When Class Time Falls Short: An Alternative Path to Application-Based Learning with GenAI

This study examines the impact of GenAI (Generative Artificial Intelligence) supported application-based learning on high school students' academic achievement and course perceptions in the programming languages course. The study was conducted over six weeks with 77 10th-grade students in a public high school. A quasi-experimental design was used, involving two experimental groups and one control group. While one experimental group engaged in application-based activities under teacher guidance in class, the other completed the same activities at out-of-class using ChatGPT prompts. The control group followed the standard curriculum. Quantitative data were collected using an achievement test and course evaluation scale. One-way ANOVA results indicated no statistically significant difference in academic achievement among the groups. But the mean scores of the students in the experimental groups were higher than the control group. Moreover, students in both experimental groups reported significantly more positive course perceptions compared to the control group, particularly in the dimensions of course, instructor, and method-technique. Furthermore, while a weak positive correlation was found between course perception and academic achievement, it was not statistically significant. The findings highlight that although short-term academic gains may not differ significantly, both in-class application-based activities and GenAI-supported out-of-class activities enhance students’ perception of the course. The study underscores the potential of GenAI tools as pedagogical aids in promoting active learning, especially when in-class application time is limited. It suggests increasing the number of application-based course hours in the curriculum and emphasizes that, in cases where this is not possible, GenAI-supported out-of-class activities can be considered as an alternative.