From Linguistic Friction to Cognitive Equity: Introducing the AI-Mediated Accessibility Equalisation (AMAE) Framework for Non-English-Medium Learners in Indian Academic Libraries

Abstract

Purpose: Academic libraries in India deliver digital resources overwhelmingly in English, in a country where only about one in ten people reports any facility in that language. This paper introduces the AI-Mediated Accessibility Equalisation (AMAE) framework, explaining how artificial intelligence can reduce the Linguistic-Cognitive Accessibility Friction (LCAF) that non-English-medium learners experience.

Design/methodology/approach: The paper follows a theory synthesis design (Jaakkola, 2020), integrating Cognitive Load Theory, translanguaging theory, UTAUT, multi-level digital divide scholarship, information poverty theory, and the critique of linguistic imperialism, from which five testable propositions are derived.

Findings: AMAE comprises five intervention dimensions — Linguistic Translation, Cognitive Simplification, Interactive Dialogic, Modality, and Adaptive Personalisation Equalisation — governed by a cross-cutting Equity-Safeguard Layer. It repositions language as a stratum modulating all three levels of the digital divide, and specifies how AI offloads the extraneous cognitive load of English-language processing.

Research implications: The five propositions define a four-track empirical agenda covering instrument development, quasi-experimental testing in college libraries, adversarial auditing of translation quality, and comparative replication in other multilingual systems.

Practical implications: Libraries can equalise metadata, abstracts, open-access holdings, and library-authored content immediately, extending to licensed full text only where agreements permit derivative works. An incremental service stack, a governance policy, and consortium negotiating priorities are set out.

Originality/value: AMAE is, to the authors’ knowledge, the first framework uniting cognitive load, translanguaging, and digital divide perspectives in an AI-mediated accessibility model for academic libraries in a multilingual developing-country setting, completing the authors’ cumulative LCAF–DURA–AMAE research programme.