Reimagining teacher-AI co-design in learning task design: trends and perspectives

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Authors: Zongran Wang, Liu Mingzhuo, A.Y.M. Atiquil Islam
Cited by: 5
References: 39

Abstract

This systematic review synthesizes 28 empirical studies published between 2015 and 2025 that explore how teachers collaborate with artificial intelligence (AI), including Generative AI (GenAI) in the design of learning tasks across diverse educational contexts. The review focuses on four research questions regarding the theoretical frameworks used, the AI features implemented, the teacher–AI collaboration modes, and the objectives shaping such collaboration. Thematic coding reveals that GenAI tools are predominantly employed to support lesson planning, prompt generation, and creative ideation, and traditional AI tools are commonly used for designing analytics and for feedback design. Teacher-AI collaboration in design manifests in multiple forms, with AI and GenAI most often serving as an assistant or content generator, and less frequently as a co-designer or dialogic partner. Analysis of AI features indicates that tools are selected based on their alignment with four pedagogical affordances: efficiency, responsiveness, creativity, and equity. This review underscores the evolving role of teachers as instructional designers in the process of designing with AI tools and highlights the need for further research into equitable, context-sensitive theoretical models of teacher-AI co-design of learning tasks.

Fields of Study & Subjects

Innovative Teaching and Learning Methods Intelligent Tutoring Systems and Adaptive Learning AI in Service Interactions

Publication details

Journal / source
Humanities and Social Sciences Communications
Publisher
Springer Science and Business Media LLC
Volume
13
Issue
1
Publication date
3 April 2026
Language
EN