AJAArab J. Admin.1110-54532663-4473The Arab Journal of AdministrationLeague of Arab States, Arab Administrative Development OrganizationCairo, Egypt10.21608/aja.2025.352984.1779645Research ArticleThe Role of AI Personalization in Shaping Students’ Decision-making: The Mediating Role of Students’ Intentions and the Moderating Effect of Academic Technology Experience in Higher EducationIsmailEmanemanismail@aast.eduEl-ShihyDinaElshihy.dina@ngu.edu.egProfessor of Statistics, Arab Academy for Science, Technology & Maritime Transport (AAST)EgyptAssistant Professor of Marketing, New Giza University (NGU)Egypt042025280120260220261001202513072025461373390This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Purpose: This study investigates the impact of AI-driven personalization on university students’ decision-making, with a particular focus on the mediating role of intention to apply and the moderating effects of experience with academic technology. By exploring these relationships, the research aims to offer insights into how personalized communications influence program choice and foster institutional trust.
Design/methodology/approach: A quantitative approach was employed, with data collected through surveys completed by 425 university students. Structural Equation Modeling (SEM) was applied to analyze the relationships between the variables.
Findings: The findings indicate that AI-powered personalization significantly influences students’ decision-making directly. However, it does not have a direct effect on intention to apply. The mediating role of intention to apply is crucial, linking AI personalization to decision-making. Furthermore, experience with academic technology moderates the relationship between AI personalization and intention to apply, with higher levels of experience strengthening this connection.
Originality: This study provides insights into how AI-powered personalization shapes student decision-making in higher education. The results show that while AI personalization may not directly drive students’ intention to apply, it plays a key role in fostering trust and engagement, which ultimately influences their decisions. Students with more experience in academic technology are particularly responsive to personalized content, emphasizing the importance of tailoring communication strategies to different technological proficiency levels. These findings offer valuable guidance for institutions looking to enhance student engagement and create more meaningful interactions through AI-driven personalization.
AI-Powered PersonalizationIntention to ApplyHigher EducationStudent EngagementTechnology ExperienceProgram SelectionIntroduction
The education sector has undergone a significant shift facilitated by the incorporation of artificial intelligence (AI) technologies, redefining how educational institutions engage with students through personalized interactions. AI-powered tools are increasingly used for tailored communication, including automated emails, targeted advertisements, and customized program recommendations, enhancing engagement and satisfaction.
Amid heightened competition, AI-driven marketing helps institutions attract and retain prospective students by leveraging behavioral and preference data. AI-powered personalization tailors interactions to individual needs, influencing program selection and institutional trust. However, effects in education remain underexplored, especially regarding intention to apply and decision-making. This study assesses the influence of AI personalization on intentions and decisions, tests mediation by intention to apply, and examines moderation by academic technology experience.
Literature ReviewAI-Powered Personalization in Higher Education
AI enables individualized interactions using behavioral, academic, and preference data to deliver personalized emails, targeted ads, and recommendations, improving engagement and application rates. Ethical concerns include privacy, fairness, and bias, requiring transparency and accountability.
Theory of Planned Behavior (TPB)
TPB frames how personalized communications shape attitudes, subjective norms, and perceived behavioral control, influencing intention to apply. AI campaigns highlighting achievements and career outcomes can strengthen intentions and confidence, but TPB must account for algorithmic trust and control perceptions.
Consumer Decision-Making & AI Personalization
AI streamlines information search, evaluation, and choice via personalized messages, comparisons, and reminders, improving conversion. Personalized communications drive engagement and actions such as applications.
AI-Powered Personalization and Intention to Apply
Customized content aligned to interests fosters engagement and application-related behaviors; follow-ups sustain momentum and completion.
Moderating Role of Experience with Academic Technology
Digital literacy and prior exposure shape how students engage with AI personalization. Lower access or skills can hinder engagement, underscoring the need for inclusive design and training.
Research Questions, Objectives, and Hypotheses
Key questions address how AI personalization affects intention and decision-making, mediation by intention, and moderation by technology experience.
H1: AI-powered personalization positively influences students’ intention to apply.H2: Students’ intention to apply positively influences student decision-making.H3: AI-powered personalization directly enhances student decision-making.H4: Students’ intention to apply mediates the relationship between AI-powered personalization and student decision-making.H5: Students’ familiarity with academic technology moderates the relationship between AI-powered personalization and students’ intention to apply.Methodology
This quantitative study surveyed 425 university students (stratified by discipline and tech experience). Measures: AI personalization (Tsai & Men, 2017), intention to apply (Ajzen TPB), decision-making (McKnight et al., 2002; Soutar & Turner, 2002), and experience with academic technology. An online survey (Google Forms) with pilot (n=50) was used. Analyses: descriptive stats, reliability (Cronbach’s α), CFA, SEM for direct/indirect effects, and moderation (PROCESS, Hayes 2022). Ethical approval: IRB of Badr University in Cairo; informed consent; anonymized data.
Results
Sample: 79.1% aged 18–20; 61.6% Management/Business; tech experience: 39.8% beginner, 47.3% intermediate, 12.9% advanced. Reliability and validity were high across constructs. Correlations: AI personalization correlated with intention (r=0.623) and decision-making (r=0.599); intention correlated with decision-making (r=0.700), all p<0.01. SEM showed good fit. H1 not supported (no direct AI→Intention). H2 supported (Intention→Decision). H3 supported (AI→Decision). H4 supported (partial mediation via intention). H5 supported (moderation by tech experience: stronger effects for advanced users).
Tables
Reliability and Validity of Constructs
Construct
Item(s)
Cronbach’s α
Validity
AI-Powered Personalization
Personalized Emails
0.819
0.905
AI-Powered Personalization
Tailored Advertisements
0.885
0.941
AI-Powered Personalization
Program Recommendations
0.875
0.935
Overall AI-Powered Personalization
—
—
—
Intention to Apply (All items)
—
0.923
0.961
Intention to Apply
Expression of Interest
0.738
0.859
Intention to Apply
Request for Information
0.731
0.855
Intention to Apply
Application Submission
0.869
0.932
Student Decision-Making (All items)
—
0.865
0.930
Student Decision-Making
Program Selection
0.770
0.877
Student Decision-Making
Trust in Institution
0.851
0.922
Overall Student Decision-Making
—
0.879
0.938
Source: Survey data analyzed using AMOS.
Correlations between Constructs
Constructs
AI-Powered Personalization
Intention to Apply
Student Decision-Making
AI-Powered Personalization
1.000
0.623**
0.599**
Intention to Apply
1.000
0.700**
Student Decision-Making
1.000
** p < 0.01 (two-tailed). Source: Survey data analyzed using AMOS.
Goodness-of-Fit Indices for Model Evaluation
Fit Index
Value
Threshold
Chi-square (χ²)
47.095
p < 0.05
Degrees of Freedom (df)
15
—
Goodness of Fit Index (GFI)
0.974
> 0.90
Adjusted GFI (AGFI)
0.938
> 0.90
RMSEA
0.071
<= 0.08
Comparative Fit Index (CFI)
0.983
> 0.95
Source: Survey data analyzed using AMOS.
Path Analysis Results
Path
Estimate
Standardized
p-value
Supported?
H1 AI-Powered Personalization → Intention to Apply
Effects of Technology Experience on AI-Powered Personalization → Intention to Apply
Tech Experience
Effect
SE (HC3)
t
p
LLCI
ULCI
Beginner
0.4811
0.1083
4.44
0.00
0.2683
0.6939
Intermediate
0.6155
0.0873
7.05
0.00
0.4439
0.7871
Advanced
0.8446
0.1214
6.95
0.00
0.6059
1.0833
Source: Survey data analyzed using SPSS (PROCESS).
Discussion
AI personalization did not directly raise intention to apply (H1 not supported), but intention significantly drives decision-making (H2), and AI personalization directly affects decision-making (H3). Intention partially mediates AI’s effect on decisions (H4). Technology experience strengthens AI’s effect on intention (H5). Personalization fosters trust and engagement; effectiveness varies by digital literacy.
Implications
Practically, institutions should design transparent, inclusive AI personalization, tailor to tech proficiency, and use interactive tools to build intention and trust. Training staff and monitoring ethics (fairness, privacy) are essential. Theoretically, findings extend TPB with AI-specific mediation and moderation.
Limitations
Cross-sectional self-reports limit causality and may include response bias. Tech terms may be variably understood. Rapid AI evolution may shift effects.
Future Research
Longitudinal and experimental designs, qualitative insights, cross-context comparisons, inclusion of application/behavioral data, and study of emerging AI advising and adaptive learning are recommended.
Declarations
Ethics approval: IRB Badr University in Cairo (BUC). Consent obtained. Data available on request. No competing interests. No funding. Authors contributed equally. Acknowledgements: AI language model was used for editing/proofreading; core content is authors’ work.
Figures
Article first page (screenshot)
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