Improving learning achievement and self-regulated learning through AI-enabled formative assessment and visual reports: An experimental study

A total of 125 participants enrolled in ninth-grade biology courses participated in the experimental study and were assigned to one of two groups: a treatment group (n = 63), who received AI-enabled formative assessment in a visual format, and a control group (n = 62), who received a traditional assessment in the form of test scores and teacher feedback. Repeated-measures analysis of variance and nonparametric test results showed that the visual reports effectively improved students’ learning achievement. The results provide important implications for designing and implementing effective formative assessments supported by AI and visualization

共计125名选修九年级生物课程的参与者参与本实验研究，并被分为两组：实验组（n=63）接受可视化形式的人工智能赋能形成性评价（AI-enabled formative assessment），对照组（n=62）则采用以考试分数与教师反馈为形式的传统评价方式。重复测量方差分析（Repeated-measures analysis of variance）与非参数检验（nonparametric test）结果显示，可视化报告可有效提升学生的学习成绩。本研究结果可为设计与实施人工智能及可视化技术支撑的高效形成性评价提供重要参考启示。