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Does AI video summary help? The effects of AI-generated video summary

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

The accelerated life and flourishing short-form videos have squeezed the viability of long-form videos, which play important roles in the diffusion of knowledge. Video summary has been proposed to facilitate video views and knowledge acquisition as a solution to this dilemma. And the emergence of AI has made the solution possible. We propose that AI-generated video summaries may reduce the effort to acquire information, but logical confusion and information loss may reduce the acquired information quality. Based on the effort-accuracy framework, users with different information quality requirements will react differently towards videos with/without AI summary. Therefore, we plan to conduct laboratory experiments to explore whether and how AI summary increases users’ video-watching intention. Moreover, we will also examine whether AI summary usage affects knowledge acquisition quality. We expect to improve the understanding of AI video summary usage and provide insights into how to make it work efficiently. Copyright © 2024 [Ouyang & Xu].
Original languageEnglish
Title of host publicationACIS 2024 Proceedings
Publication statusPublished - 10 Dec 2024
EventAustralasian Conference on Information Systems (ACIS 2024) - University of Canberra, Canberra, Australia
Duration: 4 Dec 20246 Dec 2024

Conference

ConferenceAustralasian Conference on Information Systems (ACIS 2024)
PlaceAustralia
CityCanberra
Period4/12/246/12/24

Funding

The work described in this paper was fully supported by the National Natural Science Foundation of China (NSFC Grant No. 72271210).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 4 - Quality Education
    SDG 4 Quality Education
  2. SDG 10 - Reduced Inequalities
    SDG 10 Reduced Inequalities

Research Keywords

  • AI Video Summary
  • Effort-accuracy Framework
  • Loss Aversion
  • Video-watching Intention, recognition accuracy
  • Recognition Speed

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