The AI Readiness Assessment and Sandbox Initiative (use of AI tools in safe controlled environment) at the University of Jordan is designed as a structured, evidence-based activity to evaluate the Faculty of Educational Sciences preparedness for responsible AI adoption while generating actionable insights for implementation and innovation. Within this initiative, the AI Research & Assessment Consultant for the AI Readiness Assessment Stream is responsible for leading the design of data collection tools and methodologies, and the analysis of quantitative and qualitative data, to assess level of maturity across eight core domains:

Strategic Positioning & AI Vision (D1)
Governance, Policy & Risk (D2)
Human Capital & Culture (D3)
Teaching, Learning & Assessment (D4)
Research & Innovation (D5)
Infrastructure & Data (D6)
Operations & Efficiency (D7)
Ecosystem & Sustainability (D8)
He/She will also define clear sandbox success indicators—such as learning improvement, time saved, teaching quality, and student engagement—and develop tools to systematically measure and support evidence-based adoption.

The role focuses on operationalizing these domains into measurable indicators and tools, ensuring that all data collection instruments are clearly mapped to the domains and generate actionable insights. The Consultant designs and implements data collection tools, including diagnostic assessments, key informant interview (KII) guides and questions, surveys, and scoring rubrics, which are used to collect both quantitative and qualitative data. These data are then systematically analyzed and triangulated across domains to produce a comprehensive AI readiness profile. The Consultant leads the analysis, synthesis, and interpretation of findings to generate domain-level maturity scores, identify gaps, and develop evidence-based recommendations, requiring strong expertise in survey design, quantitative and qualitative analysis, and ideally higher education systems. He/She will also develop appropriate data collection tools to systematically measure and analyze these dimensions, ensuring that evidence generated from the sandbox informs both pedagogical refinement and institutional effectiveness.

Key Responsibilities:

1. Tools Design & Methodology

Design and develop research methodology data collection tools aligned to the eight domains (D1–D8), including:
Key informant interview guides and forms
Survey instruments
AI and technical Diagnostic tests
Focus group questions for targeted stakeholders based on stakeholders’ mapping
Ensure all tools are explicitly mapped to domain indicators
Define data collection protocols and methodologies
Deliverables:

Validated data collection tools by Strategy Committee and UJ Steering committee
Domain-aligned indicators and scoring criteria
Data collection protocols
2. Data Collection

Implement data collection activities along with data collection team across stakeholder groups, including leadership, faculty, administrative staff, IT teams, and students
Conduct key informant interviews and surveys
Ensure coverage across all eight domains
Deliverables:

Completed datasets (quantitative and qualitative)
Interview summaries and transcripts
Survey datasets
3. Analysis & Synthesis

Analyze quantitative data (e.g., survey results, scoring matrices)
Analyze qualitative data (e.g., interviews, open-ended responses, document review)
Triangulate findings across methods and domains (D1–D8)
Produce: Domain-level maturity assessments, Cross-domain comparisons, and gap analysis and priority areas
Deliverables:

Maturity profiles by domain
Analytical outputs (heat maps, scoring tables)
Gap analysis and prioritized recommendations
4. Reporting & Documentation

Lead drafting of the findings section of the AI Readiness Diagnostic Report, structured around the eight domains
Develop executive summaries and presentations for leadership
Provide analytical inputs to AI sandbox design and use case prioritization
Deliverables:

Final AI Readiness Diagnostic Report Findings
Executive presentations
Inputs for sandbox design and roadmap
Qualifications

Professional fluency in English and Arabic (written and spoken)
Advanced degree (Master’s or higher) in Education, Public Policy, Social Sciences, Data Analytics, or a related field
Demonstrated experience in research design, data collection, and mixed-methods analysis
Proven experience in developing assessment tools, surveys, and diagnostic frameworks in education or institutional contexts
Familiarity with higher education systems
Exposure to digital transformation, AI in education, or innovation-related projects is highly desirable
Competencies

Strong quantitative analysis skills (survey design, statistical interpretation, data analysis tools)
Strong qualitative research skills (interview design, thematic analysis, data triangulation)
Ability to translate complex data into clear, actionable insights and recommendations
High-level analytical thinking and problem-solving skills
Strong communication and reporting skills, including the ability to produce professional technical reports and executive summaries
Stakeholder engagement and facilitation skills, including conducting interviews, workshops, and validation sessions
Ability to work collaboratively with multidisciplinary teams and align technical findings with strategic decision-making
Attention to detail and ability to manage complex datasets across multiple domains

Please send your resume/CV