PLANATHON ACTIVITY - STUDENT FEEDBACK SURVEY

The integration of generative AI tools like large language models (LLMs) into software engineering (SE) curricula presents a challenge: balancing efficiency gains with the preservation of critical thinking and manual learning processes. This study proposes a two-phase instructional model that begins with manual project planning tasks (e.g., SRS, WBS, estimation, scheduling) and transitions into AI-assisted application through a capstone-style simulation called \Planathon.\ Implemented in an undergraduate-level software project management course (N = 57), the model guided students through foundational skill building before introducing LLMs (e.g., ChatGPT, Gemini) and task-specific AI tools. Findings show strong performance in estimation and WBS tasks but reveal common challenges in diagram generation and scheduling. Student reflections revealed effective prompt use and AI literacy, though limited engagement with ethical considerations. The results validate a scaffolded approach to AI integration\u2014preserving depth while enhancing productivity\u2014and offer a replicable framework for preparing SE students with critical, industry-aligned AI competencies.