Biases in English Testing: A Call for Fairer Evaluation Systems

A Pearson survey reveals biases in English proficiency tests in India based on accents, appearance, and socioeconomic status. It highlights the need for fairer systems, free of biases. Pearson proposes using AI to eliminate such biases, focusing solely on language skills.

A recent survey by Pearson highlights significant biases faced by English proficiency test-takers in India. More than 62% of participants believe their Indian accent negatively impacts their speaking test results, and 74% worry that their appearance might skew scores when a human examiner is involved.

The survey, conducted among 1,000 respondents, indicates prevalent concerns over biases related to looks, accents, and appearances. Insights reveal fears of favouritism based on skin colour and attire, notably among residents of Maharashtra, Andhra Pradesh, Uttar Pradesh, and Tamil Nadu.

Pearson's director, Prabhul Ravindran, emphasized the organization's commitment to combating such biases by employing AI and language experts to focus purely on language proficiency. This strategy aims to foster an inclusive environment, ensuring everyone has equal opportunities.

(With inputs from agencies.)