{"resourceId":"higher-education-facets-samr-review-2026","versions":[{"version":"external-6b7d7b5dd42bd7d2658f6ba57b4101f0dd0ac31658e612407b31f81a9196414a","resource":{"id":"higher-education-facets-samr-review-2026","title":"September review offers an integration map with substantial evidence-quality limits","organization":"Mona Hmoud AlSheikh, Rania Zaini, Manahel A. ALmulhem and Shakil Ahmad","sector":"Higher education teaching and assessment","geography":"International review; authors based in Saudi Arabia and United Arab Emirates","publishedAt":"September 1, 2026","publicationDate":"2026-09-01","eventDate":null,"sourceName":"Frontiers in Education","sourceLabel":"Original systematic mapping review","sourceUrl":"https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1871468/full","evidenceClass":"academic-research","outcomeClass":"cautionary","topics":["knowledge-work","developers-agents","governance-procurement","accessibility-workforce","operating-model"],"finding":"The review maps AI use cases and integration depth; it does not estimate a pooled learning effect or establish that deeper technology integration is better.","sledRelevance":"Interpretation: A starting inventory for U.S. college teaching pilots, not a local effectiveness forecast.","evidence":"Reports 959 records and 22 included studies, searched across eight databases for January 2015–July 2025. No formal risk-of-bias appraisal or meta-analysis. Table 1 includes a nursing editorial despite eligibility excluding non-empirical commentaries.","architectureImplications":"Interpretation: Inventory the task, user, model, content and integration boundary for each pilot. A taxonomy cannot choose cloud versus local deployment or establish an agent's reliability.","governanceImplications":"Interpretation: Review original study quality before converting mapped applications into institutional policy or an approved-use catalog.","securityPrivacyImplications":"Interpretation: Attach data-flow and access reviews to each mapped use; broad labels cannot establish handling of student records.","caveats":"English-only, excludes grey literature, heterogeneous designs and an older search cutoff. Abstract's single Redefinition claim conflicts with multiple Table 1 labels; exact category counts are not reused.","streamIds":["student-success"],"roles":{"sales":"Interpretation — Academic leaders may need to compare scattered proposals that use different definitions of AI transformation. Include the teaching centre, faculty governance, institutional research, procurement and students. Ask what instructional task changes, which learners benefit, and what primary evidence supports that expectation. Offer a bounded inventory and evidence-quality review before platform selection. A credible value hypothesis is clearer prioritization and fewer unsupported benefit claims. The mapping paper helps frame the conversation but cannot rank vendors or predict local learning gains. Avoid promising that a higher integration category delivers better outcomes, and identify older evidence and reporting inconsistencies before using it in customer materials.","engineering":"Interpretation — Translate each proposed teaching use into a diagram of content inputs, model calls, user permissions, outputs and human review. Prerequisites include named users, approved data classes and an explicit target task. Distinguish code assistance, retrieval support and automated assessment because they require different tests. Cloud, on-premises and hybrid choices need workload-specific privacy, cost and latency evaluation. Proposed proof of value: validate one task with representative learners, adversarial inputs, accessible interactions and independently scored outputs. Record model versions and fallback behaviour. A successful integration test is insufficient evidence of learning; require a separate educational evaluation before increasing deployment scope.","delivery":"Interpretation — Teaching and learning leadership should maintain an evidence register, while a faculty owner accepts each course change and IT owns service reliability. Implementation work includes mapping current practice, verifying primary citations, preparing training and setting review dates. Dependencies include instructional design time, procurement review and measurement capacity. Proposed acceptance criteria: every pilot has an owner, an approved data-flow review, a baseline and a documented stop-or-expand decision. These are proposed gates. Train reviewers to challenge category labels and unsupported outcome language. Risks include stale evidence, overlooking smaller institutions and treating a broad review as independent validation of a local system."},"retrievedAt":"2026-09-09T03:03:35Z","enrichedAt":"2026-09-09T03:03:35Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Interpretation: Include assistive-technology testing and faculty development in every pilot description; classification does not demonstrate equitable access.","procurementImplications":"Interpretation: Require primary outcome evidence and a costed support model per use case, rather than buying on a transformation label.","operatingModelImplications":"Interpretation: Teaching and learning leadership owns the pilot portfolio; faculty own pedagogical changes and institutional research reviews evidence.","updateExplanation":"New URL in the complete 119-resource archive and candidate-specific search. Recent September 1 mapping evidence is newly archived, with original-source quality concerns; no update since yesterday is claimed.","sourceVerification":{"openedUrl":"https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1871468/full","referenceExcerpt":"established risk-of-bias instruments (such as the MMAT) were not used","promptVersion":"sled-research-v3.1","model":null,"basis":"agent-reported inspection"}}}]}