"GenLadder SUS & GEQ Questionnaire"

"Recent advances in Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), have enabled dynamic and adaptive content generation in interactive applications such as digital games. However, the use of GenAI in logic-constrained puzzle games introduces significant challenges related to correctness, solvability, and consistent difficulty control. This paper presents GenLadder, an AI-driven word ladder game that integrates LLM-based puzzle generation with a deterministic, graph-theoretic validation framework to ensure linguistic validity and guaranteed solvability. Personalized word ladder puzzles are generated in real time based on player proficiency and selected difficulty levels, while a multi-stage validation pipeline filters invalid content and verifies solvability using breadth-first search (BFS) over length-specific word graphs. The BFS solver guarantees optimal transformation paths and enables objective difficulty classification based on minimal path length.To evaluate usability and player experience, a quantitative user study was conducted with 31 participants who played the GenLadder mobile application and completed standardized questionnaires. Usability was assessed using the System Usability Scale (SUS), while player experience was evaluated using the Game Experience Questionnaire (GEQ). Results indicate that GenLadder achieved high perceived usability, exceeding established SUS benchmarks, and demonstrated strong player engagement, particularly in the dimensions of challenge, immersion, and flow. These findings suggest that combining GenAI-driven content generation with formal algorithmic validation can produce scalable, reliable, and engaging puzzle games. Beyond the word ladder domain, the proposed architecture provides a generalizable blueprint for safely integrating generative AI into interactive systems that require strict logical constraints."