Recently, the academic paper "Inquiry-based Classroom Research: Constructing an AIED Human-AI Collaborative Pedagogical Research Framework for Developing Wisdom" co-authored by Executive Vice Dean and Director of Teacher Development Center Xiaoyong Hu, doctoral student Yaqi Xie, master's student Jieru Lu, and Associate Researcher Zirou Lin was published in Modern Distance Education. This marks important progress for the "Wenke" project, with the proposed AIED human-machine collaborative teaching research model solving the methodological problem of "how to ask."

Figure 1: Paper "Inquiry-based Classroom Research: Constructing an AIED Human-AI Collaborative Pedagogical Research Framework for Developing Wisdom"

"Wenke" (Inquiry-based Classroom Research) refers to a new teaching research paradigm where teachers collaborate with machine intelligence, using "questions" as the medium to peel back classroom phenomena, gather practical evidence, and achieve teaching improvement through precise questioning and deep inquiry. Addressing the dual dilemmas of "participation imbalance" between humans and machines and "questioning aphasia" among teachers commonly found in current human-machine collaborative teaching research practice, this study constructs an AIED human-machine collaborative teaching research model for promoting wisdom generation. This includes a four-stage cyclic action framework of Aim (positioning teaching research goals), Interpret (interpreting classroom phenomena), Explore (exploring multi-source evidence), and Derive (extracting practical knowledge), systematically responding to the methodological problem of "how to ask" for teachers conducting Wenke research.

Figure 2: AIED human-machine collaborative teaching research model

To conduct Wenke research guided by the AIED model, the research team developed a Wenke system prototype (hereinafter referred to as "Wenke System") based on large models such as DeepSeek and Qwen. Taking diagnostic teaching research of a new lesson on "Pythagorean Theorem" for eighth-grade mathematics at a middle school as an example, the study illustrates the process of teachers conducting AIED collaborative Wenke research based on the Wenke System, revealing the unique advantages of this model.

Figure 3: Wenke system prototype interface

 Based on pre-test and post-test data of teaching research, the study systematically evaluates the application effectiveness of the AIED Wenke research model from two dimensions: classroom teaching questioning and teaching research reflective questioning. Experimental results show that the AIED model can effectively guide teachers to explore the essence of classrooms through precise questioning and deep inquiry, significantly improving their classroom teaching questioning and teaching research reflective questioning abilities.

Figure 4: Dual value-added effects of classroom questioning and teaching research questioning based on aied model

 This research represents a milestone achievement of our institute in Wenke research, systematically responding to the core proposition of "how to ask" in human-machine collaborative teaching research for the first time. It promotes the innovative transformation of classroom research paradigm from "understanding lessons through data" to "solving lessons through questions," effectively serving the overall requirements of "cultivating future teachers and building future classrooms" in the national education digitalization strategic action.

 Our institute will continue to deepen theoretical construction and practical innovation in Wenke research, advance the development and promotion of "Wenke Large Model" through industry-university-research collaboration, and build replicable and scalable digital intelligent classroom teaching research models relying on the "Wenke" intelligent teaching research community with deep participation from frontline teachers, contributing wisdom and practical experience to the education digitalization strategic action and high-quality teacher team building.

Editor: Jieru Lu

Managing Editors: Yuyin Lin, Tianwei Peng

First Review: Yaqi Xie

Second Review: Zirou Lin