Weijuan Kong, Yanhua Ning, Ting Ma, Fei Song, Yuxin Mao, Cailing Yang, Xinjin Li, Yahong Guo, Haiyan Liu, Jing Shi, Lingna Liu
Experience of undergraduate nursing students participating in artificial intelligence + project task driven learning at different stages: a qualitative study
BMC nursing
study aimed assess performance chatgpt gpt35 gpt4 study tool solving biostatistical problems identify potential drawbacks might arise using chatgpt medical education particularly solving practical biostatistical problems chatgpt tested evaluate ability solve biostatistical problems handbook medical statistics peacock peacock descriptive study tables problems transformed textual questions ten biostatistical problems randomly chosen used textbased input conversation chatgpt versions 35 4 gpt35 solved 5 practical problems first attempt related categorical data crosssectional study measuring reliability probability properties ttest gpt35 failed provide correct answers regarding analysis variance chisquare test sample size within 3 attempts gpt4 also solved task related confidence interval first attempt solved questions within 3 attempts precise guidance monitoring assessment versions chatgpt performance 10 biostatistical problems revealed gpt35 4s performance average correct response rates 5 6 10 first attempt gpt4 succeeded providing correct answers within 3 attempts findings indicate students must aware tool even providing calculating different statistical analyses wrong aware chatgpts limitations careful incorporating model medical education