Oyinkansola Onwuchekwa

Oyinkansola Onwuchekwa is an AI Research Engineer and PhD Researcher in Data Science and Artificial Intelligence at the University of Hull. Her work sits at the intersection of multilingual natural language processing, low-resource African language technologies, responsible AI, digital humanities, and applied AI systems for research, education and decision-making.

Her doctoral research investigates emotional expression in contemporary global music lyrics, with a focus on code-mixed Afrobeats lyrics and African language contexts. She develops NLP methods for culturally grounded emotion modelling, including token-level language identification, retrieval-augmented classification, annotation design, transformer-based modelling, error analysis and provenance-aware evaluation. Her research addresses the ways standard multilingual models fail when language is code-mixed, culturally specific, informal, musical or underrepresented in mainstream datasets.

Alongside her PhD, Oyinkansola builds applied AI tools and research software. Her projects include SAGE-AI, a modular agentic prototype for systematic review workflows; a reflective multi-agent LLM tutor for AI education research; public low-resource NLP tools for African language processing; coding-agent evaluation datasets and monitor harnesses; and evidence-focused automation workflows for public-sector research and evaluation. She is particularly interested in systems that preserve audit trails, support human review, make model limitations visible and produce outputs that researchers and practitioners can inspect.

Her applied work spans healthcare, education, workforce evaluation, cultural heritage and responsible AI. At NHS England, she has supported reproducible evidence-review workflows, structured datasets, source logs, review protocols and Power BI-ready outputs for workforce, leadership and evaluation work. She has also contributed to British Academy and UNESCO-linked work on AI literacy, community-led data governance, intangible cultural heritage and locally responsible AI frameworks.

Oyinkansola teaches and supports learning in machine learning, natural language processing, deep learning, Python programming and applied AI. She has taught laboratory sessions to MSc students and is interested in how AI systems can support learning without replacing critical thinking. Her wider interests include agentic AI, RAG, coding-agent evaluation, AI governance, human-in-the-loop systems, digital research infrastructure and culturally aware AI evaluation.

She is an Associate Fellow of Advance HE, a Professional Member of BCS, a member of EPSRC and NERC UKRI peer review colleges, and a reviewer for venues including ICLR and The Deep Learning Indaba.

She has presented and spoken on multilingual AI, responsible AI, digital humanities and culturally intelligent AI systems through academic conferences, invited talks and public engagement events.