed without considering how students actually learn online.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFunctionality Gaps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;We lack basic features that would make collaborative learning possible.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSystem Integration Deficiencies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;I have to use five different apps for one course\u0026mdash;none of them talk to each other.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTechnical Support Limitations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ldquo;When issues arise, support is slow and rarely solves the real problem.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003e5.1 Interpretation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results demonstrate that espousing technology by itself does not guarantee better learning opportunities (Selwyn, 2019). Deficits in functionality, integration, and interface usability directly affect user pleasure. On the other hand, students\u0026apos; desire for intelligent but human-centered systems is established by their high optimism for AI integration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Contributions to Theory\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study expands on TAM and UTAUT by showing how contextual implementation factors have significant effects on their vital constructs. Precisely, the relationship between technology provision and adoption results is directly enabled by usability (ease of use), integration (easing conditions), and assistance. Additionally, it upholds the CoI framework\u0026apos;s tenet that persistent social and instructional presence is necessary for effective digital learning, which is harshly compromised by the identified functional and technical obstacles.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003cstrong\u003e5.3 Practical Recommendation\u003c/strong\u003e: \u003cstrong\u003eTowards a Strategic Framework for Digital Transformation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInstitutional tactics must progress from ad hoc technology adoption to a comprehensive, learner-centric framework in order to fully attain the potential of digital transformation in higher education. Five fundamental pillars are indispensable for a digital transformation strategy to be effective. First, prioritize user-centered design by using a mobile-first strategy and iterative student testing. Second, create unified ecosystems with smooth data synchronization and single sign-on to guarantee technological integration. Third, switch to proactive technical support that addresses root-cause problems using data analytics . Lastly, assign resources to scalable hardware and essential network stability by advancing \u0026nbsp;initial infrastructure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Restrictions and Upcoming Projects\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe use of self-reported data and the single-institution sample limits the generalizability of our results. Longitudinal and multi-institutional investigations in the future are advised. Faculty viewpoints should be included in future studies, and the effectiveness of certain AI tools in curriculum delivery should be experimentally evaluated.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e5.4 Limitations\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The generalizability of these findings is inadequate due to the single-institution sample and the use of self-reported data. Future multi-institutional and longitudinal studies are recommended. Further research should embrace faculty perspectives and empirically assess the efficacy of specific AI tools in curriculum delivery.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA strategic balance between technology innovation and human-centered design is essential for effective digital transformation in developing higher education contexts. Adequate infrastructure, usability, and technical support are central requirements, as this study demonstrates. Moreover, rather than seeing AI as a substitute for human education, students see it as a collaborative partner for personalized learning. Building cohesive systems, guaranteeing dependable networks, and\u0026mdash;above all\u0026mdash;maintaining continuous engagement with the learner voice are ultimately essential for lasting transformation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eF.C. collected the data. H.C. conducted the data analysis and performed the content analysis. Both authors contributed to writing, revising, and approving the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmarzooq, Z. I., Lopes, M., \u0026amp; Kochar, A. (2020). Virtual learning during COVID-19: A disruptive technology in graduate education. \u003cem\u003eJournal of the American College of Cardiology, 75\u003c/em\u003e(20), 2635\u0026ndash;2638. https://doi.org/10.1016/j.jacc.2020.04.015\u003c/li\u003e\n\u003cli\u003eBraun, V., \u0026amp; Clarke, V. (2006). Using thematic analysis in psychology. \u003cem\u003eQualitative Research in Psychology, 3\u003c/em\u003e(2), 77\u0026ndash;101. https://doi.org/10.1191/1478088706qp063oa\u003c/li\u003e\n\u003cli\u003eCampbell, J. L., Quincy, C., Osserman, J., \u0026amp; Pedersen, O. K. (2013). Coding in-depth interviews: Problems of unitization and intercoder reliability. \u003cem\u003eSociological Methods \u0026amp; Research, 42\u003c/em\u003e(3), 294\u0026ndash;320. https://doi.org/10.1177/0049124113500475\u003c/li\u003e\n\u003cli\u003eCreswell, J. W., \u0026amp; Plano Clark, V. L. (2017). \u003cem\u003eDesigning and conducting mixed methods research\u003c/em\u003e (3rd ed.). Sage Publications.\u003c/li\u003e\n\u003cli\u003eDavis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. \u003cem\u003eMIS Quarterly, 13\u003c/em\u003e(3), 319\u0026ndash;340. https://doi.org/10.2307/249008\u003c/li\u003e\n\u003cli\u003eEl Hage, S. (2020). Digital transformation in Lebanese higher education: Opportunities and challenges. \u003cem\u003eJournal of Educational Technology in the Arab World, 3\u003c/em\u003e(2), 45\u0026ndash;62. http://www.jetaw.org/index.php/jetaw/article/view/45\u003c/li\u003e\n\u003cli\u003eGarrison, D. R., Anderson, T., \u0026amp; Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. \u003cem\u003eThe Internet and Higher Education, 2\u003c/em\u003e(2\u0026ndash;3), 87\u0026ndash;105. https://doi.org/10.1016/S1096-7516(00)00016-6\u003c/li\u003e\n\u003cli\u003eGautreau, C. (2011). Motivational factors affecting LMS integration by faculty. \u003cem\u003eJournal of Educators Online, 8\u003c/em\u003e(1), 1\u0026ndash;25. https://www.thejeo.com/archive/2011_8_1/gautreau\u003c/li\u003e\n\u003cli\u003eHenderson, M., Selwyn, N., \u0026amp; Aston, R. (2017). What works and why? \u003cem\u003eStudies in Higher Education, 42\u003c/em\u003e(8), 1567\u0026ndash;1579. https://doi.org/10.1080/03075079.2015.1007946\u003c/li\u003e\n\u003cli\u003eHolmes, W., Bialik, M., \u0026amp; Fadel, C. (2019). \u003cem\u003eArtificial intelligence in education: Promises and implications for teaching and learning.\u003c/em\u003e Center for Curriculum Redesign. https://curriculumredesign.org/wp-content/uploads/AI-in-Education-Promises-and-Implications-CCR.pdf\u003c/li\u003e\n\u003cli\u003el-Harthi, H., \u0026amp; Al-Busaidi, S. (2021). Digital transformation strategies in higher education: A case from Oman. \u003cem\u003eEducation and Information Technologies, 26\u003c/em\u003e(3), 2331\u0026ndash;2347. https://doi.org/10.1007/s10639-020-10355-5\u003c/li\u003e\n\u003cli\u003eSelwyn, N. (2019). \u003cem\u003eShould robots replace teachers? AI and the future of education.\u003c/em\u003e Polity Press.\u003c/li\u003e\n\u003cli\u003eSun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., \u0026amp; Yeh, D. (2008). What drives successful e-learning? \u003cem\u003eComputers \u0026amp; Education, 50\u003c/em\u003e(4), 1183\u0026ndash;1202. https://doi.org/10.1016/j.compedu.2006.11.007\u003c/li\u003e\n\u003cli\u003eVenkatesh, V., \u0026amp; Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. \u003cem\u003eManagement Science, 46\u003c/em\u003e(2), 186\u0026ndash;204. https://doi.org/10.1287/mnsc.46.2.186.11926\u003c/li\u003e\n\u003cli\u003eVenkatesh, V., Morris, M. G., Davis, G. B., \u0026amp; Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. \u003cem\u003eMIS Quarterly, 27\u003c/em\u003e(3), 425\u0026ndash;478. https://doi.org/10.2307/30036540\u003c/li\u003e\n\u003cli\u003eZawacki-Richter, O., Mar\u0026iacute;n, V. I., Bond, M., \u0026amp; Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. \u003cem\u003eInternational Journal of Educational Technology in Higher Education, 16\u003c/em\u003e(1), 1\u0026ndash;27. https://doi.org/10.1186/s41239-019-0171-0\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"digital transformation, artificial intelligence, student satisfaction, mixed-methods research, higher education, Lebanon","lastPublishedDoi":"10.21203/rs.3.rs-8061681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8061681/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the interplay between digital transformation initiatives, artificial intelligence (AI) integration, and student satisfaction in Lebanese higher education. An explanatory sequential mixed-methods approach was employed, with data collected from 300 undergraduates at three Lebanes University campuses. Quantitative findings revealed significant dissatisfaction with current digital systems (78%), together with substantial optimism concerning AI\u0026rsquo;s potential for personalized learning (85%). Regression analyses identified infrastructure quality (β\u0026thinsp;=\u0026thinsp;.42, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), system reliability (β\u0026thinsp;=\u0026thinsp;.38, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and technical support (β\u0026thinsp;=\u0026thinsp;.31, p\u0026thinsp;=\u0026thinsp;.002) as significant predictors of satisfaction, accounting for 58% of the variance. Qualitative content analysis identified four main barriers: interface usability, functionality gaps, system integration challenges, and inadequate technical support. These results highlight the importance of human-centered, contextually appropriate digital transformation that prioritizes robust infrastructure, integrated systems, and sustainable AI implementation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Digital Transformation and AI Integration in Lebanese Higher Education: An Explanatory Sequential Mixed-Methods Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 08:52:17","doi":"10.21203/rs.3.rs-8061681/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"973abe1c-290d-4c93-ab51-e28da5d3a0f3","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T08:42:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-27 08:52:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8061681","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8061681","identity":"rs-8061681","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}