Beyond the Tool-User Dichotomy: A Socio-Technical Investigation into Human-AI Co-Evolutionary Progress - Amartya Ghosh,; Pranab Gharai,; Ria Pyne,; Piyali De,; Avijit Kumar Chaudhuri,; Payel Sengupta,; Debmalya Mukherjee,; Ranjan Banerjee,; Arindam Sarkar, (The Bioscan, 2026)
This research examines the shifting paradigm of human-artificial intelligence (AI) interaction, moving from the historical view of AI as a static tool to a dynamic model of co-evolutionary progress. By analyzing the interplay between human cognitive capabilities and machine learning iterations, we identify specific mechanisms—communication interfaces, trust calibration, and reciprocal learning— that facilitate synergistic advancement. Through a multi-domain analysis involving healthcare, drug discovery, and creative arts, this paper provides empirical data showing that collaborative intelligence consistently outperforms autonomous systems. However, this progress is contingent on navigating socio- technical risks, including cognitive offloading and algorithmic bias. We argue that the future of work and intelligence lies not in competition, but in a synchronized niche where humans and machines shape each other’s developmental trajectories.