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Understanding Student Acceptance, Trust, and Attitudes Toward AI-Generated Images for Educational Purposes

arXiv 2411.15710 ↗First posted 24 Nov 2024cs.CY cs.AIcited by 0

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Abstract (arXiv)

Recent advancements in artificial intelligence (AI) have broadened the applicability of AI-generated images across various sectors, including the creative industry and design. However, their utilization in educational contexts, particularly among undergraduate students in computer science and software engineering, remains underexplored. This study adopts an exploratory approach, employing questionnaires and interviews, to assess students' acceptance, trust, and positive attitudes towards AI-generated images for educational tasks such as presentations, reports, and web design. The results reveal high acceptance, trust, and positive attitudes among students who value the ease of use and potential academic benefits. However, concerns regarding the lack of technical precision, where the AI fails to accurately produce images as specified by prompts, moderately impact their practical application in detail-oriented educational tasks. These findings suggest a need for developing comprehensive guidelines that address ethical considerations and intellectual property issues, while also setting quality standards for AI-generated images to enhance their educational use. Enhancing the capabilities of AI tools to meet precise user specifications could foster creativity and improve educational outcomes in technical disciplines.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 26 references that are in the map. First posted on arXiv 24 Nov 2024.

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References · 3

  1. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  2. 2023GenImage: A Million-Scale Benchmark for Detecting AI-Generated Imagecited by 321
  3. 2023Learning to Evaluate the Artness of AI-generated Imagescited by 10

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