Theme 1
Responsible & Culturally Attuned AI
Designing AI systems that work responsibly within the social, cultural and infrastructural realities of the people they serve.
Research agenda
How can computing systems work responsibly within the cultures, institutions and infrastructure of the people they serve?
Questions and themes
Safe Drive Africa is the flagship programme within a wider agenda of responsible, culturally aware and practical computing research.
Designing AI systems that work responsibly within the social, cultural and infrastructural realities of the people they serve.
NLG, personalised feedback and behaviour-change interventions that are explainable, supportive and context-aware.
Smartphone telematics, unsafe-driving analytics, alcohol-influence detection and scalable fleet/road-safety applications.
Offline-first, smartphone and sensor systems for low-resource and intermittently connected environments.
Human-centred mobile systems and context-aware digital health interfaces informed by ASICA project experience.
Computing systems that can improve transparency, project validation, public information and service accountability while protecting privacy.
Ontology, structured data and information-retrieval research carried forward from earlier academic work.
An umbrella for agriculture, infrastructure, education, public services and locally appropriate digital innovation.
Philosophy: Build. Deploy. Measure. I specialize in creating AI that survives the "wild." My work focuses on building mobile systems for low-resource environments, collecting original datasets where none exist, and using Generative AI to turn telematics into meaningful, culturally appropriate feedback. I believe in AI that doesn't just compute, but communicates—prioritizing safety by delivering insights after the journey, not during the drive.
Current focus
Currently focused on transitioning a validated smartphone-based telematics prototype into an institutionally adopted road safety tool for West Africa, featuring AI-driven driving feedback, alcohol detection, and hijack detection for fleet operations.
Structured programmes
Longer-running bodies of work that bring questions, methods, evidence, partners and routes to adoption together.
Evidence base
Selected scholarly outputs behind the research agenda and its applied programmes.
Iniakpokeikiye Peter Thompson, Jawwad Baig, Ehud Reiter, Dewei Yi · 2026 · International Natural Language Generation (INLG) 2026 conference
Jimoh Yusuf Omogbera (Department of Computer Science, Edo State University Iyamho) Taibat Onome Asunogie (Department of Computer Science, Edo State University Iyamho) Iniakpokeikiye Peter Thompson (Department of Computer Science, Niger Delta University) · 2026 · International Journal of Research and Scientific Innovation (IJRSI)
Thompson, I. P. · 2025 · University of Aberdeen
Thompson, I. P., Yi, D., & Reiter, E. · 2025 · 11th International Conference on Computer and Communications, IEEE
Thompson, I. P., Yi, D., & Reiter, E. · 2025 · 30th International Conference on Automation and Computing (ICAC), Loughborough, UK
Applied work
Projects turn focused research questions into prototypes, systems and evidence that others can inspect and use.
Transitioning a validated smartphone-based telematics prototype into a widely adopted safety tool for fleet operations in West and Central Africa.
Learn moreAn open-source Android app for collecting real-time driving and sensor data in Nigerian conditions.
Learn moreAn end-to-end NLG pipeline that generates culturally sensitive post-trip driving feedback for Nigerian drivers.
Learn moreA decision-tree classifier detecting alcohol-influenced driving from smartphone sensor data collected in Nigeria.
Learn more