AI IN EDUCATION

My work examines how artificial intelligence can strengthen teaching, learning and academic administration without weakening academic integrity, privacy, fairness or human judgement.

CORE PRINCIPLES

• Human oversight and educator accountability

• Transparent and ethical AI use

• Assessment redesign for authentic learning

• Evidence verification and critical thinking

• Equity, accessibility and data protection

AI-COMPATIBLE & HUMAN-ASSURED ASSESSMENT 

Generative Artificial Intelligence has shifted the central assessment question from whether students used AI to whether the institution has sufficient appropriate evidence of individual learning, professional judgement and capability. My approach preserves authentic professional tasks while strengthening transparency, process evidence, authoritative-source verification, reproducible analysis, reflection, oral verification and secure individual corroboration. 

Six-Stage Assessment Transformation Method 

Preserve the authentic professional task → Diagnose AI-substitution and evidence risks → Classify Open and Secure components → Add proportionate process evidence → Verify material claims → Corroborate individual understanding. 

SELECTED WORK

• Next-Generation AI-Compatible, Human-Assured Assessment Design

• MP212 AI-Compatible Assessment Transformation Exemplar

• AI Roadmap for Academic

• Empowering Educators with AI: Practical Frameworks for Teaching, Assessment and Academic Integrity

• Transforming Education: Nurturing an AI-Ready Culture in Academic Institutions

• Introduction to Generative AI

• Innovation in Education

The objective is not simply to adopt new technology. It is to build institutional capability, strengthen assessment assurance, improve learning quality and ensure that Artificial Intelligence supports—rather than replaces—professional and academic judgement.