2026 ASEE Annual Conference & Exposition

This is a evidence-based practice full paper. The rapid diffusion of digital technologies and the emergence of generative artificial intelligence (GAI) have fundamentally reshaped the landscape of educational practice worldwide. In response to these paradigm shifts, scholars have sought systematic ways to align emerging technologies with pedagogical objectives. The classic”Digital Bloom” framework—originally proposed by Andrew Churches to map various readily-available digital tools onto the six cognitive levels of the revised Bloom’s taxonomy—has become a widely cited reference for such alignment. Recognizing the need for cultural and infrastructural relevance, Chinese researchers localized this model in 2011 by substituting foreign platforms with domestically developed online classrooms, cloud-based services, and other home-grown applications. This localization not only expanded the repertoire of usable tools but also addressed cross-cultural fit, data-security regulations, and the specific demands of China’s rapidly expanding K-12 and higher-education ecosystems.
Since that initial adaptation, the educational technology environment has undergone a second, more profound transformation driven by GAI. Traditional digital tools are evolving into large language model (LLM) tutoring agents, AI-driven learning assistants, intelligent assessment systems, and personalized recommendation engines. These AI-enhanced resources possess capabilities that extend far beyond the static functionalities of earlier tools, offering dynamic content generation, real-time feedback, and adaptive learning pathways. To capture this evolution and provide a coherent selection guide for educators and policymakers, the present study proposes a “Chinese AI Digital Bloom (CAID Bloom)” framework, developed through the collection and comparative analysis of AI product data.
CAID Bloom retains the hierarchical structure of the revised Bloom’s taxonomy (remember, understand, apply, analyze, evaluate, create) while systematically cataloguing contemporary AI-enhanced educational products. The taxonomy is organized into six tiers corresponding to the cognitive levels and includes, for each tier, representative examples such as LLM-based tutoring agents that scaffold knowledge acquisition and comprehension, AI-augmented simulation environments that support application and problem-solving, automated grading and analytics platforms that facilitate analysis and evaluation and generative content-creation systems that enable learners to synthesize and create novel artifacts. For each tool, the framework documents functional features, typical application scenarios, technological maturity and compliance with Chinese data-privacy and cybersecurity regulations.
The CAID Bloom framework serves two interrelated purposes. First, it offers educators a visual, taxonomy-driven guide for selecting AI technologies that align precisely with specific cognitive objectives, thereby ensuring that AI augments rather than replaces essential learning processes. Second, it provides learners with AI resources tailored to each cognitive level, supporting precise, innovative, and learner-centered experiences in an AI-infused environment. By integrating functional evaluation with policy-relevant considerations, the framework delivers actionable technical support and theoretical insight for China’s ongoing educational informatization and intelligent transformation.
This research contributes both a theoretical extension of Bloom’s taxonomy into the GAI context and a practical, policy-compatible taxonomy that can be directly employed by curriculum designers, institutional leaders, and classroom practitioners. Future work will empirically test the efficacy of CAID Bloom across diverse instructional settings, examine its impact on learning outcomes and teacher practice, and explore its scalability across regions, disciplines, and emerging AI modalities.