From marked scripts to diagnostic insight: a viewpoint on AI-assisted assessment in paper-based primary classrooms

Purpose This Viewpoint examines how AI-assisted dashboards might support teachers' diagnostic interpretation of paper-based primary assessment evidence, particularly in Caribbean and other low-resource contexts. It argues that AI's value in classroom assessment lies less in automating judgment than in strengthening teachers' responsible interpretation of evidence. Design/methodology/approach The article uses a conceptual AI-assisted assessment dashboard, developed using simulated data from 22 fictional students, as an illustrative case. The prototype combined optical character recognition (OCR), structured data extraction, and teacher-facing visualizations to illustrate how marked paper scripts may be transformed into diagnostic outputs. Findings The prototype illustrates that traditional summative marking can be reorganized into descriptive information that supports formative interpretation. It also highlights the importance of human oversight, transparent descriptive outputs, privacy-by-design and contextual fit when applying AI and OCR to classroom assessment. Research limitations/implications As the prototype used simulated data, future research should validate the workflow with authentic scripts under a fixed, fully reported technical configuration, with error rates, timed comparisons against manual entry, teacher and student perspectives and classroom implementation. Social implications By extending the formative value of routine summative assessments, AI-supported assessment tools should be designed as lightweight, human-in-the-loop decision-support systems that reduce clerical burden, preserve teacher agency and operate within clear data-governance procedures. Originality/value The article contributes a contextually grounded Viewpoint on AI-assisted assessment in paper-based primary settings. It shifts attention from automation and prediction to diagnostic visibility, teacher judgment and ethically restrained innovation in low-resource educational contexts.