EDAIL-EduAI-NG: Benchmarking Educator AI Readiness for Scalable Deployment in Low-Resource Classrooms

Oluwakemi D. Olurinola, Sakinat Folorunso, Patrick Owor
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:37-50, 2026.

Abstract

Artificial Intelligence (AI) is increasingly accepted as a change agent in education, but scale-up in resource-scarce contexts is hindered by lack of educator readiness and the absence of longitudinal FAIR-aligned benchmarks. This paper presents EduAI-NG, an open-source, FAIR-aligned longitudinal dataset from the EDAIL (Educators’ AI Literacy) programme in Nigeria. The dataset consists of 2,239 pre-training and 1,068 post-training records, along with a matched dataset of 770 educators. A composite Teacher AI Deployment Readiness Index (TADRI-lite) is constructed with lightweight machine learning baselines to validate predictive utility. Results show substantial improvements following the intervention: mean AI understanding increased from 2.80 to 3.93 (Cohen’s $d = 0.852$, $p < 0.001$), while the composite readiness index demonstrated a very large effect ($d = 1.29$) with excellent internal consistency (Cronbach’s $\alpha = 0.93$). Context-aware professional development has significant potential to affect educator AI readiness in the Global South.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-olurinola26a, title = {{EDAIL-EduAI-NG}: Benchmarking Educator {AI} Readiness for Scalable Deployment in Low-Resource Classrooms}, author = {Olurinola, Oluwakemi D. and Folorunso, Sakinat and Owor, Patrick}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {37--50}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/olurinola26a/olurinola26a.pdf}, url = {https://proceedings.mlr.press/v319/olurinola26a.html}, abstract = {Artificial Intelligence (AI) is increasingly accepted as a change agent in education, but scale-up in resource-scarce contexts is hindered by lack of educator readiness and the absence of longitudinal FAIR-aligned benchmarks. This paper presents EduAI-NG, an open-source, FAIR-aligned longitudinal dataset from the EDAIL (Educators’ AI Literacy) programme in Nigeria. The dataset consists of 2,239 pre-training and 1,068 post-training records, along with a matched dataset of 770 educators. A composite Teacher AI Deployment Readiness Index (TADRI-lite) is constructed with lightweight machine learning baselines to validate predictive utility. Results show substantial improvements following the intervention: mean AI understanding increased from 2.80 to 3.93 (Cohen’s $d = 0.852$, $p < 0.001$), while the composite readiness index demonstrated a very large effect ($d = 1.29$) with excellent internal consistency (Cronbach’s $\alpha = 0.93$). Context-aware professional development has significant potential to affect educator AI readiness in the Global South.} }
Endnote
%0 Conference Paper %T EDAIL-EduAI-NG: Benchmarking Educator AI Readiness for Scalable Deployment in Low-Resource Classrooms %A Oluwakemi D. Olurinola %A Sakinat Folorunso %A Patrick Owor %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-olurinola26a %I PMLR %P 37--50 %U https://proceedings.mlr.press/v319/olurinola26a.html %V 319 %X Artificial Intelligence (AI) is increasingly accepted as a change agent in education, but scale-up in resource-scarce contexts is hindered by lack of educator readiness and the absence of longitudinal FAIR-aligned benchmarks. This paper presents EduAI-NG, an open-source, FAIR-aligned longitudinal dataset from the EDAIL (Educators’ AI Literacy) programme in Nigeria. The dataset consists of 2,239 pre-training and 1,068 post-training records, along with a matched dataset of 770 educators. A composite Teacher AI Deployment Readiness Index (TADRI-lite) is constructed with lightweight machine learning baselines to validate predictive utility. Results show substantial improvements following the intervention: mean AI understanding increased from 2.80 to 3.93 (Cohen’s $d = 0.852$, $p < 0.001$), while the composite readiness index demonstrated a very large effect ($d = 1.29$) with excellent internal consistency (Cronbach’s $\alpha = 0.93$). Context-aware professional development has significant potential to affect educator AI readiness in the Global South.
APA
Olurinola, O.D., Folorunso, S. & Owor, P.. (2026). EDAIL-EduAI-NG: Benchmarking Educator AI Readiness for Scalable Deployment in Low-Resource Classrooms. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:37-50 Available from https://proceedings.mlr.press/v319/olurinola26a.html.

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