From the Student Success edition of October 29, 2026
WashU professors diverge on AI: an intro psychology professor replaced writing assignments with in-class exams after colleagues could not tell AI from student writing
Washington University in St. Louis (+AI initiative; Psychological & Brain Sciences; English) · Higher education teaching, assessment design and academic integrity · St. Louis, Missouri, United States
- Publisher
- Student Life
- Original publication
- Student Life, 29 October 2026
- Source retrieved
- 2026-10-30
What happened
WashU's +AI initiative provides enterprise AI tools and a mandatory Canvas module for incoming students. An Introduction to Psychology professor who uses AI for slides and research ran an informal test giving five faculty a mix of AI and student writing; they said they could not identify the origin. He removed writing assignments and now uses three in-class exams and an in-class final, which he called a terrible way to evaluate students, and said he lacks administrative guidance on AI-resistant writing assignments. A creative writing director rejects AI for learning, ignores +AI emails, and starts integrity concerns with a conversation because detection tools are unreliable. Students said instructors should remain the core source of learning.
Why it matters
A concrete case of AI pressure narrowing assessment to in-class multiple-choice exams, a student-success risk for students who show learning better through writing.
Evidence and measured results
Faculty and student interviews; an informal, unpublished faculty detection test.
Limitations and uncertainty
Two professors; anecdotal test; reporter is a student in one course (disclosed by the paper).
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-30; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Faculty abandon writing assessment for in-class exams because they cannot distinguish AI from student writing and lack guidance.
- Stakeholders
- Teaching and learning center
- Department chairs
- Faculty
- Academic integrity office
- Students
- Discovery questions
- How many courses have removed writing because of AI?
- What assessment-design support exists for new faculty?
- How are integrity concerns handled without detectors?
- Value hypothesis
- Interpretation: discipline-specific AI-resilient assignment patterns could preserve writing without heavy detection reliance.
- Potential engagement
- Assessment redesign support for large intro courses, piloted in one department.
- Unsupported claims
- That faculty cannot detect AI in general, from one informal test
- That in-class exams reduce learning
- That detection tools are effective
Pre-sales engineering
Role takeaway
- Fit
- Institutions with an LMS supporting staged submissions and version history.
- Architecture and integration
- Staged LMS assignments, draft and version capture, and optional in-class or oral checkpoints.
- Prerequisites
- LMS staged-submission features
- Teaching assistant capacity
- Faculty willing to pilot
- Constraints
- Large enrolments raise grading load
- Students can use tools outside managed systems
- Discipline differences
- Security
- Process evidence includes student drafts; set access and retention rules.
- Proof of value
- Grading time per student measured in a pilot
- Student feedback compared with exam format
- Process evidence review feasible for teaching assistants
Delivery
Role takeaway
- Work
- Build an assignment design library, run new-faculty orientation sessions, and pilot staged writing in a large intro course.
- Dependencies
- Teaching center staff
- Faculty participation
- LMS configuration
- Ownership
- Center for teaching and learning with department chairs.
- Skills and adoption
- Faculty workshops on AI-resilient writing; teaching assistant training on process review.
- Governance checkpoints
- Department review of redesigned assessments
- Integrity office review of conversation-first process
- End-of-term pilot review
- Acceptance criteria
- Proposed: pilot course retains a writing component
- Proposed: grading time within agreed limits
- Proposed: student feedback collected and reported
- Risks
- Faculty time constraints
- Uneven adoption across departments
- Students still outsourcing drafts
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?
Institutional AI suites and mandatory LMS modules do not by themselves supply assessment-design support.
Governance
Who approves, reviews and stays accountable for outcomes?
Faculty report no direct administrative guidance on AI-resilient writing assignments.
Security and privacy
What data, permissions and controls need testing?
Not addressed beyond reliance on conversation rather than detection software.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Professor notes multiple-choice formats disadvantage students who are not strong test-takers; faculty want orientation support.
Procurement
What should contracts, pricing and exit terms secure?
Detection tools are distrusted by these faculty, weakening the case for buying them.
Operating model
Which teams own the service once it runs?
Assessment redesign is falling to individual faculty without central support.
Publication history
- 2026-10-29Student Success · Issue 525 resources
Stable resource ID: student-life-washu-professor-drops-writing-for-in-class-exams-ai-detection-20261029