Figure 4.
The elements and levels are as follows. Cross-cutting elements. Cultural factors: faculty attitudes, student expectations, disciplinary traditions, institutional culture. Quality assurance: learning outcomes, professional standards, industry alignment, continuous improvement. Technological readiness: support systems, tool accessibility, user competencies, and infrastructure capabilities. Implementation level, micro. Teaching approaches identified from ethnographic: blended learning methods, hybrid examination approaches, and case study-based resistance strategies. Assessment methods: A I inclusive assessment design, real-time evaluation, project-based assessment, authentic tasks, and continuous assessment. Student support: A I tool guidance, ethical use training, technical support, and learning assistance. Disciplinary level, meso. Context-specific adaptation: technical competency mapping, industry requirements integration, professional standard alignment, and discipline-specific A I integration. Assessment Design: A I-aware assessment strategies, authentication methods, competency verification approaches, and professional skills evaluation. Curriculum development: A I literacy integration, technical skills development, professional competency focus, and industry-relevant content. Institutional level, macro. Strategic planning: long-term integration strategies, change management, industry alignment, and risk management. Infrastructure and support: technical infrastructure, faculty development, support services, and digital readiness assessment. Policy development and governance: quality assurance mechanisms, resource allocation frameworks, academic integrity policies, and A I usage guidelines.Multilevel framework for AI integration in higher technical and engineering education
Source: Authors’ own work