AI Capability Framework

AI Capability Framework

An open source framework helping universities to assess AI maturity, identify strengths, gaps, and improvement priorities.

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GOVERNANCE & HUMAN COMMITMENT OUTREACH & OPERATIONAL EFFICIENCY TEACHING, LEARNING & ASSESSMENT RESEARCH & SCHOLARSHIP
Regulatory & Ethical Standards
Governance & Risk Management
Code of Conduct & Privacy
Leadership & Capability
AI Enhanced Recruitment
Personalised Student Support
Faculty & Administr. Efficiency
External Engmt. & Partnership
Course Design & Curriculum
Personalised Learning & Support
Assessment, Grading & Feedback
AI in Research Practice
Scholarship of AI in Practice
AI Research

1.1.1

Monitoring Adherence

1.2.1

Governanance Structure

1.3.1

Code of Conduct

1.4.1

Leadership & Planning

2.1.1

Application Processes

2.2.1

Career Services

2.3.1

AI in Administrative Processes

2.4.1

Community Learning

3.1.1

Agreed Approach for AI

3.2.1

Personalised Learning

3.3.1

Realtime Feedback

4.1.1

AI Tools for Research

4.2.1

Scholarship of AI

4.3.1

Research in AI Field

1.1.2

Ethical Guidelines

1.2.2

Risk Assessment

1.3.2

Data Protection

1.4.2

Training & Development

2.1.2

Recruitment & Conversion

2.2.2

Realtime Student Support

2.4.2

External Collaboration

3.1.2

Curriculum Design

3.2.2

Student Retention

3.3.2

Adaptive Assessment

4.1.2

AI in Research Practice

4.3.2

AI Impact in Disciplines

1.1.3

Sustainability Impact

1.2.3

Ethical Procurement

2.4.3

Industry Projects

3.1.3

AI Literacy

3.2.3

Academic Support

3.3.3

Bias Validation

3.1.4

AI Courses

Purpose

The QS AI Capability Framework is designed to assist higher education institutions to evaluate and enhance their artificial intelligence (AI) capabilities. The framework provides a structured approach for universities to assess their current AI integration, identify areas for improvement, and make informed strategic decisions. The framework is built upon four key pillars, segmented into 14 Indicators, and encompassing over 30 sub-indicators to assess institutional capabilities.

Methodology & Guiding Principles

The framework was developed in collaboration with AI experts, industry and academic stakeholders to ensure relevance, rigor, and adaptability. It is structured around four key institutional functions: Teaching, Learning, and Assessment; Research and Scholarship; Governance and Human Commitment; and, Operational Efficiency. The framework provides a systematic lens for assessing institutional capabilities in AI integration and a tool for measuring progress over time.

Institutional Assessment

Using the QS AI Capability Framework, the assessment process involves data collection completed by the institution and supported by evidence. To ensure a holistic perspective, the assessment is complemented by faculty and student surveys to capture diverse stakeholder insights. Submissions and survey results are analysed and points allocated at each sub-indicator level, ultimately resulting in a comprehensive evaluation of the institutions’ strengths and areas for strategic improvement.

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