Parallel Education with AI Agents: Teaching Innovation with New AI and Creativity Science

Wang Fei-Yue

Abstract

Higher education still largely relies on the traditional model of classroom lecturing, student assignments, and examination-based evaluation, which faces limitations in timely support, personalized learning, collaborative organization, and continuous teaching optimization. To address these limitations, this paper proposes Parallel Education, a framework for intelligent teaching and learning in the AI era. Inspired by parallel intelligence and the ACP approach, the framework constructs an artificial teaching society that operates in parallel with the real teaching system. It extends professors, teaching assistants, and students into digital professors, digital teaching assistants, and digital students, enabling educational roles, learning states, and collaborative relations to be represented and updated in digital environments. A five-layer system architecture is designed, including the physical teaching system, data collection, artificial teaching society, parallel intelligence, and application and governance layers. The paper further discusses representative scenarios, including online learning, parallel classroom teaching, offline practice, collaborative learning, and creative learning. The proposed framework aims to move intelligent education from fragmented AI tools toward a virtual-real integrated and human-centered educational paradigm.

Artificial Intelligence for Education

Parallel Education with AI Agents: Teaching Innovation with New AI and Creativity Science

Abstract

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