Alexander Meier
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Meier
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Alexander
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alexander.meier@unisg.ch

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  • Publication
    An AI Approach for Predicting Audience Reach of Presentation Slides
    ( 2024-06)
    Alexander Meier
    ;
    Roman Rietsche
    ;
    Ivo Blohm orcid-logo
    There is a near overflow of presentation slides on digital platforms, such as SlideShare.net, with 40 million. This presents a challenge in assessing their projected impact due to its high complexity and required expertise. We propose a novel approach using machine learning techniques to predict presentation slide audience reach. We crawled a unique dataset of over 8000 slides and extracted relevant attributes. A model was trained where we are the first to employ both numerical and textual inputs. Initial results with an R² value of 0.579 suggest that the audience reach of presentation slides can be automatically evaluated. Our findings contribute to the current understanding of the assessment of online documents, introducing possibilities for further research, such as focusing on domain-specific applications and incorporating them as tools for decision support in content management systems on sharing platforms.
    Type: conference paper
    URL: https://aisel.aisnet.org/ecis2024/track04_impactai/track04_impactai/9/
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/120286
  • Publication
    Towards Assisted Excellence: Designing an AI-Based System for Presentation Slide Evaluation
    ( 2024-06)
    Alexander Meier
    ;
    Roman Rietsche
    ;
    Ivo Blohm orcid-logo
    Creating and disseminating high-quality presentation slides have be come a foundation for effective communication in educational, corporate, and scientific domains. This study addresses the challenge of enhancing the quality of user-generated presentation content amidst the vast quantities of existing re sources. The study is concerned with an ongoing design science research project that focuses on constructing a nascent design theory for a novel AI-based slide evaluation support system (SESS) that aims to assist users, particularly educators, create high-quality presentation slides. The proposed concept leverages recent developments in Generative Artificial Intelligence (genAI) to analyze multi modal user-generated content. Drawing on signaling theory, user-generated online reviews, and expert interviews, this research aims to contribute to deline ating the capabilities of artificial intelligence in digital content evaluation, spe cifically in assisting users to improve the quality of their presentation slides. For practitioners, we offer a set of generalized design principles and design features for the implementation in the development of an AI-based SESS.
    Type: conference paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/120285