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Given the emergence of GenAI, students should develop GenAI literacy to promote its benefits and mitigate its drawbacks. However, many studies focus on enhancing their learning experience with GenAI, not understanding GenAI literacy. Two studies are dedicated to GenAI literacy, but they either require additional sessions or focus on overly specific tasks. We integrate GenAI into a data science course and its assessments to specifically promote GenAI literacy for non-computing students. The course design expects students to learn from their direct experience with GenAI, especially regarding GenAI usability, reliability, ethics, and privacy. Students are encouraged to use and acknowledge GenAI for some assessments and to align GenAI-generated programs to their own styles. Our evaluation involving 113 students showed that the course design might help students to understand GenAI characteristics and change their behaviour. Students are unlikely to be involved in GenAI misuse. Further, they align GenAI-generated programs and acknowledge their use. From the educational viewpoint, students could also achieve the course learning objectives.

Setting the number of operators in a production line is very important because it affects the production line's ability to achieve production targets. In the manufacturing industry with make-to-stock (MTS) production with several production lines, operators are often moved from certain production lines to production lines that require more operators. One of the reasons for the need for increasing operators in the production line is the approaching orders due date. This problem was faced by a furniture manufacturing in Indonesia which had imprecise number of operators arrangements and task assignments so that implicated to unachieved production target. This research applied U-shaped line balancing to increase the line efficiency to meet the production target. The research data were collected from direct observation on the actual production line and also from company documents. The methods used in the U-shaped line balancing were maximum ranked positional weight, maximum total number of follower tasks, minimum total number of follower tasks, maximum task time, and minimum task time. The highest line efficiency was obtained by the maximum ranked positional weight method which was 84.08%, increased by 24.37% from the actual one. Besides, the required operators were reduced from 15 to 10 persons with a production capacity of 76,688 units in 12 weeks.