From the Student Success edition of October 31, 2026
University of the West of Scotland academics argue for teaching critical AI literacy instead of detection and bans, setting out five classroom practices
University of the West of Scotland (authors); Times Higher Education Campus · Higher education teaching, assessment and AI literacy · Scotland, United Kingdom
- Publisher
- Times Higher Education (THE Campus)
- Original publication
- THE Campus, 30 October 2026
- Source retrieved
- 2026-11-01
What happened
Naeem Ramzan and Shahid Awan of the University of the West of Scotland (THE Campus, 30 October 2026) argue that detection software and prohibition rarely change how students learn, that detection tools are unreliable, and that the students least able to contest a false accusation bear the brunt. They propose teaching critical AI literacy through five practices: make student thinking visible through reasoning, annotated drafts and short oral defences; have students argue with and find failures in AI output; have staff model their own AI use openly; keep rules short, plain and written into each assessment brief; and protect the supervisory relationship from turning into surveillance. They acknowledge this asks more of staff.
Why it matters
Connects AI policy to student equity and belonging: detection-led enforcement can fall hardest on students least able to contest accusations.
Evidence and measured results
Practitioner argument and recommendations; no measured outcomes.
Limitations and uncertainty
Opinion piece without evaluation data; UK context; recommendations untested in the article.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-11-01; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Detection and bans are not changing how students learn and may harm students least able to contest accusations.
- Stakeholders
- Teaching and learning center
- Programme leads
- Academic integrity office
- Student union
- Dean of students
- Discovery questions
- How many misconduct cases rely mainly on detector scores?
- Do assessment briefs state task-level AI rules?
- What support do staff have to redesign assessments?
- Value hypothesis
- Interpretation: clearer task-level rules and visible-thinking assessments may improve fairness and student confidence about AI use.
- Potential engagement
- Assessment brief rewrite and AI literacy task design for two or three programmes over one term.
- Unsupported claims
- Reduced misconduct
- Improved learning outcomes
- Detection tools have no role
- Low staff workload
Pre-sales engineering
Role takeaway
- Fit
- Workflow support for drafts, annotations and oral defences rather than new AI tools.
- Architecture and integration
- Use LMS version history, annotation and media submission features to capture student reasoning.
- Prerequisites
- LMS draft and media submission support
- Storage rules for recordings
- Assessment brief templates
- Constraints
- Opinion-based recommendations
- UK context
- Staff time limits
- Security
- Recorded oral defences and draft histories need retention limits and access controls.
- Proof of value
- Fewer student queries about allowed AI use
- Staff time logged for redesigned tasks
- Student survey on rule clarity
Delivery
Role takeaway
- Work
- Staged redesign of assessment briefs and tasks around critical AI literacy and visible thinking.
- Dependencies
- Programme lead buy-in
- Teaching center capacity
- Integrity office alignment
- Ownership
- Teaching and learning center with programme leads.
- Skills and adoption
- Staff need examples and time to design argue-with-the-machine tasks and to model their own AI use.
- Governance checkpoints
- Approval of task-level AI statement template
- Integrity office review of detection reliance
- End-of-term review of workload and clarity
- Acceptance criteria
- Proposed: every redesigned brief includes a plain task-level AI statement
- Proposed: at least one visible-thinking component per redesigned module
- Proposed: student rule-clarity rating improves on baseline
- Risks
- Staff workload overload
- Inconsistent adoption
- Untested assumptions about learning gains
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?
Shifts investment from detection tools toward assessment design and staff development.
Governance
Who approves, reviews and stays accountable for outcomes?
Recommends short, task-level AI rules in each assessment brief and open staff disclosure of AI use.
Security and privacy
What data, permissions and controls need testing?
Warns that supervision tilted toward detection becomes surveillance; no data-handling detail.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Redesign and dialogue-based assessment require more staff time, which the authors acknowledge.
Procurement
What should contracts, pricing and exit terms secure?
Questions continued spending on AI detection tools relative to curriculum design support.
Operating model
Which teams own the service once it runs?
Teaching centres would carry the redesign work across disciplines.
Publication history
- 2026-10-31Student Success · Issue 544 resources
Stable resource ID: the-campus-uws-critical-ai-literacy-over-detection-five-practices-20261030