Policy Basis: Rector Regulation on AI, Data Governance, and Digital Services —
Applies to All Faculties and Administrative Units
GUIDANCE NOTE The university shows that digitised services are not isolated pilots but are governed by institutional policy and implemented across core functions (student services, academic administration, HR, finance, asset/facility operations). Integration and performance monitoring strengthen the evidence that technology enables efficiency and better decision-making.
1. Purpose
Misr University for Science & Technology (MUST) recognises that Artificial Intelligence (AI) and the Internet of Things (IoT) are reshaping higher education, research, and institutional management. This Policy governs the responsible, ethical, and strategically aligned adoption of advanced digital technologies to support decision-making, operational efficiency, and service delivery across all university administrative and academic processes.
2. Scope of Application
- All faculties, colleges, research centres.
- All administrative directorates: Finance, HR, Student Affairs, Facilities, IT, and Legal
- All affiliated hospitals, clinics, and community service
- All AI tools, digital platforms, IoT systems, automation software, and data analytics
- All members of the University Community: staff, researchers, students, contractors, and
3. Policy Objectives
- Establish a coherent governance framework for AI and digital technology adoption and
- Drive operational efficiency through digitisation of core administrative and academic
- Promote responsible AI use that is transparent, fair, and aligned with the University’s ethical values.
- Protect the academic integrity of teaching, learning, assessment, and
- Ensure compliance with Egyptian national legislation, accreditation standards, and international AI principles.
- Build AI and digital literacy capacity across the entire University
4. Governance Structure
4.1 University AI & Digital Transformation Committee (UADTC)
| Role | Key Responsibilities |
| Chair: Vice-Rector for Academic Affairs | Strategic oversight; annual reporting to Board of Trustees; final policy approval. |
| Chief Information Officer (CIO) | Lead digital transformation roadmap; oversee IT investments and vendor governance. |
| Director General of Educational Technology | Ethical review of AI deployments; maintain AI risk register; handle complaints. |
| Director General of Information Systems) | Ensure AI-related data processing complies with the Data Protection Policy. |
| Two Representatives from the Student Affairs Committee | Represent academic interests in teaching, research, and scholarly integrity. |
| Student Representative | Represent student interests; provide user-experience feedback on digital services. |
5. Digitisation of Core University Functions
| Function | Technologies Applied | Performance Monitoring |
| Student Services | AI chatbot; digital enrolment; automated scheduling; e-transcripts. | Satisfaction surveys; resolution-time KPIs; portal analytics. |
| Academic Administration | AI-enhanced LMS; adaptive learning; digital grade management; attendance tracking. | Learning outcome analytics; faculty adoption rates; integrity reports. |
| Human Resources | AI recruitment screening; digital onboarding; automated leave and payroll. | Time-to-hire; payroll accuracy; self-service adoption. |
| Finance & Procurement | Automated invoice processing; AI spend analytics; digital procurement workflow. | Processing time reduction; exception rates; budget variance. |
| Asset & Facility Operations | IoT smart building management; predictive maintenance; digital asset tracking. | Energy dashboards; maintenance response times; asset utilisation. |
| Research Support | AI-assisted literature review; HPC; research data management platform. | Research output metrics; repository usage; grant success rates. |
| Educationl Technology & Information Systems | E-learning platforms; Student Information System (SIS); digital faculty & student portals; adaptive assessment tools; LMS integration & support. | System uptime rate; digital platform adoption rate; technical response time; user satisfaction scores for digital services. |
6. AI in Teaching, Learning & Assessment
6.1 Student AI Use — Three-Category Framework
| Category | Permitted Use | Disclosure Requirement |
| AI-Permitted | AI tools may be used freely as productivity or brainstorming aid. | Acknowledge tool and purpose in submitted work. |
| AI-Assisted | AI may support research, drafting, or editing; core contribution must be the student’s own. | Mandatory AI contribution statement specifying AI-assisted portions. |
| AI-Prohibited | No AI use permitted; assessment tests unaided human capability. | Unauthorised AI use constitutes academic misconduct — see Article 9. |
7. AI in Research & Scholarly Activity
- AI tools may not be listed as authors or co-investigators on any research output or grant
- All AI-assisted content in publications or theses must be disclosed in a transparency
- Researchers are solely responsible for verifying the accuracy and validity of AI-generated
- Training AI models on MUST institutional or student data requires prior UADTC
- IP generated through AI-assisted University-funded research vests in MUST unless otherwise agreed.
8. Responsible AI Principles
| Principle | Institutional Commitment |
| Transparency | Users are informed when interacting with an AI system. Decisions are explainable in accessible terms. |
| Fairness & Non-Discrimination | AI systems must not perpetuate bias. Bias audits mandatory before deployment and after major updates. |
| Human Oversight | No AI system may make fully automated high-stakes decisions without meaningful human review and right of appeal. |
| Privacy by Design | AI systems use minimum necessary data. Privacy Impact Assessments required before processing personal data. |
| Safety & Security | AI systems tested for vulnerabilities before deployment. Security reviews repeat after major updates. |
| Accountability | Every AI system has a named institutional owner responsible for its performance and compliance. |
9. Academic Integrity & AI Misconduct
- Submitting AI-generated content as original work in any AI-Prohibited
- Failing to disclose AI assistance where disclosure is required under the AI-Assisted
- Using AI to fabricate, falsify, or misrepresent research data, citations, or
- Employing AI to bypass plagiarism detection or other integrity-verification
- Commissioning or sharing AI-generated work for another student’s benefit (contract cheating).
10. Data Governance for AI Systems
| Tier | Examples | AI Processing Requirements |
| Public | Published research, course catalogues | No additional restrictions. |
| Internal | Internal reports, staff directories | University-approved systems; vendor DPA required. |
| Confidential | Student records, HR data, research data | UADTC approval; encryption mandatory; no offshore processing without DPA. |
| Restricted | Medical records, biometric data | Board approval; on-premises processing only; annual security audit. |
11.Digital Transformation Strategic Roadmap
| Pillar | Focus | Key Initiatives 2025–2028 |
| 1 | Smart Campus | IoT infrastructure; smart energy; AI-driven facilities; digital access control. |
| 2 | Digital Learning | AI-enhanced LMS; adaptive learning; virtual labs; XR experiences; AI tutoring. |
| 3 | Research Excellence | HPC cluster; AI research hub; open-access data repository; collaboration platforms. |
| 4 | Administrative Efficiency | End-to-end digital workflows; AI admissions; automated finance; student chatbots. |
| 5 | Community Engagement | Digital alumni platform; AI career services; community health and legal AI tools. |
12. Training, Digital Literacy & Capacity Building
- All new staff must complete the MUST AI Literacy Induction within 30 days of
- All students must complete a foundational AI & Digital Literacy module in their first
- AI Coordinators and IT staff must obtain advanced certification within six months of role
- The Centre for Academic Development offers at least two AI-in-education workshops per
13. Compliance & Enforcement
| Stakeholder | Minor Breach | Serious / Repeated Breach |
| Students | Formal warning; remedial training. | Grade penalty; suspension; or expulsion. |
| Faculty / Academic Staff | Written warning; mandatory retraining. | Formal disciplinary proceedings; up to dismissal. |
| Administrative Staff | Supervisory reprimand; retraining order. | HR disciplinary process; termination. |
