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

Introduction

Digital transformation is a central agenda in global higher education, principally altering teaching, learning, and institutional procedures. This procedure extends beyond technology adoption and requires systemic reconfiguration of pedagogy, administration, and engagement [1]. In developing contexts such as Lebanon, digital transformation efforts are further intricated by unbalanced infrastructure, financial constraints, and unequal access [2].

The Lebanese University, as the nation’s primary public institution, provides a unique context for examining the effects of digitalization and the integration of artificial intelligence (AI) on student experiences.

While existing research has explored technology acceptance and e-learning adoption, there are few studies that combine quantitative modeling with qualitative insights to reveal how various contextual factors influence student satisfaction. This study aims to fill that gap by utilizing an explanatory sequential mixed-methods design to address the following questions:

• To what extent do digital transformation initiatives and the integration of AI correlate with student satisfaction?

• What qualitative issues, as expressed in student narratives, clarify the variations in satisfaction levels?

Figure 1: Conceptual Framework of Digital Transformation, AI Integration, And Student Satisfaction

Literature Review

Frameworks for Digital Transformation

The Unified Theory of Acceptance and Use of Technology and the Technology Acceptance Model elucidate how apparent utility, accessibility, and approving conditions impact technology adoption [3,4]. Empowering conditions, primarily institutional support and infrastructure, are significant variables in Lebanon.

AI in Higher Education

Applications of AI have grown beyond administrative automation to embrace predictive analytics and adaptive learning [5]. Research conducted in developed settings validates improved personalization and engagement [6]. Nevertheless, AI’s efficacy is hindered in the Middle East due to a lack of digital infrastructure and literacy [7].

Determinants of Student Satisfaction

Many aspects, such as Usability, dependability, assistance, and instructional quality, impact student satisfaction [7]. In contexts with limited resources, infrastructure dependability is critical [8]. According to the Community of Inquiry paradigm meaningful learning experiences demand the maintenance of social, cognitive, and teaching presence [9].

Interventions for Transformation

Strategic, phased approaches that align technology with institutional goals are crucial for effective digital transformation [1]. Success through capacity building and iterative feedback is demonstrated by regional data from Jordan and Oman [10]. Such preplanned frameworks are still missing in Lebanon, emphasizing the need for student-centered, evidence-based policy recommendations. chosen (58% female, 42% male; M = 21.2, SD = 2.1). Sampling ensured that all specialties were fairly represented.

Methodology

Research Design and Approach

The design used was an explanatory sequential mixed-methods approach [11]. While qualitative content research elucidated the contextual elements influencing those interactions, quantitative data established statistical relationships.

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.

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).

Ethics and Data Gathering

Data was collected both on 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.

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 [12]. Cohen’s κ =.85 agreement was attained by two independent coders [13]. For triangulation, themes were then combined with quantitative results.

Findings

Quantitative Results

The quantitative results indicated that 78% of students reported dissatisfaction with the current digital tools, while only 22% expressed satisfaction. The multiple regression analysis revealed that 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 accounted for 58% of the variance in student satisfaction (R² = 0.58). Regarding the potential of artificial intelligence (AI) in education, students expressed optimism concerning various aspects: learning personalization (85%), evaluation feedback (78%), administrative support (72%), and predictive analytics (68%). Overall satisfaction was significantly correlated with students’ perception of AI’s potential (r = 0.65, p < 0.001).

Figure 2: Student Satisfaction with Current Digital Tools

Figure 3: Perceived Potential of AI Applications

Qualitative Findings: Content Analysis

Acontent analysis of students’ responses revealed four main themes regarding their experiences with digital learning platforms (see Table 1). The most commonly cited concern, with 147 mentions, was related to interface usability challenges, indicating that many students struggle with navigation, accessibility, and overall user-friendliness. The need for features that facilitate interactive and collaborative learning was highlighted by the presence of Functionality Gaps, which received 112 mentions. Issues related to the fragmentation of tools, where multiple programs are required for a single course without seamless interoperability, were noted as System Integration Deficiencies, mentioned 89 times. Finally, Technical Support Limitations, with 67 mentions, underscored the importance of having prompt and efficient support available.

Figure 4: Frequency Distribution of Content Analysis Themes

                                                   Table 1: Major Themes Identified in Content Analysis

Discussion

Interpretation

Table 1: Major Themes Identified in Content Analysis using a mobile-first strategy and iterative student testing. Second, create unified ecosystems with smooth data synchronization and The results demonstrate that espousing technology by itself does not guarantee better learning opportunities [14]. Deficits in functionality, integration, and interface usability directly affect user pleasure. On the other hand, students’ desire for intelligent yet human-centered systems is evident in their strong optimism about AI integration.

Contributions to Theory

This study extends TAM and UTAUT by demonstrating that contextual implementation factors have significant effects on their key 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 functional and technical obstacles found.

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 single sign-on to guarantee technological integration. Third, switch to proactive technical support that addresses root-cause problems using data analytics. Fourth, strategically tool AI with an emphasis on student-identified goals like automated feedback and tailored learning. Lastly, assign resources to scalable hardware and vital network stability by advancing in initial infrastructure.

Restrictions and Upcoming Projects

The use of self-reported data and the single-institution sample bounds 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.

Limitations

The generalizability of these findings is inadequate by 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 technological innovation and human- centered design is crucial for effective digital transformation in higher education, especially in developing contexts. This study highlights that adequate infrastructure, usability, and technical support are fundamental requirements. Furthermore, rather than viewing AI as a replacement for human education, students perceive it as a collaborative partner that enhances personalized learning. Building cohesive systems, ensuring reliable networks, and, most importantly, maintaining continuous engagement with the learner’s voice are vital for achieving lasting transformation [15,16].

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