{"resourceId":"uef-hakola-reflective-genai-use-grades-20261006","versions":[{"version":"external-a7573577d5900ce9ca7479223ed2a04df4fd53bf1c50a0b9c7af5949d4ff8e17","resource":{"id":"uef-hakola-reflective-genai-use-grades-20261006","title":"University of Eastern Finland: reflective AI use (write first, then ask for feedback) linked to highest take-home exam grades; instrumental use can trail non-users","organization":"University of Eastern Finland (Outi Hakola, Jari Kukkonen, Teemu Valtonen)","sector":"Higher education student success / generative AI study practices and assessment","geography":"Finland (single university; take-home exams 2023-2024)","publishedAt":"UEF news release 6 October 2026 on an article in Computers in Human Behavior Reports vol. 24 (2026), 101340","publicationDate":"2026-10-06","eventDate":null,"sourceName":"University of Eastern Finland news","sourceLabel":"University news release summarizing a peer-reviewed journal article; observational analysis of disclosed AI use","sourceUrl":"https://www.uef.fi/en/article/ai-may-improve-students-grades-when-used-to-support-learning-and-provide-feedback-rather-than","evidenceClass":"academic-research","outcomeClass":"mixed","topics":["knowledge-work","accessibility-workforce","operating-model"],"finding":"UEF researchers analyzed take-home exams from 2023 and 2024 in which students could use AI if they disclosed it; 891 students took part and 159 reported AI use. They identified three approaches: instrumental (delegating the assignment and using output without substantial revision), ideational (using AI for ideas, with students often over-trusting idea quality) and reflective (producing their own content first, then asking AI for feedback or missing perspectives). Reflective users earned the highest grades compared with both other AI users and non-users, while instrumental users' grades could be lower than non-users'. The authors recommend teaching students purposeful AI use rather than treating AI as inherently good or bad.","sledRelevance":"Released 6 October and not yet in the archive, this gives campuses a concrete, assessable distinction between AI use that supports performance and use that undercuts it, which is directly usable in AI-literacy curricula, syllabus policies and student-success coaching.","evidence":"891 students, 159 disclosed AI users; take-home exams 2023-2024; grade comparison across three coded use approaches and non-users. Effect sizes, course mix and controls are not given in the news release.","architectureImplications":"No specific platform; findings concern student practice. Interpretation: tools that prompt students to draft first and request critique align with the reflective pattern.","governanceImplications":"Interpretation: disclosure-based permitted-use policies produced analyzable data; campuses can pair disclosure with guidance that favors draft-first, feedback-seeking use.","securityPrivacyImplications":"Study relies on student self-disclosure in exams; institutional replication would need consent and handling rules for disclosed AI logs.","caveats":"Observational, single university, self-disclosed AI use (only 159 of 891), so selection effects are likely; data from 2023-2024 when tools and awareness were changing; release does not report effect sizes or controls; details rely on the news summary of the journal article.","streamIds":["student-success"],"roles":{"sales":"The framing point is that how students use AI matters more than whether they use it. A University of Eastern Finland study of 891 students found reflective users, who drafted first and then asked AI for feedback, earned the highest grades, while instrumental users could trail non-users. Use it with provosts and student-success leaders planning AI literacy. Keep claims bounded: observational, single university, self-disclosed use, and no effect sizes in the release.","engineering":"For engineering, the framing point is designing for the reflective pattern. Configure campus AI tools and LMS integrations to support draft-first critique, for example prompts that request feedback on student text rather than full generation, and capture disclosure metadata where policy allows. Validate locally: compare grades or rubric scores by use pattern in a pilot course, and keep in mind that self-selection in this study prevents causal claims.","delivery":"Delivery's framing point is turning a use typology into practice. Build a short module on instrumental, ideational and reflective use for first-year seminars and writing centers, align syllabus disclosure language, and define how instructors will recognize each pattern. Pilot in a few courses with consent-based disclosure and compare outcomes by pattern. Treat the Finnish grade differences as a hypothesis to test locally, not a guaranteed result."},"roleDetails":{"sales":{"customerProblem":"Campuses permit AI but lack evidence-based guidance on which student AI practices help or hurt performance.","stakeholders":["Provost or vice provost for undergraduate education","Teaching and learning center","Writing center","First-year experience office","Faculty senate"],"discoveryQuestions":["Do your AI policies require disclosure, and is that data analyzed?","What instruction do students get on draft-first, feedback-seeking AI use?","Which courses could pilot use-pattern guidance?"],"valueHypothesis":"Teaching reflective AI use may lift performance relative to instrumental use; magnitude and causality are unproven outside this observational study.","potentialEngagement":"An AI-use literacy module and disclosure-analysis pilot in two or three gateway courses.","unsupportedClaims":["That reflective AI use causes higher grades","Specific grade gains or effect sizes","That findings transfer unchanged to U.S. institutions"]},"engineering":{"fit":"Campuses configuring AI writing or study assistants and LMS policies.","architectureIntegration":"Feedback-first prompt templates in campus AI tools; optional disclosure fields in LMS assignments linked to grade data for institutional research.","prerequisites":["Approved campus AI tool","Disclosure policy in syllabi","Institutional research access to grades"],"constraints":["Self-disclosure undercounts use","Single-country, 2023-2024 data","No effect sizes published in the release"],"security":"Treat disclosed AI interaction data as education records; restrict access and retention.","proofOfValue":["Compare rubric scores by use pattern in a pilot","Check disclosure rates before and after guidance","Control for prior achievement to address self-selection"]},"delivery":{"work":"Develop and deliver reflective-use guidance, update syllabus disclosure language, and run a measured pilot.","dependencies":["Faculty pilot volunteers","Writing center staff","Institutional research"],"ownership":"Teaching and learning center owns the guidance; academic departments own course adoption; institutional research owns evaluation.","skillsAdoption":"Train instructors and tutors to recognize instrumental versus reflective use and coach students toward draft-first feedback.","governanceCheckpoints":["Policy review of disclosure requirements","IRB or consent review for analysis","End-of-term outcome review"],"acceptanceCriteria":["Guidance delivered in all pilot sections","Disclosure data collected for at least most submissions in pilot","Outcome comparison reported with prior-achievement controls"],"risks":["Students under-disclose AI use","Instructors penalize disclosed use","Overclaiming causality from observational data"]}},"retrievedAt":"2026-10-10T03:24:17Z","enrichedAt":"2026-10-10T03:27:23Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Supports investing in instruction on how to use AI, since the benefit depends on student skill and self-regulation rather than access alone.","procurementImplications":"Prefer AI writing or study tools that support draft-first feedback workflows over answer-generation defaults.","operatingModelImplications":"Embed reflective-use guidance in first-year seminars, writing centers and course policies, and track grades by use pattern where disclosure exists.","sourceVerification":{"openedUrl":"https://www.uef.fi/en/article/ai-may-improve-students-grades-when-used-to-support-learning-and-provide-feedback-rather-than","referenceExcerpt":"If students use AI instrumentally, their grades may even be lower than those of students who do not use AI at all.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}