Low-Resource Strategies for IELTS Writing Preparation using Free Generative AI - Sangia, Rohib Adrianto (International Journal of Advanced Corporate Learning (iJAC), 2026)
The study assesses whether free, web accessible generative AI can produce IELTS Academic Writing band scores and feedback credible for formative use in low-resource contexts. A corpus of 110 official Task 1 and Task 2 responses was scored by four systems accessed at no cost: ChatGPT with GPT 4, Gemini 1.5 Flash, Claude 3.5 Sonnet, and DeepSeek. Agreement with official scores was quantified using Lin’s concordance correlation coefficient and the Bland-Altman method. Feedback was coded for structure, depth, specificity, and tone, with intercoder reliability estimated by Krippendorff’s alpha. Two systems showed minimal central bias with limits of agreement, while one system systematically underestimated bands. Agreement within a one-band tolerance was high. Feedback was criterion-linked and actionable but often lacked precise phrasing and had uneven clarity. Qualitative coding reliability was acceptable. Findings suggest that free AI can offer indicative banding and revision cues when combined with rubric checking and human oversight, especially in situations where bandwidth is limited. The study logs access conditions to support replication and sets priorities for self-directed learning.