From the Student Success edition of October 19, 2026
Korea Times: NTU plans AI in 40% of courses by 2030, NUS says assess thinking not answers, and KCL calls a return to handwritten exams backward-looking
The Korea Times (reporting on Nanyang Technological University, National University of Singapore, King's College London, LSE and Korean universities) · Higher education AI literacy, curriculum and assessment redesign · Singapore, United Kingdom and South Korea
- Publisher
- The Korea Times
- Original publication
- The Korea Times, 18 October 2026 (KST); first of a three-part series
- Source retrieved
- 2026-10-20
What happened
NTU aims to integrate AI into 40 percent of courses across all 52 undergraduate programmes by 2030, up from 5 percent, with half using AI to personalise learning and half teaching students to develop and manage AI agents. NUS President Tan Eng Chye says pedagogy must drive technology and assessment should focus on reasoning rather than answers. KCL is incorporating AI into teaching and assessment, giving law students professional AI software, and President Shitij Kapur calls returning to handwritten exams a backward-looking answer. LSE gives all students Claude access and faculty time to redesign courses. Korea is expanding AI departments, with concerns that enrolment growth alone will not prepare students.
Why it matters
Why now: published just before the last run but not archived, and directly relevant to the 19 October Stanford move back to paper assessment. Leading international universities are taking the opposite stance on assessment, which frames the choice U.S. institutions face.
Evidence and measured results
Quoted plans and positions from institutional leaders. No outcome data or evaluations.
Limitations and uncertainty
Feature journalism; leaders describe their own institutions. Highly selective, non-U.S. universities. Plans, not results.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-19; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Academic leaders must choose between restricting AI in assessment and integrating it, with little comparative evidence.
- Stakeholders
- Provost and deans
- Teaching and learning center
- Assessment and accreditation lead
- CIO
- Faculty curriculum committees
- Discovery questions
- Which programmes are moving toward in-person assessment and which toward AI-integrated assessment?
- What release time is available for course redesign?
- How will reasoning, not answers, be assessed?
- What institution-wide AI access do students have today?
- Value hypothesis
- Interpretation: a deliberate, programme-level assessment strategy could reduce ad hoc course responses and align AI use with learning outcomes.
- Potential engagement
- A bounded programme-level assessment strategy review comparing integration and restriction options in two or three programmes.
- Unsupported claims
- Learning gains from AI-integrated curricula
- Employability outcomes
- That handwritten exams are ineffective
- Transferability from selective international universities
Pre-sales engineering
Role takeaway
- Fit
- Relevant to institutions planning campus-wide AI access and AI-integrated coursework.
- Architecture and integration
- Institution-wide AI tools linked to LMS and identity; agent-building environments for coursework. Not detailed in the article.
- Prerequisites
- Enterprise AI licensing with education data terms
- LMS integration
- Identity and access management
- Sandbox for student agent projects
- Constraints
- Non-U.S. examples; FERPA mapping needed
- Plans, not evaluated deployments
- Selective-institution context
- Cost of whole-student licensing
- Security
- Education-grade data terms, no training on student data, logging appropriate to assessment use, and sandboxing for agent coursework.
- Proof of value
- Pilot comparing AI-integrated assessment with oral or proctored checks
- Student reasoning rubric reliability test
- License usage and cost review
- Data-processing terms review
Delivery
Role takeaway
- Work
- Select pilot programmes, fund faculty redesign time and evaluate AI-integrated assessments against current ones.
- Dependencies
- Faculty release time
- Enterprise AI access
- Teaching-centre support
- Assessment committee approval
- Ownership
- Academic affairs with the teaching and learning center.
- Skills and adoption
- Faculty development on assessing reasoning with AI present; student AI literacy within disciplines.
- Governance checkpoints
- Curriculum committee approval
- Accreditation alignment check
- Data-protection review of AI tools
- End-of-year evaluation
- Acceptance criteria
- Proposed: pilot programmes complete redesign of targeted courses on schedule
- Proposed: rubric reliability for reasoning assessments meets an agreed threshold
- Proposed: student performance compared with prior cohorts
- Proposed: faculty workload tracked against release time
- Risks
- Redesign without faculty time
- Over-reliance on vendor tools
- Weak measures of reasoning
- Uneven adoption across programmes
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?
Personalised-learning use across 40% of courses implies institution-wide AI platforms and LMS integration; details are not given.
Governance
Who approves, reviews and stays accountable for outcomes?
Positions favour integrating AI into assessment over restriction; governance processes are not described.
Security and privacy
What data, permissions and controls need testing?
Institution-wide provision of commercial AI tools (LSE Claude access) raises data-processing questions not covered in the article.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
LSE gives faculty time away from teaching to redesign courses, which signals the workforce cost of assessment redesign.
Procurement
What should contracts, pricing and exit terms secure?
Whole-student access to commercial AI tools is becoming a baseline at leading institutions, which affects U.S. license decisions.
Operating model
Which teams own the service once it runs?
Curriculum-wide AI integration targets require programme-level redesign and faculty release time rather than course-by-course adoption.
Publication history
- 2026-10-19Student Success · Issue 425 resources
Stable resource ID: korea-times-global-universities-ai-assessment-ntu-nus-kcl-20261018