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Title
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Confidence Through Customisation: AI-Generated Differentiation in a Year 10 Girls’ English Classroom
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Author
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Jemma Cattell
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Year Published
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2026
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Description
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This action research study investigated whether artificial intelligence (AI) could effectively support readiness-based differentiation in a Year 10 girls’ English classroom, and how this influenced girls’ confidence and engagement. A class of 21 students participated in a seven-week unit in which AI-generated differentiated worksheets were embedded across non-fiction writing tasks. Students were grouped according to learning needs using cumulative reading and writing data, and for each text type, AI was used to produce tiered scaffolds tailored to differing levels of cognitive demand. Data collection techniques included confidence surveys at multiple intervals, weekly reflection journals, semi-structured interviews, classroom observations, teacher aide notes, and analysis of student writing artefacts. Thematic analysis was employed to interpret the data. Findings suggest that AI-generated tasks were able to differentiate appropriately, enabling students to access learning at an optimal level of challenge. Reduced procedural uncertainty enabled more targeted feedback and relational interaction, strengthening confidence and academic risk-taking. The intervention fostered a classroom culture characterised by collective perseverance and shared assurance. However, the effectiveness of AI-supported differentiation depended on deliberate teacher mediation and iterative refinement. The findings from this study may be valuable for educators seeking approaches to differentiation and exploring how emerging technologies can support inclusive pedagogy in girls’ schools without displacing professional expertise.
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Tags
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Technology, Teaching and Learning
