What if AI systems weren't chatbots?

2026-05-08

AI Summary

This study addresses the overemphasis on chatbot interfaces in contemporary artificial intelligence, which obscures their limitations in complex or high-stakes contexts and engenders systemic issues such as skill atrophy, knowledge homogenization, labor displacement, and computational inefficiency. Drawing on a sociotechnical systems framework and integrating perspectives from human–computer interaction, institutional design, and AI governance, the work critically exposes the structural deficiencies inherent in the prevailing chatbot paradigm. It proposes an alternative design philosophy centered on domain adaptability, accountability mechanisms, and social sustainability, advocating a shift from generic conversational agents toward diverse, task-specific AI systems embedded within robust institutional safeguards. This reconceptualization aims to advance the development of professionalized, pluralistic, and socially resilient artificial intelligence, offering both theoretical foundations and actionable policy pathways.

Abstract

The rapid convergence of artificial intelligence (AI) toward conversational chatbot interfaces marks a critical moment for the industry. This paper argues that the chatbot paradigm is not a neutral interface choice, but a dominant sociotechnical configuration whose widespread adoption reshapes social, economic, legal, and environmental systems. We examine how treating AI primarily as conversational assistants has extensive structural downsides. We show how chatbot-based systems often fail to adequately meet user needs, particularly in complex or high-stakes contexts, while projecting confidence and authority. We further analyze how the normalization of chatbot-mediated interaction alters patterns of work, learning, and decision-making, contributing to deskilling, homogenization of knowledge, and shifting expectations of expertise. Finally, we examine broader societal effects, including labor displacement, concentration of economic power, and increased environmental costs driven by sustained investment in large-scale chatbot infrastructures. While acknowledging legitimate benefits, we argue that the current trajectory of AI development reflects specific value choices that prioritize conversational generality over domain specificity, accountability, and long-term social sustainability. We conclude by outlining alternative directions for AI development and governance that move beyond one-size-fits-all chatbots, emphasizing pluralistic system design, task-specific tools, and institutional safeguards to mitigate social and economic harm.

Problem

• chatbot paradigm
• sociotechnical configuration
• AI interface
• structural downsides
• social sustainability

Innovation

• sociotechnical systems
• chatbot paradigm
• pluralistic AI design
• task-specific AI
• AI governance