Promoting cognitive skills in AI-supported learning environments: the integration of bloom's taxonomy

被引:3
作者
Elim, Emily Hui Sein Yue [1 ]
机构
[1] UCL, Inst Educ, London, England
关键词
Bloom's taxonomy; cognitive thinking; reflective practice; generative artificial intelligence; experimental learning; INTERNATIONAL BACCALAUREATE; ARTIFICIAL-INTELLIGENCE;
D O I
10.1080/03004279.2024.2332469
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
This study introduces a model of Bloom's taxonomy aimed at deepening learners' cognitive thinking. It investigates the process of questioning and reflection within the structured framework of Bloom's taxonomy and explores how these insights can be applied in students' utilisation of generative Artificial Intelligence. An experiment was conducted in a Year 5 class at an International Baccalaureate Primary school in Hong Kong, involving 25 students. The findings indicate that Creating and Evaluating were the dominant aspects in the students' questioning and answering process. However, the skill of 'Applying' showed a significantly low influence, suggesting a lack of proficiency in applying AI conversations to other learning areas. This research contributes to the field by providing insights into the integration of generative AI within Bloom's taxonomy. Educators and researches can utilise these findings to enhance critical thinking and learning outcomes through AI integration.
引用
收藏
页数:11
相关论文
共 34 条
  • [31] Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey
    Sit, Cherry
    Srinivasan, Rohit
    Amlani, Ashik
    Muthuswamy, Keerthini
    Azam, Aishah
    Monzon, Leo
    Poon, Daniel Stephen
    [J]. INSIGHTS INTO IMAGING, 2020, 11 (01)
  • [32] Inquiry and critical thinking skills for the next generation: from artificial intelligence back to human intelligence
    Spector, Jonathan Michael
    Ma, Shanshan
    [J]. SMART LEARNING ENVIRONMENTS, 2019, 6 (01)
  • [33] Syamsuddin A., 2020, International Journal of Scientific Technology Research, V9, P4418
  • [34] Wang T., 2020, INT C ED ARTIFICIAL