On Teaching Novices Computational Thinking by Utilizing Large Language Models Within Assessments

Novice programmers often struggle to develop computational thinking (CT) skills in introductory programming courses. This study investigates the use of Large Language Models (LLMs) to provide scalable, strategy-driven feedback to teach CT. Through think-aloud interviews with 17 students solving code comprehension and writing tasks, we found that LLMs effectively guided decomposition and program development tool usage. Challenges included students seeking direct answers or pasting feedback without considering suggested strategies. We discuss how instructors should integrate LLMs into assessments to support students' learning of CT.