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2 2024

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Procedurally generated AI compound media for expanding audial creations, broadening immersion and perception experience

Samson Grzegorz

International Journal of Electronics and Telecommunications

2024

Vol. 70, No. 2

342--348

Recently, the world has been gaining vastly increasing access to more and more advanced artificial intelligence tools. This phenomenon does not bypass the world of sound and visual art, and both of these worlds can benefit in ways yet unexplored, drawing them closer to one another. Recent breakthroughs open possibilities to utilize AI driven tools for creating generative art and using it as a compound of other multimedia. The aim of this paper is to present an original concept of using AI to create a visual compound material to existing audio source. This is a way of broadening accessibility thus appealing to different human senses using source media, expanding its initial form. This research utilizes a novel method of enhancing fundamental material consisting of text audio or text source (script) and sound layer (audio play) by adding an extra layer of multimedia experience - a visual one, generated procedurally. A set of images generated by AI tools, creating a story-telling animation as a new way to immerse into the experience of sound perception and focus on the initial audial material. The main idea of the paper consists of creating a pipeline, form of a blueprint for the process of procedural image generation based on the source context (audial or textual) transformed into text prompts and providing tools to automate it by programming a set of code instructions. This process allows creation of coherent and cohesive (to a certain extent) visual cues accompanying audial experience levering it to multimodal piece of art. Using nowadays technologies, creators can enhance audial forms procedurally, providing them with visual context. The paper refers to current possibilities, use cases, limitations and biases giving presented tools and solutions.

Multilingual Template-based Automatic Item Generation for Medical Education Supported by Generative Artificial Intelligence Models ChatGPT and Claude

Kiyak Yavuz Selim, Kononowicz Andrzej A., Górski Stanisław

Bio-Algorithms and Med-Systems

2024

Vol. 20, no. 1

81--89

Objective: This study has two main aims. (1) To generate multiple-choice questions (MCQs) using template-based automatic item generation (AIG) in Polish and to evaluate the appropriateness of these MCQs in terms of assessing clinical reasoning skills in medical education; (2) to present a method for using artificial intelligence (AI) to generate new item models based on existing models for template-based AIG in medical education. Methods: This was a methodological study. For the first aim, we followed Gierl’s three- -step template-based AIG method to generate MCQ items in Polish. The quality of the generated MCQs were evaluated by two experts using a structured form. For the second aim, we proposed a four-step process for using a parent template in English to transform it into new templates. We implemented this method in ChatGPT and Claude by using two medical MCQ item models. Results: Both experts found the automatically generated Polish questions clear, clinically sound, and suitable for assessing clinical reasoning. Regarding the template transformation, our findings showed that ChatGPT and Claude are able to transform item models into new models. Conclusions: We demonstrated the successful implementation of template-based AIG in Polish for generating case-based MCQs to assess clinical reasoning skills in medical education. We also presented an AI-based method to transform item models for enhancing diversity in template-based AIG. Future research should integrate AI-generated models into AIG, evaluate their exam performance, and explore their use in various fields.