From the Student Success edition of September 28, 2026
Discourse analysis: HE GenAI research grants AI agency but never makes AI responsible
Charles Darwin University · Higher education / academic research · International English-language HE research corpus (transferable as discourse evidence)
- Publisher
- arXiv
- Original publication
- 2026-09-23
- Source retrieved
- 2026-09-29
What happened
Across 366 HE GenAI titles/abstracts (91,405 tokens), AI is the most frequent named actor (2,050 mentions) with predominantly action predicates, while students are cast as knowers/judges/verifiers; no sentence makes AI explicitly responsible. Responsibility lands on educators, institutions, and policy or disappears in passives and nominalisations—even when texts invoke 'responsible AI.'
Why it matters
Warns U.S. SLED student-success leaders that policy and vendor language celebrating AI agency can outrun named human accountability for tutoring, advising, and assessment decisions.
Evidence and measured results
Corpus-assisted discourse analysis with frequency, collocation, actor–predicate, obligation-clause, and passive/nominalisation heuristics on Scopus/WoS/Springer/IEEE-derived records after deduplication.
Limitations and uncertainty
Titles/abstracts only; incomplete 2026; not an outcomes evaluation.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-09-29; 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 buy 'agentic' student-success AI while policies never name who answers for errors.
- Stakeholders
- provost/academic affairs
- integrity office
- IT governance
- student success
- Discovery questions
- Who is answerable when a tutor over-helps on graded work?
- Do policies define responsible use with named roles?
- Where does vendor language assign agency to the model?
- Value hypothesis
- Bounded value: clarify human accountability language before scaling agentic tutoring/advising.
- Potential engagement
- 2–3 week discourse/policy audit of GenAI student-success tools—not an outcomes guarantee.
- Unsupported claims
- Discourse analysis proves learning harm
- AI systems can be made legally responsible
- Guaranteed integrity improvement from wording changes alone
Pre-sales engineering
Role takeaway
- Fit
- Fits as governance/provenance layer across tutoring and advising GenAI services.
- Architecture and integration
- Action log with human approver IDs; policy engine for obligation clauses; UI attribution strings.
- Prerequisites
- Role directory
- Audit logging
- Policy CMS
- Constraints
- Evidence is corpus discourse
- Title/abstract bias
- No deployment metrics in source
- Security
- Assign data stewards; prevent unowned model access to education records.
- Proof of value
- Named owner per GenAI action class
- Zero 'AI is responsible' product claims in campus UI
- Obligation clauses in published policy
Delivery
Role takeaway
- Work
- Run corpus-informed language audit; rewrite policies; train staff on attribution.
- Dependencies
- Academic affairs
- Legal/integrity
- Communications
- Ownership
- Academic affairs with IT governance co-owner.
- Skills and adoption
- Workshop faculty/advisors on who acts vs who answers.
- Governance checkpoints
- Policy obligation-clause review
- Vendor contract accountability schedule
- Syllabus template update
- Acceptance criteria
- RACI published for each GenAI student-success tool
- Sample syllabi name human verification duties
- Risks
- Symbolic compliance
- Vendor pushback on logs
- Over-reading abstracts as campus practice
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?
Design logs and UI copy that attribute actions to named human roles (instructor, advisor, student) even when models generate drafts; avoid agent-washing in product claims.
Governance
Who approves, reviews and stays accountable for outcomes?
Rewrite 'responsible AI' policies to state who authorizes, who verifies, and who answers for consequences of GenAI-shaped student work and support.
Security and privacy
What data, permissions and controls need testing?
Accountability gaps compound privacy risk when no owner is named for data entered into tutors/advising bots.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
When accountability is unnamed, disabled students relying on GenAI assistance face unclear appeal paths—policies should state human review rights explicitly.
Procurement
What should contracts, pricing and exit terms secure?
Require vendors to identify customer-side accountable roles and logging for AI-generated student-facing outputs; reject agent claims without human override paths.
Operating model
Which teams own the service once it runs?
Separate functional automation metrics from accountability assignments; escalate unresolved 'who answers' gaps before scaling bots.
Publication history
- 2026-09-28Student Success · Issue 223 resources
Stable resource ID: poudyal-genai-agency-accountability-20260923