Hope Oluwaseun Adegoke
2025
Augmented Measurement Framework for Dynamic Validity and Reciprocal Human-AI Collaboration in Assessment
Taiwo Feyijimi | Daniel O Oyeniran | Oukayode Apata | Henry Sanmi Makinde | Hope Oluwaseun Adegoke | John Ajamobe | Justice Dadzie
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
Taiwo Feyijimi | Daniel O Oyeniran | Oukayode Apata | Henry Sanmi Makinde | Hope Oluwaseun Adegoke | John Ajamobe | Justice Dadzie
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
The proliferation of Generative Artificial Intelligence presents unprecedented opportunities and profound challenges for educational measurement. This study introduces the Augmented Measurement Framework grounded in four core principles. The paper discussed practical applications, implications for professional development and policy, and charts a research agenda for advancing this framework in educational measurement.
Medical Item Difficulty Prediction Using Machine Learning
Hope Oluwaseun Adegoke | Ying Du | Andrew Dwyer
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
Hope Oluwaseun Adegoke | Ying Du | Andrew Dwyer
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
This project aims to use machine learning models to predict a medical exam item difficulty by combining item metadata, linguistic features, word embeddings, and semantic similarity measures with a sample size of 1000 items. The goal is to improve the accuracy of difficulty prediction in medical assessment.