{"resourceId":"kahn-staff-student-ai-perceptions-2026","versions":[{"version":"external-b56f75ad2d0abed77d702798e8172a21f7049f116644ea1fa60ed1bd0179f496","resource":{"id":"kahn-staff-student-ai-perceptions-2026","title":"UK survey exposes differing AI expectations without measuring learning","organization":"Peter Kahn and colleagues; University of Manchester and University of the Basque Country","sector":"Higher education teaching and student learning","geography":"United Kingdom; one social-science school at a research-intensive university","publishedAt":"September 10, 2026; survey conducted April–June 2024","publicationDate":"2026-09-10","eventDate":null,"sourceName":"Still emerging: understanding Generative AI use in Higher Education","sourceLabel":"Original peer-reviewed cross-sectional survey","sourceUrl":"https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1885253/full","evidenceClass":"academic-research","outcomeClass":"emerging","topics":["knowledge-work","governance-procurement","accessibility-workforce","operating-model"],"finding":"Staff and students differed in their expectations and perceptions of AI use; the study does not measure learning gains.","sledRelevance":"Interpretation: Useful for U.S. colleges planning teaching support, with substantial limits from a single UK setting and older observations.","evidence":"Two questionnaires, with 45 complete academic responses and 86 complete student responses; partial responses increase item denominators. Table 1 measures expected performance, not tested achievement. There is no experimental baseline.","architectureImplications":"Interpretation: Use an approved practice environment with explicit task instructions and separate assessment access. No comparative evidence selects cloud, on-premises or hybrid hosting; developer tools and autonomous agents were not evaluated.","governanceImplications":"Interpretation: Ask learners how they actually use assistance before setting assessment rules; do not equate a staff impression with misconduct evidence.","securityPrivacyImplications":"Interpretation: Collect minimal, voluntary workflow examples and anonymize consultation records; avoid surveillance of personal AI accounts.","caveats":"Self-selection, low response, one school and 2024 data constrain current generalization. The underlying dataset is not openly released. Scale consistency cannot establish educational efficacy.","streamIds":["student-success"],"roles":{"sales":"Interpretation: A teaching leader may lack a shared understanding of acceptable AI help across courses. Include faculty, students, learning support and institutional research in discovery. Ask where expectations differ, what independent skill the course assesses, and whether students can obtain help without paid subscriptions. A bounded engagement could examine one course's instructions and test revised examples with both groups. The value hypothesis is clearer expectations and fewer avoidable support problems. The survey provides a reason to investigate this locally, not a forecast of learning gains. Do not promise better grades, retention, reduced misconduct or staff savings. Applicability depends on the local student mix and teaching context.","engineering":"Interpretation: Start with a sandbox exercise that makes permitted assistance observable, such as comparing a generated answer with a student explanation. Prerequisites include faculty-approved tasks, accessible examples and a fixed model configuration. Connect only approved course materials; keep grades and student records outside the experiment. Test whether learners can identify errors and complete an unaided follow-up task. Record model changes so differences are not attributed entirely to training. Validate privacy and course-access boundaries independently of educational quality. This would be a new proof of value, because the survey measures perceptions rather than system behavior. Avoid treating usage logs or satisfaction scores as substitutes for mastery.","delivery":"Interpretation: A course director should own the educational decision, with learning designers facilitating joint staff–student sessions and institutional research defining evaluation. Prepare examples, recruit beyond enthusiastic users, and budget staff time for discussion and revision. Dependencies include accessible participation, consent and clear assessment rules. Proposed acceptance criteria: every tested task has an agreed assistance boundary, participants can explain it in a scenario exercise, and independent follow-up results are reported alongside participation gaps. These are proposed local gates. Review at pilot exit before expanding training or licenses. Risks include selecting only confident users, discouraging candid disclosure and measuring policy recall while overlooking whether learning support works."},"retrievedAt":"2026-09-14T03:02:30Z","enrichedAt":"2026-09-14T03:05:12Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Interpretation: Include disabled learners, multilingual students and non-users in task testing; reserve staff time for learning rather than assuming an efficiency dividend.","procurementImplications":"Interpretation: Require a short evaluation period, accessible access and configuration records; adoption interest alone does not justify long-term licenses.","operatingModelImplications":"Interpretation: Faculty own educational boundaries, learning technology staff own the environment, and evaluators separate perceptions from independent performance.","updateExplanation":"Absent from all 274 archive records inspected across offsets 0, 100 and 200 and targeted DOI search. Recent publication newly added to the archive; no post-last-run event is asserted. It adds direct staff–student comparison to earlier student-only adoption coverage.","sourceVerification":{"openedUrl":"https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2026.1885253/full","referenceExcerpt":"It was clear in this study that staff possessed limited knowledge about ways in which students were making use of GAI tools.","promptVersion":"sled-research-v3.2","model":null,"basis":"agent-reported inspection"}}}]}