Evaluation of the Quality of Various Generative AI Models in Creating Infographic Learning Media for Computer Science Education
Keywords:
Evaluation of the Quality of Artificial Intelligence, Infographic, Computer ScienceAbstract
The objectives of this study were: 1) to evaluate the quality of infographics generated by artificial intelligence for teaching and learning in primary-level computer science, and 2) to compare the quality of infographics produced by six AI models GPT, Midjourney, Bing AI, Canva AI, Google AI, and Meta AI—using ten sets of prompts designed to cover the core content of the primary computer science curriculum. The research instruments comprised a four-dimension infographic quality assessment form, which evaluated content accuracy, visual design, communication effectiveness, and attractiveness. Three experts assessed a total of 60 infographic samples (10 prompts × 6 AI models). The Index of Item Objective Congruence (IOC) ranged from 0.67 to 1.00, indicating a good level of alignment between the assessment items and the research objectives.
Descriptive statistics mean and standard deviation were employed for data analysis, and One-way ANOVA was used to compare the quality scores among the AI models. The findings revealed that infographics generated by GPT achieved the highest quality scores with statistical significance (Mean = 48.87, S.D. = 1.66), followed by Google AI (Mean = 29.63, S.D. = 1.51) and Canva AI (Mean = 25.63, S.D. = 2.45). In contrast, Bing AI, Midjourney, and Meta AI produced noticeably lower average scores. Tukey’s HSD test further classified the AI models into four quality groups: Group A: GPT; Group B: Google AI; Group C: Canva AI; and Group D: Bing AI, Midjourney, and Meta AI. These groupings reflect the different capabilities of AI models in generating educational media. The results underscore the importance of prompt design as a key factor influencing the quality of AI-generated instructional materials.
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