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Statistical Prompt Engineering Framework for Multiple-Choice Questions for Film Critique Pedagogy |
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PP: 875-890 |
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doi:10.18576/jsap/140625
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Author(s) |
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Sahar R. Hamzah,
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Abstract |
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| This research presents a statistical prompt engineering framework for developing film and literary critique. The
framework is designed to generate multiple-choice questions that scaffold students through the cognitive skills needed for film critique. Film criticism is a major component of film and media studies. Students are often required to learn and improve their critique skills through performing critique without first having internalized the underlying cognitive skills required to perform critiques. The purpose of this study is to develop a replicable prompt engineering framework that demonstrates how instructors can use generative artificial intelligence to create multiple-choice questions that will support the step-by-step development of critique skills. Using a design-based methodology, the study breaks down film criticism into teachable cognitive components and aligns these components with Blooms Taxonomy. A master prompt was developed to generate a 50-question MCQ. The questions were of mixed difficulty and designed to support multiple interpretations and encourage comparative judgment. The master prompt was created using an iterative meta-prompting process to refine the structure, the constraints, and output strategies. The contribution of the study is a theoretically grounded master prompt and step-by-step instructor workflow that expands higher-order multiple-choice question design into humanities contexts, where such approaches are underexplored. The research also presents a prompt engineering framework for item and frequency analysis of the multiple-choice questions. The study concludes that, when constrained by pedagogical intent and explicit design rules, generative artificial intelligence can be used as an instructional design assistant that encourages the development of critical
thinking in humanities education. |
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