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Communications of the Association for Information Systems

Author ORCID Identifier

Maximiliano Jeanneret Medina: https://orcid.org/0000-0003-4203-7680

Cédric Baudet: https://orcid.org/0000-0002-9606-7132

Denis Lalanne: https://orcid.org/0000-0001-7834-0447

Abstract

Education for all requires accessible digital documents. However, accessibility principles have yet to be integrated into the practices of publishers and teachers who create such documents. Moreover, the remediation of educational documents remains time-consuming when done by specialists. To address this issue, in collaboration with a department for document remediation, we introduced an automated and autonomous software artifact leveraging artificial intelligence (AI) to make static educational documents more accessible to blind and low vision (BLV) learners. Following a multilevel research design and two perspectives of agency, we adopted naturalistic methods of inquiry (participant observation and interviews) complemented by a rich set of secondary materials. From a sociomaterial perspective and drawing on actor-network theory, we reveal the rich ecosystem of actors around document remediation at the macro level and the regulatory role of BLV learners at the meso level. At the micro level, by analyzing the interactions between the BLV learner, the remediation specialist, and the agentic IS artifact through the lens of the willingness to delegate theoretical framework, we identified that two types of agents (reactive and prescriptive) would be more effective than one. Finally, we discuss key considerations about such computing systems and accessibility in IS research.

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