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Course-specific digital learning materials can support engagement with chemical concepts, but their development may require programming expertise and external resources. This Technology Report describes the design, classroom implementation, and preliminary evaluation of two instructor-developed browser-based chemistry games used in an undergraduate analytical chemistry course. A central design principle was to make selected chemical information relevant to decisions during play rather than use chemistry only as quiz content. In Carbon Chain Puzzle, molecular names and formulas can help players anticipate simplified block geometry and plan spatial moves; in Titration Rush, stoichiometry, acid identity, and pKa can inform a coarse-to-fine titrant-addition strategy under a time–accuracy trade-off. Generative-AI-assisted coding supported implementation, while the instructor retained responsibility for chemical validity, instructional aims, gameplay, and final design decisions. A voluntary anonymous survey yielded 27 responses from 72 enrolled students. On a five-point rating scale, mean enjoyment scores were 4.59 and 4.67, and mean perceived-value scores as chemistry learning materials were 4.15 and 4.33 for Carbon Chain Puzzle and Titration Rush, respectively. Auxiliary web analytics documented use but were not treated as measures of conceptual understanding. The study addresses feasibility, student perceptions, and design considerations rather than learning gains, and documents an instructor-led case of developing chemistry games with generative-AI-assisted coding.
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Hiroyuki Tonami (2026) studied this question.
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