Deliver AI tutor explanations in learners' native language as a prerequisite for meaningful educational access¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 review), q3 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

Because foreign-language delivery of technical content significantly reduces content and language learning outcomes, the article recommends AI tutoring systems explain programming errors in the learner's native language. It states that for a rural Bengali-speaking student, "native-language instruction is a prerequisite for meaningful educational access rather than an enhancement layered on top of it." The language of the tutor's output is therefore a core design decision, not a localization convenience.

Design Implications¶

Context¶

Requirements¶

• Native-language explanations require the underlying model and training data infrastructure to support the language adequately

Constraints¶

• General-purpose models deliver explanations in English given current training data distributions, so the strategy depends on the structural remediation the article describes

Target Learners¶

• Bengali-speaking learners with limited English proficiency
• Non-English-dominant programming students

Target Learning Goals¶

• Programming concepts, error diagnosis, and debugging comprehension

Claims¶

• English-delivered AI explanations impose a double cognitive burden: foreign-language content significantly reduced learning outcomes versus native-language instruction in controlled higher-education studies [+M]

Related Strategies¶

Examples¶

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Key Sources¶

• Lb0
• Avijit Roy, Proma Roy. (2026). Structural Silence: When AI Infrastructure Fails Speakers of Underrepresented Languages — A Case Study in Bengali, the Digital Divide, and the Hidden Cost of English-Centric Design. arXiv preprint. https://arxiv.org/abs/2608.12278