人机共创的教学特征:认知发展与角色交互研究

The Teaching Characteristics of Human-Machine Co-Creation:A Study on Cognitive D

本研究构建了面向人机协同的创造性问题解决“HMC-PISC”模型,并开展为期8周的教学实验,通过认知网络分析和社会网络分析挖掘人机交互数据,揭示人机共创的教学特征与规律。研究表明,在认知发展方面,学生的认知网络呈“房子”形的网状稳定结构,高水平组问答式交互以知识型问题为主,认知发展轨迹呈“の”形,认知网络结构紧密且均衡,连接强度较强,低水平组更关注非知识型问题,认知发展轨迹呈“ㄑ”形,认知网络结构松散,连接强度弱;在角色交互方面,高水平组作为平衡群体,充分发挥着ChatGPT的“供给者”作用,实现了“人—机—人”之间观点的流通和汇聚,低水平组分别为分散群体和权利斗争群体,ChatGPT成为“边缘者”,以“人—人”交互为主。未来的教学实践可通过“可为”“有为”“能为”等路径明确人机共创由低到高的转换方法,培养面向人机协同的创新型人才。

This research constructed a creative problem-solving model for human-machine collaboration(HMC-PISC)and conducted an 8-week teaching experiment to test it.Using Event Network Analysis(ENA)and Social Network Analysis(SNA)to explore Human-Machine interaction data,the research revealed the characteristics and rules of Human-Machine co-creation in teaching.The results showed that all students'cognitive networks exhibited a stable“house-shaped”structure in cognitive development.The question-and-answer dialogues of the students in the high-level group were dominated by“knowledge problems,”with cognitive development trajectories resembling an“の”shape.Their cognitive network structure was tight and balanced,with a strong connection strength.In contrast,the low-level group focused more on non-knowledge-based problems,showing“ㄑ”shaped with cognitive development trajectories and a looser network structure with weaker connections.In role interactions,the high-level group,as a balanced community,fully utilized ChatGPT's role as a“provider,”facilitating the flow and convergence of ideas between“Human-Machine-Human”interactions.The low-level group,composed of dispersed and power-struggle communities,marginalized ChatGPT,relying mainly on“Human-Human”interactions.In the future,to promote the transition from low to high-level Human-Machine,co-creation can be facilitated through three pathways:“Can-Do,”“Will-Do,”and“Competent-Do,”fostering innovative talents equipped for Human-Machine collaboration.