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AI-Powered Personalised Learning: Enhancing ESOL and Remedial English Education

Sixth Form
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Teaching & Inclusive Practices
Key Stage 2
Key Stage 3
Key Stage 4
Key Stage 5
Modern Languages
Case Study
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Pippa Sutcliffe

Deputy Headteacher, Windlesham

In a mixed-ability ESOL and extra English classroom, AI was used to personalise learning experiences. By translating challenging words for ESOL students and providing definitions for native English speakers, coupled with progressively challenging questions, the AI system allowed for tailored assessments and subsequent interventions. This method enabled the teacher to address specific areas of improvement and study comprehension patterns over time.

Teaching a varied group of ESOL and remedial English students once a week posed challenges in ensuring each student's unique needs were met. To bridge the differentiation gap, a consistent extract was provided to all students. However, for ESOL students, AI highlighted and translated difficult words, whereas native English speakers received definitions.

The students were then assessed through progressively difficult questions in a Google form. These questions ranged from basic understanding and word-level comprehension to more complex tasks like information retrieval, inference, deduction, and summarising. Based on the data from these responses, the AI system generated new questions targeting each student's specific areas of need. This iterative process continued weekly, tracking individual progress and adjusting interventions accordingly.

This targeted approach allowed the teacher to observe how different students progressed through comprehension skills, whether sequentially or in a varied order. Additionally, by translating questions to the students' native languages, the aim was to gauge if this led to more nuanced English responses.

The scenario in this case study is genuine and based upon real events and data, however its narration has been crafted by AI to uphold a standardised and clear format for readers.

Key Learning

AI was used to differentiate instruction by translating or defining challenging words based on students' backgrounds in ESOL and remedial English classes.

Progressive assessments, facilitated by AI, adapted to individual students' needs, ensuring a tailored approach to address areas of improvement.

The iterative AI-driven process provided insights into students' comprehension skill progression and the impact of translating questions to their native languages on their English responses.


Translations provided by AI may not always be accurate, leading to potential comprehension missteps for ESOL students.

Relying solely on one set of responses for data might give a limited view of students' abilities. A more prolonged observation period can offer a fuller understanding.

Educators must be discerning in what they assess, ensuring they're evaluating ESOL students based on intended learning outcomes and not just linguistic comprehension.