Due to the deep penetration of Generative artificial intelligence(GenAI),the cultivation of pre-service teachers'teaching practice reflection competence is confronted with risks of superficial reflection and excessive reliance.Providing learning scaffolds constitutes one of the effective means to address these risks.Based on this,relying on social comparison theory,this paper designs GenAI-supported structural reflective scaffolds and explores its impact on pre-service teachers'teaching practice reflection competence through a randomized controlled experiment.Adopting methods such as questionnaire surveys,LDA topic analysis,and epistemic network analysis,this paper conducts analyses of inter-group differences in reflective thinking,quality differences in human-machine dialogues,and inter-group differences in reflection effectiveness.The findings reveal that the collaborative mechanism between intelligent technology and reflective scaffolds jointly facilitates the improvement of reflective thinking;GenAI-supported structural reflective scaffolds serve as a key factor influencing pre-service teachers'teaching practice reflection effect by standardizing and deepening the cognitive process of human-machine dialogues and improving real-time interaction quality.This paper reveals the necessity and feasibility of collaborative design between technology empowerment and scaffold support in pre-service teacher education,and provides empirical reference for cultivating pre-service teachers'teaching practice reflection competence in the intelligent era.
Unlike traditional learning platform with the core functions of resource organization,teaching interaction,and learning process support,as well as AI-augmented learning platform that merely introduce AI plugins into partial functions,AI-native learning platform emphasize the deep integration of artificial intelligence technologies into platform architecture,business workflows,and user interactions.Facing the in-depth demands that the development of personalized education places on learning platform,this paper first proposes the concept of an"AI-native learning platform"and designs a three-layer architecture consisting of the bottom layer,middle layer,and top layer based on the fundamental reshaping of the development paradigm.With large language model serving as the cognitive hub,this architecture can enable dynamic organization and intelligent scheduling of system services,providing a new idea for the intelligent,personalized and sustainable evolution of learning platform.Then,taking University G in North China as an example,this paper elaborates on the transformation pathway for existing platforms evolve toward AI-native one,including the reconstruction of the underlying data system,the middle-layer middle platform,and top-layer interaction,as well as newly-added modules for AI-native learning platform.The engineering feasibility of the architecture is validated through the construction of the eLLM training platform.Finally,this paper discusses the problems encountered during the deployment and application of AI-native learning platform and puts forwards targeted directions for future research.The architecture of AI-native learning platform proposed in this paper provides a systematic design idea and implementation pathway for the construction of smart educational infrastructure,and can serve as a reference for the application of AI agent in large-scale educational scenarios.
The disconnection between learning content and the real world constitutes one of the main problems in current education,and it also serves as an important reason influencing students'cognitive development.Grounded from the perspective of genetic epistemology,this paper interprets the fundamental connotation of learning scenarios in supporting cognitive development and analyzes the value implications of digital-intelligent technology empowering learning scenarios starting from the evolutionary logic of digital-intelligent technology.Based on four key dimensions of learning scenarios including boundary,representation,interaction,evaluation,combined with four advanced levels including cognitive activation,cognitive construction,cognitive transfer,cognitive improvement,centering on how to construct learning scenarios based on genetic epistemology mechanism,how to highlight the empowering effect of learning scenarios on cognitive advancement,and how to realize the empowerment of learning scenarios for cognitive advancement practice,the basic framework of digital-intelligence technology empowering learning scenarios is established.Finally,this paper proposes specific practical pathways from the perspectives of embodied field construction,precise resource supply,human-AI collaborative interaction,and data-driven evaluation,so as to provide theoretical basis and path guidance for overcoming application misconceptions and informing teaching practices.
Human-machine collaborative teaching has become an inevitable trend in educational reform in the intelligent era,but existing literature lacks evaluation research on primary and secondary school teachers'human-machine collaborative teaching ability.Accordingly,following the research idea of"concept definition—system construction—scale development and revision application",this paper first elucidates the connotation of human-machine collaborative teaching ability.Next,the evaluation system for teachers'human-machine collaborative teaching ability is preliminary constructed.Two rounds of expert opinions consultation via the Delphi method are conducted to revise the evaluation indicators,and the evaluation system for teachers'human-machine collaborative teaching ability including 4 first-level indicators,11 second-level indicators,and 37 third-level indicators is ultimate formed.The analytic hierarchy process and expert ranking methods are adopted to determine the weight of each indicator.Subsequently,combined with actual teaching scenarios in primary and secondary schools,this paper develops the assessment tool named"assessment scale for primary and secondary school teachers'human-machine collaborative teaching ability".The scale is revised through Rasch model test and its secondary test.Results analyzing the ability differences among primary and secondary school teachers reveal that their overall human-machine collaborative teaching ability is at a lower-than-intermediate level.In response,this paper recommends that education administrative departments conduct precise diagnosis and implement stratified training to improve primary and secondary school teachers'human-machine collaborative teaching ability.The evaluation system for teachers'human-machine collaborative teaching ability constructed in this paper can provide a structured reference for assessing teachers'professional ability in the intelligent era,and the developed assessment tool can provide reliable support for teachers'professional development and educational administration decision-making.
To advance the construction of smart education demonstration zones,it is urgent to establish a scientific and effective policy support system,for which precise quantitative evaluation of current policy schemes serves as an important prerequisite for driving policy optimization and dynamic adjustment.Based on this,the paper first proposes an analytical framework of policy supply for smart education demonstration zones,and then constructs an evaluation index system for such policy supply.Taking policy documents released by 18 national smart education demonstration zones as research objects,this paper adopts the LDA topic model and PMC index model to quantitatively evaluate policy attention allocation and policy supply characteristics,and analyzes policy implementation dilemmas of smart education demonstration zones.The results show that there are differences in policy issue selection preference,policy attention orientation,and policy attention spatio-temporal allocation among different demonstration zones.The policy supply for smart education demonstration zones shows high consistency in overall design and core-element selection,forming three types of demonstration zones include the"resource-talent"oriented type,"technology-service"oriented type,and"security-service"oriented type.In terms of specific policy implementation,each demonstration zones face multi-faceted dilemmas.Finally,from three paths of problem-driven approach,process control,and pressure response,this paper proposes dynamic adjustment strategies for policy supply of smart education demonstration zones,with a view to providing decision-making references for optimizing policy supply quality and promoting the realization of the national strategic goal of"driving overall progress through exemplary pilot sites".
