Enhancing Computational Thinking of Islamic Education Students through CT-Based Prompt Engineering: A Quasi-Experimental Study on AI Multimodal Media Design - Imam Kharits Najibulloh; Ahmad Latif; Muhtyas Yugi; Beny Riswanto (Jurnal Teknologi Pendidikan : Jurnal Penelitian dan Pengembangan Pembelajaran, 2026)
This study evaluates the effectiveness of Prompt Engineering strategies based on Computational Thinking (CT) in enhancing the ability of Islamic Religious Education (PAI) students to design multimodal learning media (images, videos, and games) powered by Artificial Intelligence (AI). Using a quasi-experimental design involving 35 students, the research integrates the four pillars of CT decomposition, pattern recognition, abstraction, and algorithm design as logical foundations for composing AI instructions. The results show a significant increase in students’ CT scores, from an average of 56.80 to 85.10, with an N-Gain Score of 0.65 (effective category). Students successfully produced educational image media (92%), interactive game logic (84%), and animated videos (78%) with high theological accuracy. The abstraction pillar was found to be the most crucial in minimizing AI “hallucinations” in sensitive religious content, while algorithm design enabled the creation of systematic game flows. This strategy successfully transformed students’ roles from mere users to logical and critical instruction designers (Prompt Engineers). The study recommends integrating CT-Prompting into the PAI curriculum as a core competency to produce innovative and valid digital content in the era of artificial intelligence.