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Position Papers of the 21st Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 48

Towards Inclusive Intelligence: Defining the Research Challenges at the Intersection of Artificial Intelligence, Universal Design for Learning, and Workplace Training

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DOI: http://dx.doi.org/10.15439/2026F1991

Citation: Aina Irbe,

Full text

Abstract. Artificial intelligence (AI) is rapidly reshaping workplace learning and development (L\&D), yet this shift is occurring without a validated framework for inclusion. In particular, there is no clear, evidence-based approach for designing, delivering, and evaluating AI-enabled learning in ways that anticipate learner variability, reduce barriers, and support equitable participation. Universal Design for Learning (UDL) provides a relevant conceptual foundation because its three principles align with capabilities associated with generative AI and adaptive learning systems. However, conceptual alignment does not by itself demonstrate inclusive impact. Research on UDL in workplace settings remains limited, and the available evidence suggests that outcomes may vary by learning domain. At the same time, AI tools can introduce new accessibility barriers through inaccurate captions, inaccessible generated content, biased speech recognition, and optimization targets that fail to account for learner variability. No validated framework currently exists to evaluate whether the use of AI to develop workplace learning content and systems meet UDL-informed accessibility and inclusion criteria. This challenge paper identifies three open problems: misalignment between AI optimization targets and inclusive workplace learning outcomes, accessibility barriers introduced by AI tools themselves, and the absence of a validated framework for evaluating whether AI-powered workplace learning is genuinely inclusive.

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