From the Student Success edition of September 17, 2026
Transcription ≠ generation: integrity policies must separate assistive AI format conversion from GenAI authorship
University of Exeter / International Journal for Educational Integrity · Higher education assessment integrity / disability equity · United Kingdom primary framing with international (incl. U.S.) policy references
- Publisher
- International Journal for Educational Integrity (Springer Open)
- Original publication
- Received 19 Feb 2026; Accepted 4 August 2026; IJ EI (2026) 22:24
- Source retrieved
- 2026-09-18
What happened
Conceptual/policy analysis arguing HE GenAI assessment policies often lack technical precision to distinguish generative AI (machine produces assessed intellectual content) from non-generative AI-powered tools—OCR, voice-to-text transcription, handwriting recognition—that perform clerical format conversion on student-authored content. Notes widespread faculty-facing GenAI guidelines (e.g., An et al. 2025 on top U.S. universities) and UK QAA/Russell Group guidance. Argues treating accessibility transcription as misconduct raises fairness concerns under equality law (UK Equality Act reasonable-adjustment duties; parallels to ADA/Australian DDA). Recommends explicit policy language and an evidential framework separating generation from transcription.
Why it matters
Pass B scrutiny complementary to 2026-09-16 Shepherd Against Prohibition: adds technical policy precision on assistive vs generative tools—critical as student-success units deploy voice/OCR supports alongside GenAI tutors. Unarchived; accepted Aug 2026.
Evidence and measured results
Source type: conceptual and policy analysis with interpretive arguments and proposed evidential framework—not an empirical outcomes study. Limitations: UK-weighted legal framing; tribunal outcomes not claimed; depends on cited secondary surveys of university guidelines; no measured false-positive rates in this paper.
Limitations and uncertainty
Not empirical; legal characterizations are interpretive. Transfer to U.S. requires counsel review of ADA/Section 504 interplay with AI integrity rules. Accepted Aug 2026 but not previously archived.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-09-18; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- AI integrity rules risk treating assistive transcription/OCR as cheating, creating disability equity exposure.
- Stakeholders
- Academic integrity
- Disability/accessibility services
- General counsel
- Assessment leads
- Student success
- Discovery questions
- Do policies define generation vs format conversion?
- How are OCR/STT accommodations documented?
- Are detectors used on accommodated submissions?
- Who adjudicates borderline tool use?
- Value hypothesis
- Precise tool-class policies and disclosure UX reduce wrongful misconduct findings while still governing generative authorship.
- Potential engagement
- Policy language workshop plus disclosure-form redesign and case-handler training for integrity/disability teams.
- Unsupported claims
- Empirical proof of tribunal outcomes from this paper
- That banning GenAI is disability-safe
- Detector accuracy guarantees fairness
Pre-sales engineering
Role takeaway
- Fit
- Fits assessment platforms and integrity case systems needing tool-use taxonomy fields.
- Architecture and integration
- Disclosure schema with tool-class enum; allowlisted assistive connectors; human review queue before AI-misconduct flags.
- Prerequisites
- Policy taxonomy
- Accommodation registry integration
- Vendor allowlists
- Audit logs
- Constraints
- Jurisdictional legal variation
- Students using consumer STT
- Incomplete accommodation disclosure
- Security
- Audio/documents in transcription flows need DPIA-level controls and retention limits.
- Proof of value
- Measure false-positive misconduct referrals involving assistive tools
- Time-to-resolution for accommodation-related cases
Delivery
Role takeaway
- Work
- Rewrite integrity codes, train investigators, and align disability services referral paths.
- Dependencies
- Counsel review
- Faculty senate approval
- Platform configuration
- Student communications
- Ownership
- Academic integrity office jointly with disability services.
- Skills and adoption
- Case handlers need technical literacy on OCR/STT vs GenAI; faculty need syllabus examples.
- Governance checkpoints
- Equality impact assessment
- Detector-use restriction sign-off
- Annual policy refresh
- Acceptance criteria
- Published generation-vs-transcription definitions
- Disclosure form fields live
- Investigator checklist updated
- Risks
- Policy lag behind tools
- Inconsistent faculty enforcement
- Chilling effects on accommodation use
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?
Student-success and assessment systems should tag tool-use modality (transcription/OCR vs generative) in disclosure workflows and avoid one-bit “AI used” flags that collapse assistive use into misconduct.
Governance
Who approves, reviews and stays accountable for outcomes?
Source: policies need technical distinctions and equality-law-aware reasonable adjustments. Interpretation: integrity codes for AI tutoring/advising must whitelist documented assistive non-generative uses.
Security and privacy
What data, permissions and controls need testing?
Transcription tools still process student audio/documents. Interpretation: apply vendor controls and disability-data sensitivity even when content is non-generative.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Source centers disability equity: non-generative AI accessibility tools should be governed as reasonable adjustments, not presumptive misconduct—directly shapes student-success accessibility staffing and advocacy.
Procurement
What should contracts, pricing and exit terms secure?
RFPs for integrity/detection must require assistive-use exemptions and human review; procure campus STT/OCR that meet accessibility and privacy bars.
Operating model
Which teams own the service once it runs?
Operating model pairs integrity adjudication with disability services before sanctioning AI-related cases involving format conversion.
Publication history
- 2026-09-17Student Success · Issue 125 resources
Stable resource ID: wright-transcription-not-generation-integrity-2026