Perceived institutional support and its effects on student perceptions of AI learning in higher education: the role of mediating perceived learning outcomes and moderating technology self-efficacy
Open accessAbstract
The study aims at gaining insights into relationships between perceived institutional support and students’ perceptions of AI-supported learning. It also investigates the mediating role perceived learning outcomes and moderating effect of technology self-efficacy within this context. Research model was developed and validated based on Social Cognitive Theory (SCT) and the learning outcomes of students. Using quantitative research design and convenience sampling technique, 204 students from higher education institutions were included in the analysis. Data were analyzed using structural equation modeling (SEM) to test the hypothesized relationships. The results revealed that perceived institutional support significantly impacts students’ perceptions of AI-supported learning (β = 0.200, C.R. = 2.291, p = 0.022), technology self-efficacy (β = 0.492, C.R. = 9.671, p < 0.001), and learning outcomes. Additionally, technology self-efficacy was found negative moderating effect (β = −0.146, CR = −2.507, p = 0.012) the relationship between perceived institutional support and AI-supported learning perceptions. Perceived learning outcome partial mediated the relationship between perceived institutional support and students’ perceptions of AI-supported learning, with a direct effect of (β = 0.155, p < 0.001) and an indirect effect of (β = 0.539, p < 0.001), as evidenced by the confidence interval [0.235, 0.549]. These findings highlight the significant interplay of perceived institutional support, technology self-efficacy, and perceived learning outcomes in shaping students’ perceptions of AI in higher education, underscoring the importance of fostering supportive academic environments for effective AI integration. The theoretical and practical implications of the study are discussed.
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Geographic diversity across Global North and South contexts strengthens external validity.
“Geographic diversity across Global North and South contexts strengthens external validity (Hanh and Duyen, 2025; Jeilani and Abubakar, 2025; Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Jeilani and Abubakar, 2025
The Limitations section (section 8; page unavailable) says the study’s focus on Somalia may limit generalizability and calls for cross-cultural, multi-regional studies. That supports the value of geographic breadth, but the paper does not specifically discuss Global North–South diversity or show that such diversity strengthened external validity. (LLM, conf=0.86)
Experimental — accuracy not yet measuredLarge-scale surveys employed rigorous controls and theoretical grounding.
“Methodological scrutiny revealed that large-scale surveys employed rigorous controls and theoretical grounding (Jeilani and Abubakar, 2025; Lund et al., 2025)”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Jeilani and Abubakar, 2025
Section 2.1 grounds the study in Social Cognitive Theory, and Sections 3.1 and 4.1 describe steps to assess or reduce survey bias. But Section 3.2 reports 204 respondents, and the study does not describe rigorous controls; its convenience sample and cross-sectional, self-reported design limit that characterization. (LLM, conf=0.85)
Experimental — accuracy not yet measuredStudents demonstrate generally positive orientations toward AI integration, valuing AI for productivity enhancement and immediate feedback.
“Students demonstrate generally positive orientations, valuing AI for productivity enhancement and immediate feedback (Jeilani and Abubakar, 2025; Kumar et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Jeilani and Abubakar, 2025
Section 2.2 says students generally view AI positively and appreciate its potential for immediate feedback. The paper also includes a survey item about completing tasks more efficiently with AI (References, questionnaire), but the passages do not establish that students specifically value AI for productivity enhancement. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPolitical and economic limitations impede infrastructure access in resource-constrained regions like Palestine and Somalia.
“where political and economic limitations impede infrastructure access (Jeilani and Abubakar, 2025; Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Jeilani and Abubakar, 2025
The Methods (§3.1, page unavailable) place the study in Somalia, and the Discussion (§5, page unavailable) discusses institutional resources and technology infrastructure. Neither passage says political or economic limitations impede infrastructure access; searches for those terms and for Palestine found no supporting passage in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredGeographic inequities in AI integration are pronounced, with academic discourse concentrated in Global North contexts and resource-constrained regions like Palestine and Somalia overlooked.
“Geographic inequities are pronounced: while AI tools have achieved global reach, academic discourse remains concentrated in Global North contexts, overlooking challenges in resource-constrained regions like Palestine and Somalia where political and economic limitations impede infrastructure access (Jeilani and Abubakar, 2025; Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Jeilani and Abubakar, 2025
The Method section (3.1) studies AI-supported learning among undergraduates in Mogadishu, Somalia. The Limitations section (8) calls for multi-regional research but does not say academic discourse is concentrated in the Global North or that Somalia and Palestine are overlooked. Searches for geographic research disparities and political, economic, or infrastructure barriers found no passage establishing those claims. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Students in under-resourced contexts like Palestine and Somalia face connectivity limitations and political constraints that fundamentally impede access.
“Students in under-resourced contexts like Palestine and Somalia face connectivity limitations and political constraints that fundamentally impede access (Jeilani and Abubakar, 2025; Khlaif et al., 2025).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Jeilani and Abubakar, 2025
The Method (§3.1) studies undergraduates in Mogadishu, Somalia, but requires participants to have prior experience with AI; it does not establish barriers to access. The Limitations (§8) explicitly say accessibility challenges were unaddressed. Searches for connectivity limitations, political constraints, and Palestine found no passage supporting those claims. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Jeilani, A., & Abubakar, S.
- Affiliations
- Mogadishu University (SO)
- Corresponding author
- Jeilani, Abdulkadir
- ORCID
- Jeilani, Abdulkadir
- Venue
- Frontiers in Education
- Publisher
- Frontiers Media SA
- Published
- 2025-03-13
- Volume
- 10
- ISSN
- 2504-284X (ISSN-L)
- OpenAlex
- W4408411005
- Cited by
- 64
- References
- 71
- Citations by year
- 2026: 40 · 2025: 24
- FWCI
- 66.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Education (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Information Systems › Education and Learning Interventions
- Keywords
- perceived institutional support, AI-assisted learning, student perceptions of AI, technology self-efficacy, perceived learning outcomes, mediating effect, moderating effect, higher education, social cognitive theory
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