Dr Shabbar Naqvi

Profile

I am a Teaching Fellow in Artificial Intelligence in the School of Computer Science at the University of Leeds. I hold a PhD in Computer Science from the University of Nottingham and a Fellowship of the Advance HE (FHEA), and completed the Postgraduate Certificate in Academic Practice (PGCAP) at the University of Leeds with Distinction.

Before joining Leeds, I held senior academic and leadership roles at Balochistan University of Engineering and Technology, Pakistan, including Associate Professor, Head of Department, Director of Postgraduate Studies, and Dean of the Faculty of Sciences. I led postgraduate programme development, faculty-wide curriculum reform, and engineering accreditation under the Washington Accord.

At Leeds, I teach on the MSc Artificial Intelligence Online Distance Learning programme, supervise postgraduate and undergraduate research projects, and serve as Academic Personal Tutor to students across multiple intake cohorts. I also serve on the Advisory Board of the University's eXplainable Artificial Intelligence (XAI) Hub.

Research interests

My research sits at the intersection of artificial intelligence, assessment design, and pedagogical practice in online higher education. My central scholarly question is how assessment can be designed to develop rather than circumvent student capability in AI-rich learning environments, a question with implications for curriculum design, academic integrity, and AI policy in higher education.

My work spans three interconnected strands. The first examines the reliability of AI-based grading systems, producing a co-authored study published in Springer Nature's Lecture Notes in Artificial Intelligence (Ruddle & Naqvi, 2026). The second investigates student comprehension of AI disclosure policy in online postgraduate education, with findings disseminated at the University of Leeds Student Education Conference and an open-access pedagogical resource available via Zenodo (Nerantzi et al., 2025) and reflected upon in a published piece on the Knowledge Equity Network (KEN) (Naqvi & Wray, 2026). The third explores the application of Multi-Criteria Decision Analysis (MCDA) and Explainable AI frameworks to the evaluation of educational and clinical AI.

I also hold an earlier research background in fuzzy logic and type-2 fuzzy systems. Collaborative enquiries from researchers working in AI education, assessment integrity, or explainable AI are welcome.

Qualifications

• PhD (Computer Science), University of Nottingham, UK
• ME (Computer Systems Engineering), NED University of Engineering and Technology Karachi, Pakistan
• BE (Computer Systems Engineering), NED University of Engineering and Technology Karachi, Pakistan
• Postgraduate Certificate in Academic Practice (PGCAP), University of Leeds, UK

Professional memberships

• Fellow Advance HE (FHEA)
• Member of British Computer Society (MBCS)
• Member of Pakistan Engineering Council as Professional Engineer (P.E)

Student education

I teach across the MSc Artificial Intelligence Online Distance Learning programme, with a particular focus on the theoretical foundations of AI, including formal logic, symbolic reasoning, probabilistic methods, and machine learning, and their application to real-world problems.

I work with students who are predominantly working professionals studying alongside full-time employment, often from international and varied disciplinary backgrounds. My approach to teaching and personal tutoring reflects this context, structured, accessible, and responsive to the practical constraints of distance learners, with support available across time zones and professional schedules.

I supervise postgraduate research projects in AI and related areas and contributes to interdisciplinary project supervision in collaboration with other Schools, including undergraduate Mechatronics and Robotics projects with the School of Mechanical Engineering, integrating AI methods with applied robotic system design.

My own scholarly work on AI policy in education, assessment design, and the reliability of automated grading directly informs my teaching practice, meaning students engage with questions that are both academically grounded and practically current.

I am committed to inclusive and accessible learning, ensuring that online resources are designed for a diverse international cohort and that all students have equitable access to the support they need to succeed.