From the Student Success edition of October 9, 2026
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
University of Eastern Finland (Outi Hakola, Jari Kukkonen, Teemu Valtonen) · Higher education student success / generative AI study practices and assessment · Finland (single university; take-home exams 2023-2024)
- Publisher
- University of Eastern Finland news
- Original publication
- UEF news release 6 October 2026 on an article in Computers in Human Behavior Reports vol. 24 (2026), 101340
- Source retrieved
- 2026-10-10
What happened
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.
Why it matters
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 and measured results
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.
Limitations and uncertainty
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.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-10; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- 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
- Discovery questions
- 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?
- Value hypothesis
- Teaching reflective AI use may lift performance relative to instrumental use; magnitude and causality are unproven outside this observational study.
- Potential engagement
- An AI-use literacy module and disclosure-analysis pilot in two or three gateway courses.
- Unsupported claims
- That reflective AI use causes higher grades
- Specific grade gains or effect sizes
- That findings transfer unchanged to U.S. institutions
Pre-sales engineering
Role takeaway
- Fit
- Campuses configuring AI writing or study assistants and LMS policies.
- Architecture and integration
- 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.
- Proof of value
- 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
Role takeaway
- 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.
- Skills and adoption
- Train instructors and tutors to recognize instrumental versus reflective use and coach students toward draft-first feedback.
- Governance checkpoints
- Policy review of disclosure requirements
- IRB or consent review for analysis
- End-of-term outcome review
- Acceptance criteria
- 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
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
No specific platform; findings concern student practice. Interpretation: tools that prompt students to draft first and request critique align with the reflective pattern.
Governance
Who approves, reviews and stays accountable for outcomes?
Disclosure-based permitted-use policies produced analyzable data; campuses can pair disclosure with guidance that favors draft-first, feedback-seeking use.
Security and privacy
What data, permissions and controls need testing?
Study relies on student self-disclosure in exams; institutional replication would need consent and handling rules for disclosed AI logs.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Supports investing in instruction on how to use AI, since the benefit depends on student skill and self-regulation rather than access alone.
Procurement
What should contracts, pricing and exit terms secure?
Prefer AI writing or study tools that support draft-first feedback workflows over answer-generation defaults.
Operating model
Which teams own the service once it runs?
Embed reflective-use guidance in first-year seminars, writing centers and course policies, and track grades by use pattern where disclosure exists.
Publication history
- 2026-10-09Student Success · Issue 333 resources
Stable resource ID: uef-hakola-reflective-genai-use-grades-20261006