In short: every indicator is scored on the same five-level scale, against written descriptions of each level. Assessors cite evidence for every score, record their confidence, and score alone. This page is the complete rubric used by our assessors.
How to score
| Level | Name | Points |
|---|---|---|
| 1 | Absent | 0 |
| 2 | Emerging | 25 |
| 3 | Established | 50 |
| 4 | Advanced | 75 |
| 5 | Leading | 100 |
Indicator points are combined into pillar scores, and pillar scores into the overall score, each weighted for the ranking's field and level.
- Score the evidence, not the claim. Marketing pages count only when corroborated by regulations, syllabi, reports or data.
- Award a level only when all of its description is met. If an entry sits between two levels, award the lower one.
- Cite at least one evidence item for every level you award, by its ID in the evidence file.
- Use "Insufficient evidence" only after checking the evidence file and finding nothing relevant. It is never a neutral default, and it is never imputed above Established.
- Read the note for the ranking's level of study (master's, institution, DBA or PhD) where one is given. It overrides the general reading.
- Evidence should be no older than 36 months; research indicators use a five-year window.
- Score alone. Do not discuss an entry with other assessors before you submit.
- Record your confidence honestly. Low confidence widens the published band; it does not lower the score.
Confidence
| Confidence | Meaning |
|---|---|
| High | Two or more independent sources agree, or one authoritative primary source (program regulations, audited data) settles the question. |
| Medium | One good primary source, or several sources with minor gaps or inconsistencies. |
| Low | Indirect, partial or dated evidence; the level is a reasoned judgment rather than a clear reading. |
When two assessors' scores for a pillar differ by more than 15 points, a senior assessor decides. An entry with more than 25% of its indicators insufficiently evidenced is not ranked.
AI Integration
Is AI part of how and what students learn?
AI.1 AI across the core curriculum
The extent to which AI and data literacy are built into required learning for every student, rather than confined to electives.
Does every student meet AI in required courses, and use it to do real work?
| Level | Description |
|---|---|
| 1 Absent | No required course addresses AI or data. AI appears, if at all, in electives or marketing material only. |
| 2 Emerging | AI is covered in one required course or a short module, mainly as awareness rather than applied skill. |
| 3 Established | At least one required course is substantially about AI or data, with assessed learning outcomes, and AI appears in a few other required courses. |
| 4 Advanced | AI and data are integrated across most required courses, with assessed applied work in which students use AI to analyze, build or decide, and a clear progression from foundations to application. |
| 5 Leading | AI is a designed thread through the entire core: every required course has explicit AI-related outcomes, students produce assessed AI-enabled work in the discipline, and the AI content was updated within the last 24 months. |
By level of study
- Institutions: schools or faculties assessed across their portfolio: Judge the programs in scope as a portfolio. Levels 4 and 5 require most programs to meet the description, not a single flagship.
- Professional doctorates (DBA): Read "required courses" as the required research training: AI methods for applied research, data handling and reproducibility.
- Research doctorates (PhD): Read "required courses" as the required research training: AI and computational methods, reproducibility and responsible use of AI tools in research.
Evidence to look for
- Program regulations listing required courses
- Core syllabi with learning outcomes (current or previous academic year)
- Assessment briefs for core courses
AI.2 Responsible AI and governance
Teaching of the ethical, legal, social and governance dimensions of AI, with outcomes that are assessed, not only discussed.
Do students learn to judge when and how AI should be used, and is that learning assessed?
| Level | Description |
|---|---|
| 1 Absent | No identifiable teaching on AI ethics, risk, regulation or governance. |
| 2 Emerging | Responsible AI appears in course descriptions or one-off sessions, without assessed outcomes. |
| 3 Established | At least one course or substantial module covers AI ethics, risk and regulation (for example the EU AI Act, data protection, bias) with assessed outcomes. |
| 4 Advanced | Responsible-AI content is integrated into discipline courses through cases and assignments; students practise risk assessment or governance design, and outcomes are assessed. |
| 5 Leading | Responsible AI is treated as a professional competence across the program: assessed in several courses, tied to current regulation and standards, informed by faculty research or policy work, and reflected in the program's own AI governance. |
By level of study
- Research doctorates (PhD): Include research ethics with AI: data governance, disclosure of AI use, authorship and ethics-board procedures that cover AI.
- Professional doctorates (DBA): Include research ethics with AI and governance of AI in the candidate's own organization.
Evidence to look for
- Syllabi and module descriptions
- Assessment briefs and rubrics
- Faculty research or policy work on AI governance
AI.3 Tools, data and compute access
Whether students and faculty can use current AI systems, datasets and computing resources for learning and research, safely and fairly.
Can every student work hands-on with current AI tools and data?
| Level | Description |
|---|---|
| 1 Absent | No institutional provision: students rely on personal accounts, or AI tools are prohibited without an alternative. |
| 2 Emerging | Limited provision: a few licensed tools or lab access for selected courses or students. |
| 3 Established | Institution-provided access to at least one current general-purpose AI system for all students in scope, plus the analytical software and datasets used in courses. |
| 4 Advanced | Broad and equitable access: current AI systems with data-protection safeguards, datasets and compute (for example cloud credits or GPU time) for all students who need them, with training and support. |
| 5 Leading | A leading environment: a secure institutional AI platform, research-grade compute and curated data, documented equity of access so that cost is no barrier, and regular renewal of tools. |
By level of study
- Research doctorates (PhD): Weigh research compute heavily (GPU clusters, allocation policies, data access agreements). Level 5 requires research-grade compute available to doctoral candidates.
Evidence to look for
- IT service and licensing pages
- Student handbooks
- Compute and lab documentation
- Data-protection notices for AI tools
AI.4 AI policy and teaching capability
A clear published policy on AI in teaching and assessment, assessments redesigned for an AI world, and faculty development that supports both.
Does the institution know how it wants AI used, and are faculty equipped to teach that way?
| Level | Description |
|---|---|
| 1 Absent | No published policy on AI in teaching or assessment. |
| 2 Emerging | A general statement or interim guidance exists but is not applied consistently at course level, and there is no faculty development. |
| 3 Established | A published policy defines permitted and prohibited uses; syllabi state their AI rules; some faculty development is offered. |
| 4 Advanced | The policy is applied course by course; assessments have been redesigned to stay valid (oral defences, process portfolios, supervised work); faculty development is regular, with meaningful participation. |
| 5 Leading | The policy is reviewed at least annually with student input; assessment validity is monitored; faculty development is systematic and evaluated; and the institution shares its practice publicly. |
Evidence to look for
- Dated policy documents
- AI clauses in syllabi
- Teaching and learning centre programs and participation data
- Public reports or open resources
Academic Depth
Is the learning rigorous and grounded in research?
DEP.1 Research quality and relevance
Volume, field-normalized impact and relevance of research produced by the faculty behind the entry, measured mainly from open bibliometric data over five years.
Does the faculty produce research that matters in the field?
| Level | Description |
|---|---|
| 1 Absent | Little or no peer-reviewed output in the field over the last five years. |
| 2 Emerging | Modest output, with field-normalized impact clearly below world average (below about 0.8). |
| 3 Established | Steady output with field-normalized impact around world average (about 0.8 to 1.2). |
| 4 Advanced | Substantial output with field-normalized impact clearly above world average (about 1.2 to 1.5), including work in leading venues. |
| 5 Leading | Large, sustained output well above world average (above about 1.5), with recognized leadership in the field, including research on AI or its effects. |
By level of study
- Master's programs, such as the MBA: Use the publications of faculty who teach in the program or of the owning school.
- Institutions: schools or faculties assessed across their portfolio: Use the relevant faculties only, not the whole university.
- Professional doctorates (DBA): Use the publications of supervisors and of the owning school.
- Research doctorates (PhD): Use the publications of supervisors and of the department hosting the program.
Evidence to look for
- OpenAlex works and citations (CC0)
- CWTS Leiden Ranking Open Edition indicators such as PP(top 10%) and MNCS (CC0)
- Faculty publication lists
DEP.2 Faculty qualifications and research activity
The academic qualifications and active research engagement of the people who teach.
Are students taught by doctorally qualified, research-active faculty?
| Level | Description |
|---|---|
| 1 Absent | Fewer than a quarter of teaching faculty hold a doctorate, or qualifications are not disclosed. |
| 2 Emerging | A quarter to half hold a doctorate; research activity is limited to a few individuals. |
| 3 Established | Half to three quarters hold a doctorate, and most of them have published peer-reviewed work in the last five years. |
| 4 Advanced | More than three quarters hold a doctorate and are research active; practitioners who teach complement them and are well qualified. |
| 5 Leading | Nearly all core faculty are doctorally qualified and research active, with external recognition (grants, editorial roles, fellowships), and they teach the core, not only electives. |
By level of study
- Master's programs, such as the MBA: Practitioner faculty with strong professional credentials support levels 4 and 5 but do not count toward the doctoral share.
- Professional doctorates (DBA): Practitioner faculty with strong professional credentials support levels 4 and 5 but do not count toward the doctoral share.
Evidence to look for
- Faculty directories with qualifications
- Accreditation self-studies
- OpenAlex author records
DEP.3 Rigor of assessment
Whether students must produce original, examined work that cannot be completed by AI alone, and whether academic integrity is protected.
Is passing the program hard proof of capability?
| Level | Description |
|---|---|
| 1 Absent | Assessment relies mainly on unsupervised take-home tasks or quizzes, with no original research or examined project. |
| 2 Emerging | Some substantial assignments, but no individually examined capstone and no AI-aware integrity measures. |
| 3 Established | An individually assessed capstone, project or thesis, and published integrity rules that address AI. |
| 4 Advanced | Several examined pieces of original work, at least one with an oral or supervised component, and external examination or moderation. |
| 5 Leading | Assessment designed for validity with AI: authentic tasks, oral defences, staged submissions and external examiners, with monitoring of grade integrity. |
By level of study
- Research doctorates (PhD): Thesis examination by independent external examiners with an oral defence is expected for level 3. Level 5 requires published candidate output and documented examination standards.
- Professional doctorates (DBA): Thesis examination by independent external examiners with an oral defence is expected for level 3.
Evidence to look for
- Program regulations
- Capstone or thesis handbooks
- External examiner reports
- Academic integrity policies
DEP.4 Doctoral training and supervision
Quality of supervision, progression and completion in doctoral training.
Do doctoral candidates get the supervision and structure they need to finish with strong work?
Applies to: Institutions: schools or faculties assessed across their portfolio; Professional doctorates (DBA); Research doctorates (PhD).
| Level | Description |
|---|---|
| 1 Absent | No information on supervision, progression or completion. |
| 2 Emerging | Supervision arrangements are described, but there is no structured training, progression review or completion data. |
| 3 Established | Each candidate has at least two supervisors or a committee; progression is reviewed annually; research training is required; completion data is published. |
| 4 Advanced | A structured program with milestones, a reasonable supervision load (about six candidates per supervisor or fewer), completion within the expected time for most candidates, and regular publication by candidates. |
| 5 Leading | Excellent supervision ratios, high on-time completion, strong publication and placement records, and investment in candidate development, including training in AI-era research methods. |
By level of study
- Institutions: schools or faculties assessed across their portfolio: Assess the doctoral programs in the relevant field or fields.
Evidence to look for
- Doctoral regulations
- Graduate school reports
- Completion and time-to-degree statistics
- Candidate publications (OpenAlex)
Field Connection
Does learning meet real organizations and real problems?
FLD.1 Experiential learning with partners
Learning that puts students to work on real problems with real organizations, as an assessed part of the program.
Do students work on real problems with real organizations, and is that work assessed?
| Level | Description |
|---|---|
| 1 Absent | No structured experiential component. |
| 2 Emerging | Optional internships or occasional guest lectures; partner work is not assessed. |
| 3 Established | At least one required, assessed project or placement with an external organization. |
| 4 Advanced | Several required experiential components with named partners, including AI-related projects, with academic supervision and partner feedback. |
| 5 Leading | Experiential learning is central: most students complete several assessed partner projects with documented outcomes for partners, and AI-related problems are routine. |
By level of study
- Professional doctorates (DBA): Practice-based research inside the candidate's own organization counts when it has organizational sponsorship and academic supervision.
- Research doctorates (PhD): Read as industry-partnered research, placements, internships or co-supervision with non-academic partners.
Evidence to look for
- Course descriptions
- Partner lists and project catalogs
- Placement statistics
- Project reports
FLD.2 Practitioner involvement in the curriculum
The role of practitioners in designing and delivering learning.
Do practitioners shape what is taught, not only appear in it?
| Level | Description |
|---|---|
| 1 Absent | No practitioner involvement documented. |
| 2 Emerging | Guest speakers or an advisory board without visible influence on the curriculum. |
| 3 Established | Practitioners teach parts of the program, and an advisory board reviews the curriculum periodically. |
| 4 Advanced | Practitioner faculty and advisory boards have documented influence on recent curriculum changes, including changes on AI. |
| 5 Leading | Systematic co-design of the curriculum with employers and professional bodies, renewed regularly, with evidence of changes made in response. |
Evidence to look for
- Advisory board membership and reports
- Faculty lists with roles
- Curriculum review documents
FLD.3 Applied research and knowledge transfer
Research and knowledge exchange with industry, government and civil society.
Does the faculty's knowledge reach organizations that use it?
| Level | Description |
|---|---|
| 1 Absent | No identifiable applied research or knowledge transfer. |
| 2 Emerging | Occasional collaborations or contract research, not systematically reported. |
| 3 Established | Regular collaborations, some publications co-authored with non-academic partners, and a knowledge-transfer function. |
| 4 Advanced | A substantial portfolio: co-authorship with non-academic partners above field average, funded partnerships, licensing or policy contributions. |
| 5 Leading | Recognized leadership in applied impact: sustained partnerships, documented societal or economic impact, and spin-offs or standards work related to AI. |
Evidence to look for
- OpenAlex co-authorship with non-academic organizations (ROR types)
- Annual and technology-transfer reports
- Impact case studies
FLD.4 Entrepreneurship and innovation support
Support for students and graduates who create ventures or bring innovations to use.
Can students turn ideas into ventures with real support?
| Level | Description |
|---|---|
| 1 Absent | No entrepreneurship or innovation support. |
| 2 Emerging | Occasional events, or a single elective on entrepreneurship. |
| 3 Established | A structured offer (incubator, accelerator or venture course) available to students in scope. |
| 4 Advanced | An active ecosystem with mentoring, access to funding and a track record of student or alumni ventures. |
| 5 Leading | A leading ecosystem with documented venture outcomes, AI-focused ventures and integration into the curriculum. |
By level of study
- Research doctorates (PhD): Include research commercialization and spin-out support for doctoral researchers.
Evidence to look for
- Incubator and accelerator pages
- Venture lists and outcome reports
Future Readiness
Is the program built to keep changing?
FUT.1 Curriculum renewal
How quickly and systematically the curriculum is reviewed and renewed.
Is the program built to keep changing?
| Level | Description |
|---|---|
| 1 Absent | No evidence of curriculum review in the last five years. |
| 2 Emerging | Reviews are irregular, and changes in the last 24 months are minor. |
| 3 Established | A formal review cycle of five years or less, and at least one substantive update in the last 24 months. |
| 4 Advanced | Annual review mechanisms, several substantive updates in the last 24 months including on AI, and a fast-track process for new courses. |
| 5 Leading | Continuous renewal: documented annual updates, emerging topics introduced within one academic year, and evaluation of the effect of changes. |
Evidence to look for
- Dated program regulations (compare versions)
- Review reports
- Archived syllabi
- Announcements of curriculum changes
FUT.2 Interdisciplinary pathways
Structured ways to combine domain expertise with AI, data and other disciplines.
Can students combine their discipline with AI and other perspectives in a structured way?
| Level | Description |
|---|---|
| 1 Absent | Discipline-only study with no cross-disciplinary options. |
| 2 Emerging | A few electives outside the discipline. |
| 3 Established | Structured options (tracks, minors, concentrations) combining the discipline with AI or data. |
| 4 Advanced | Joint degrees or cross-faculty teaching that integrate domain expertise with AI and data, open to most students. |
| 5 Leading | Interdisciplinary by design: team-taught courses, shared projects across faculties, and students routinely combining domain, technical and societal perspectives. |
Evidence to look for
- Program structures and track descriptions
- Joint-degree regulations
- Cross-listed course catalogs
FUT.3 Sustainability, society and the future of work
Teaching and research on the societal effects of AI, sustainability and changing work.
Does the program prepare people for the consequences of the technology, not only its use?
| Level | Description |
|---|---|
| 1 Absent | Not addressed. |
| 2 Emerging | Mentioned in electives or events only. |
| 3 Established | Required content on sustainability or societal impact, including the effect of AI on work. |
| 4 Advanced | Integrated across courses with assessed outcomes and links to faculty research. |
| 5 Leading | A program-wide commitment with assessed outcomes, research leadership and public engagement on AI and society. |
Evidence to look for
- Syllabi and learning outcomes
- Research centers and outputs
- Public engagement records
FUT.4 Lifelong learning
Pathways for graduates and professionals to keep learning, especially on AI.
Does learning continue after graduation?
Applies to: Master's programs, such as the MBA; Institutions: schools or faculties assessed across their portfolio; Professional doctorates (DBA).
| Level | Description |
|---|---|
| 1 Absent | No lifelong learning offer. |
| 2 Emerging | Occasional executive courses not connected to degrees. |
| 3 Established | Short courses or micro-credentials in AI-related topics, open to alumni and professionals. |
| 4 Advanced | Stackable credentials that count toward degrees, and structured alumni upskilling. |
| 5 Leading | A comprehensive offer with recognition of prior learning, AI upskilling pathways and evidence of uptake. |
Evidence to look for
- Executive education catalogs
- Credit-transfer and stacking rules
- Alumni learning programs and uptake data
Learner Outcomes
What happens to people who study here?
OUT.1 Graduate outcomes
Verified career and further-study outcomes of graduates.
What happens to people after they graduate?
| Level | Description |
|---|---|
| 1 Absent | No outcome data published. |
| 2 Emerging | Outcome data published without method: no response rate, definitions or timing. |
| 3 Established | Outcome data with method: employment or further study at about six months, with response rates stated. |
| 4 Advanced | Audited or externally standardized outcome data, strong relative to peers, with information on roles. |
| 5 Leading | Longitudinal outcomes over three years or more, externally verified, strong results, including evidence of graduates in AI-shaped roles. |
By level of study
- Master's programs, such as the MBA: For MBA programs, outcomes reported under a recognized standard (for example the MBA CSEA standards) support level 4.
- Research doctorates (PhD): Read as placement in academic, research and industry positions, tracked for at least three cohorts.
- Professional doctorates (DBA): Read as career progression and organizational impact reported by graduates, verified where possible.
Evidence to look for
- Employment reports
- National graduate surveys
- Audited outcome reports
OUT.2 Completion and support
Completion rates, time to degree and the support that makes completion likely.
Do students who start, finish?
| Level | Description |
|---|---|
| 1 Absent | No completion data. |
| 2 Emerging | Completion data without context, or completion clearly low for the program type. |
| 3 Established | Completion rates published and in line with norms for the program type, with academic advising available. |
| 4 Advanced | High completion, structured advising, wellbeing and career support, and early-warning systems. |
| 5 Leading | Excellent completion that is equitable across student groups, with evidence that support interventions are evaluated. |
Evidence to look for
- Completion and retention statistics
- Student support service descriptions
- Evaluation reports
OUT.3 Access and value
Who can get in, what it costs, and whether outcomes justify the cost.
Is the program open and worth its price?
| Level | Description |
|---|---|
| 1 Absent | Fees are not transparent and there is no financial support. |
| 2 Emerging | Fees are published; scholarships are limited. |
| 3 Established | Transparent total cost, need- or merit-based support, and some measures to widen access. |
| 4 Advanced | Significant financial support, published diversity data, and outcomes that justify the cost. |
| 5 Leading | Strong access commitments with evidence of impact (diverse cohorts, support for under-represented groups) and excellent value relative to cost. |
By level of study
- Research doctorates (PhD): Read cost as funding: the share of candidates with full funding, and stipend levels relative to local living costs.
Evidence to look for
- Fee pages
- Scholarship and funding data
- Cohort diversity data
OUT.4 Learner and alumni voice
Validated feedback from current students and alumni, including on preparation for AI.
What do learners say, measured properly?
Planned from edition 2028.1. Not scored in the current edition; its weight is shared among the other indicators of the pillar.
| Level | Description |
|---|---|
| 1 Absent | No survey data. |
| 2 Emerging | Institutional satisfaction data without method. |
| 3 Established | Validated survey data with an adequate response rate, showing satisfaction around norms. |
| 4 Advanced | A RankingLab or validated national survey with good response and above-norm results. |
| 5 Leading | Consistently high results in validated surveys, including on AI preparedness, with response rates of at least 30%. |
Evidence to look for
- RankingLab learner and alumni surveys (planned)
- Validated national surveys
Reputation & Standing
How strong are the institution, its accreditation and its graduates?
REP.1 Institutional reputation
The standing of the awarding institution in established global university rankings, read as its best global position. Top 50 is 100 points, 51 to 100 is 75, 101 to 300 is 50, 301 to 1,000 is 25, and lower or unranked is 0. Computed, not scored by assessors.
How strong is the institution's standing worldwide?
Evidence to look for
- Global university rankings, cited by position and edition year
REP.2 Global leadership in the field
The position of the program or its school in established rankings of its field (subject rankings, and program rankings where they exist), read as the best global position. Top 25 is 100 points, 26 to 50 is 75, 51 to 100 is 50, 101 to 200 is 25, and lower or unranked is 0. Computed, not scored by assessors.
Is it among the leaders of its field worldwide?
Evidence to look for
- Subject and program rankings, cited by position and edition year
REP.3 Accreditation
Accreditation by the leading international accreditors of the field; for business, AACSB, EQUIS and AMBA. All three is 100 points, two is 75, one is 50, national accreditation only is 25, and none is 0. Computed, not scored by assessors.
Which leading accreditors have accredited it?
Evidence to look for
- The accreditors' own lists of accredited institutions and programs
REP.4 Alumni profile
The strength of the graduate body as a whole: the seniority and reach of alumni, and alumni prominent in the field. Judged on the alumni as a group; no individual is profiled or named. Until a researched check replaces it, an estimate by the research desk on the five levels (0 to 100 points), marked as estimated.
How strong and visible are its graduates as a group?
Evidence to look for
- Alumni outcome reports and published alumni statistics
- Public reporting on graduates in senior roles, in aggregate
External Reference
What do established, permissibly usable sources say?
EXT.1 External reference signal
A normalized 0 to 100 value from established sources that may lawfully be used: open-licensed data, data used with permission or under licence, or limited factual references consistent with the publisher's terms. Computed by the data team, not scored by assessors.
What do established, permissibly usable sources say?
Evidence to look for
- CWTS Leiden Ranking Open Edition (CC0)
- Licensed or permitted data, listed in the edition notes
Assessor toolkit
The same rubric drives these tools, so what assessors see is exactly what is published here.
- Scoring form: Offline form for assessors. Open the evidence file, score each indicator, export your scoring file.
- Evidence file template: Structure of the evidence file prepared for each entry.
- Evidence log (spreadsheet): Spreadsheet version of the evidence items, convertible into the evidence file.
- External signal template: How the data team records the capped external reference signal.