Post-Digital Dialogism: A Multimodal Pragmatic Analysis of Algorithmic Discourse in Human-AI Interaction

Abstract This study investigates post-digital dialogism by analyzing the multimodal pragmatic dynamics of algorithmic discourse in human-AI interaction. Drawing on Bakhtin’s (1981) dialogic theory, the research explores how text, speech, and visual cues converge to shape meaning in AI-mediated communication. A mixed-methods approach, combining naturalistic observations, controlled experiments, eye-tracking, surveys, and interviews, was employed with 50 participants interacting with AI systems under three conditions: fully multimodal, text-only, and speech-only. Quantitative findings revealed that multimodal interactions yielded higher trust (M = 5.9, SD = 0.6) and engagement (M = 6.1, SD = 0.5) compared to single-modal conditions (p < .001), with eye-tracking showing increased attention to visual cues in aligned settings (p < .01). Qualitative analysis identified themes of dialogic authenticity, pragmatic alignment, and cultural dialogic variance, with participants from diverse backgrounds interpreting cues differently, aligning with cross-cultural communication patterns (Kita, 2009). Pragmatic misfires in single-modal or monocultural systems reduced coherence, while algorithmic interpellation influenced user responses in 65% of interactions. These results highlight the importance of semiotic convergence for fostering dialogic resonance and underscore the limitations of current AI pragmatic competence (Bender et al., 2021). The study advances linguistic theory by introducing computational pragmatics and informs ethical AI design for inclusive, context-sensitive systems. Future research should explore naturalistic settings and additional modalities to enhance generalizability.

Keywords:

Post-digital dialogism , algorithmic discourse , multimodal pragmatics , human-AI interaction , semiotic convergence , computational pragmatics , cultural dialogic variance