A Documentary Evaluation of Offline AI Integration in Teacher-in-a-Box OER Programmes for Low-Connectivity Schools

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Purpose of the study: To conduct a documentary, ex ante programme evaluation of how a bounded offline AI augmentation could be specified, governed and later tested within an offline OER teacher development programme delivered through Teacher in a Box. Methodology: Documentary programme evaluation using a thesis corpus. Methods: Theory of Change reconstruction, CIPP evaluation framework, directed qualitative content analysis with a scope-aligned codebook, and construct-to-instrument mapping for assessment tools. Offline AI specification is GPT4All class local LLMs plus retrieval-augmented generation (RAG) over the local Teacher in a Box OER and training repository, with governance wrappers. Main Findings: Offline OER effectiveness depends on integrated, in-situ teacher support rather than access alone. Offline AI is most defensible as a governed, retrieval-grounded, teacher-facing layer that improves resource discovery, supports formative assessment routines, and scaffolds assessment literacy. The study outputs measurable CIPP indicators, an evidence-to-implication warrant structure, a baseline-versus-augmentation risk and safeguard matrix, and offline-feasible instruments for formative assessment, summative task quality, character education evidence, and deeper learning portfolios. Novelty/Originality of this study: This study provides an audit-ready, evaluation methodology linking offline OER programmes to offline AI. It advances knowledge by separating evidence, implication, and design claims; specifying a minimum viable governance package; and producing offline-feasible assessment instruments and indicators for formative, summative, character, and deeper learning assessment under low-connectivity conditions, supporting policy and implementation decisions before field trials.

This publication has 25 references indexed in Scilit: