Response Mechanism to AI driven Unemployment: A Design Science Study of the AI Shared Intelligence Education Platform and an Early Evaluation of Learning Retention and Digital Assetization

AI is reshaping job tasks and role structures. For many workers, the problem is not low capability, but that the tasks they used to do are no longer needed or have been rewritten, which increases the risk of unemployment and forced career transitions. Societies commonly respond by encouraging reskilling, yet practice often stalls because learning is frequently interrupted, learning outcomes are hard to prove, and high quality content supply is difficult to sustain.This study proposes the AI Shared Intelligence Education Platform as a response mechanism. The platform does not only teach AI tools. It provides broad, transferable knowledge and practice paths spanning workplace competencies, communication and collaboration, digital tools, content production, and practical life skills. The platform is organized around three roles: Learners who build routines and progress through small steps, Promoters who diffuse access and outcome cases to reduce information gaps, and Profit Sharers who co create high quality content and accumulate reusable assets.We adopt Design Science Research to design an operational platform concept and evaluate two core mechanisms: learning retention and digital assetization. As an early evaluation, we integrate a January 2026 Master Table (N = 1000; Learner 370, Profit Sharer 310, Promoter 320) using an anonymized unique user identifier as the master key. Results indicate high recorded participation and strong payment and upgrade intentions across roles, with role aligned patterns in promotion and asset indices. Limitations include the use of monthly aggregated proxy indicators and the need for longitudinal pre and post measurements.