Enhancing Human-Computer Interaction in Socially Inclusive Contexts: Flow Heuristics and AI Systems in Compliance with DIN EN ISO 9241 Standards

Abstract

In the domain of generative Artificial Intelligence (AI), the deployment of user-centered Human-Computer Interaction (HCI) methodologies, compliant with the DIN EN ISO 9241 standard, furnishes a substantive methodological scaffold. These methodologies have demonstrably succeeded in accomplishing specific, contextual objectives across a spectrum of applications, affirming their efficacy and user satisfaction. The investigation leverages a systematic theoretical framework and investigator triangulation to explore the customization of flow heuristics with respect to social permeability, inclusion, and accessibility. This approach facilitates the assimilation of multi-dimensional perspectives, thereby augmenting both accessibility and the user experience within these delineated domains. Within the ambit of enhancing HCI in socially inclusive settings, current heuristic models aimed at simplifying complexity in AI systems manifest notable shortcomings, particularly in relation to social permeability, inclusion, and accessibility. Academic scrutiny not only highlights these deficiencies in the application of DIN EN ISO 9241-210 standards but also uncovers substantial lacunae vis-à-vis the intricate demands of DIN EN ISO 9241-110. These encompass facets such as task appropriateness, self-descriptiveness, expectation conformity, error tolerance, controllability, individualizability, and learning facilitation, whose integrated and considered implementation is indispensable for the efficacious tailoring of interactive systems to meet specific user requirements in inclusive settings. This study significantly bolsters the epistemological base essential for the formulation of a conceptual framework tailored for AI deployment in educational and social management contexts, in alignment with DIN EN ISO 9241 standards. The study aims to articulate a detailed practical guide that outlines the requisite mechanisms, metrics, and characteristics necessary for user-centric adaptations in AI-enhanced systems. The objective is to augment the efficacy of flow induction and elevate user satisfaction within realms characterized by social permeability, inclusion, and accessibility.

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