Professional development of University teachers for GenAI competencies

Summary
Generative Artificial Intelligence (GenAI) has reshaped the Higher Education landscape since late 2022. While early institutional responses focused on restrictive bans, Higher Education leaders now recognise that GenAI adoption is fundamentally student-driven. To ensure meaningful, ethical, and effective integration, universities must transition from ad-hoc, supply-driven training workshops to educator-driven, continuous development pathways.

Presented at Bett Asia 2026, this session overview examines a validated four-dimension GenAI Competency Framework, a diagnostic self-assessment tool, and operational case studies from Universiti Putra Malaysia (UPM) and Universiti Malaya (UM). Together, these insights demonstrate how Higher Education systems can scale individual digital literacy into broad institutional capability.

Key Takeaways
Student-Driven Adoption: Students utilise GenAI regardless of institutional readiness; static curricula and traditional assessments must adapt to maintain academic integrity and relevance.

Needs-Driven Professional Learning: Effective professional development relies on educator agency and diagnostic self-assessment rather than generic, top-down training modules.

The Four-Dimension Framework: GenAI competency encompasses GenAI Literacy, Curriculum and Learning Design, Teaching and Learning Activities, and Assessment, evaluated across teacher and student perspectives.

Capacity Over Competency: Institutional transformation requires shifting from isolated individual skills to continuous learning cycles and discipline-specific applications.

Policy and Transparency: Clear governance frameworks, explicit declaration levels, and robust ethical guidelines are essential prerequisites for classroom implementation.


The GenAI Imperative: Moving Beyond Institutional Prohibition
When ChatGPT emerged in late 2022, many Higher Education institutions globally reacted by banning generative tools during early 2023. However, policy leaders quickly realised that GenAI differs fundamentally from previous educational technologies. Traditional edtech is typically selected and implemented by university administrators; GenAI, by contrast, is adopted bottom-up by students.

Because students integrate GenAI into their research and writing independently, maintaining static assessment tasks and traditional teaching methods creates significant vulnerabilities. Rather than attempting to enforce unenforceable bans, national bodies—such as the Malaysian Ministry of Higher Education—became early adopters of comprehensive guidelines for ethical AI usage.

To support educators in navigating this environment, university professional development must move away from supply-driven models, where teaching and learning centres offer generic, one-size-fits-all courses. Instead, Higher Education requires a needs-driven approach that grants educators the agency to diagnose their specific skill gaps and direct their own professional growth.


The Four-Dimension GenAI Competency Framework
To address the limitations of existing models—such as TPACK or older iterations of the UNESCO ICT Competency Framework for Teachers, which often retrofit AI onto pre-digital structures—an international research team developed a dedicated GenAI Competency Framework. Published in Computers and Education:
Artificial Intelligence, the framework establishes a structured matrix for Higher Education:

Core Framework Architecture
Four Operational Dimensions:
 

• GenAI Literacy: Understanding fundamental principles, limitations, and ethical considerations.
• Curriculum and Learning Design: Structuring courses and learning outcomes that reflect AI-augmented environments.
• Teaching and Learning Activities: Designing scaffolded, interactive learning experiences.
• Assessment: Formulating authentic assessment tasks that address AI risks while leveraging learning opportunities.
• Dual Perspectives: Evaluating both the educator's personal usage and literacy, alongside the educator's capacity to empower student literacy within each dimension.
• Three Proficiency Levels: Basic, Intermediate, and Advanced skill descriptors with tangible evaluation measures.

Diagnostic Self-Assessment and Personalised Learning
To translate theoretical frameworks into practical utility, the research team created an item bank of 96 indicators, distilled into a validated 36-item self-assessment questionnaire. Tested across universities in 10 countries across Asia and Africa with over 800 respondents, the diagnostic tool evaluates educator readiness across all four dimensions.

Upon completing the online survey, educators receive an automated diagnostic report detailing their strengths and skill gaps. The tool connects directly to a digital platform offering tailored professional development resources. Currently available in English, with Chinese and Arabic versions in progress, the platform fosters self-directed professional learning rather than mandated compliance.

Scaling Competency into Institutional Capacity: The UPM Model
Translating individual teacher skills into system-wide capability requires an institutional framework. As highlighted by Universiti Putra Malaysia (UPM)—home to the UNESCO International Institute of Online Education (IIOE) National Centre in Malaysia—isolated competency amongst enthusiastic individuals does not automatically create institutional strength.

UPM advocates for a continuous development cycle: Assess → Develop → Apply → Reflect → Progress → Scale. Key strategic shifts in this model include:

 

• From One-Off Workshops to Continuous Pathways: Replacing single seminars with structured, progressive learning journeys.
• From Generic AI Skills to Discipline-Specific Applications: Contextualising GenAI within specialised fields, such as UPM's AI and Agritech micro-certification programme.
• From Individual Capability to Institutional Ecosystems: Embedding AI competencies into organisational policies, administrative workflows, and collaborative networks involving UNESCO-ICHE and international partners.

Policy, Governance, and Pedagogical Practice: The Universiti Malaya Case
At the institutional level, Universiti Malaya (UM) established a formal Academic AI Policy in 2025, providing unambiguous guidelines for academic staff and students across all study levels.

Transparency and Declaration Protocols
The UM policy emphasises mandatory transparency. Lecturers must disclose how GenAI is incorporated into course materials and assessments, providing clear written instructions regarding permissible usage levels (Level 0 through Level 3). Concurrently, students are required to submit self-declarations detailing their GenAI involvement in assignments, dissertations, and oral presentations. Crucially, institutional guidelines prohibit uploading entire student assignments into public AI tools to safeguard data privacy and intellectual property.

Classroom Application and Community Impact
To build staff capacity, UM implemented the ARISE programme (Readiness, Integration, and Strategy for Educators), guiding lecturers through basic AI foundations to advanced co-creation. In practice, educators are leveraging GenAI for authentic learning. For example, within an Economics of Education course, students used Claude AI to construct interactive games illustrating cost-effectiveness analysis. This academic exercise was subsequently expanded into a community outreach initiative for secondary school students in Ranau, Sabah, illustrating a complete cycle of pedagogical innovation.


Building a Sustainable AI Ecosystem
As artificial intelligence continues to advance rapidly toward super-intelligence, Higher Education institutions cannot rely on reactive policy adjustments or piecemeal training. Sustainable adaptation demands an integrated ecosystem encompassing robust infrastructure, clear governance, strategic industry partnerships, and educator agency. By combining diagnostic self-assessment with continuous, discipline-specific development, universities can build lasting capacity that empowers both educators and students in an AI-augmented world.

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