AIMIL-Based Human-AI Judgement for Inclusive Academic Research

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Saidatul Akmar Ismail, Wan Satirah Wan Md Sama, Md Hafizi Ahsan, Nurul Huda Md Yatim

Abstract

Generative artificial intelligence (GenAI) is increasingly embedded in academic research and can improve access for learners with disabilities through conversational search, simplification, translation, multimodal interaction, and assistive writing support. Yet AI-generated outputs may fabricate citations, obscure provenance, compress uncertainty, and encourage over-reliance on fluent but unsupported claims. This conceptual paper develops AI-Mediated Information Literacy (AIMIL) as a theory of responsible human judgement in AI-mediated information environments and proposes the AIMIL Inclusive Human-AI Information Judgement Model (AIMIL-IHIJ). A targeted conceptual review synthesises recent 2022–2026 literature on information literacy, AI literacy, critical AI literacy, Universal Design for Learning (UDL), GenAI reliability, academic-library instruction and inclusive education, supplemented by a small number of foundational sources. AIMIL is distinguished from adjacent frameworks by its explicit focus on claim-level provenance, recursive verification and accountable knowledge construction. The model comprises six capabilities: Frame, Engage, Triangulate, Trace, Verify, and Construct. These operate under three safeguards: accessibility and learner agency, human epistemic oversight and ethical responsibility. It introduces epistemic accessibility as the capacity not only to reach and understand information but also to trace, evaluate, and defend knowledge claims. Eight propositions are advanced for empirical testing, and an applied academic-library training architecture is outlined. A pseudonymised practice vignette from the researcher’s own unpublished internal institutional report is used only to demonstrate the current normalisation of AI-enabled, industry-connected learning environments. The paper argues that inclusive AI use should expand participation without displacing the learner’s responsibility for evidence, provenance and scholarly judgement.

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How to Cite
Saidatul Akmar Ismail,. (2026). AIMIL-Based Human-AI Judgement for Inclusive Academic Research. International Journal of Special Education, 41(19s), 653–659. Retrieved from https://www.internationalsped.com/index.php/ijse/article/view/5820
Section
General