JOURNAL ARTICLE

Making immersive storytelling accessible: Interactive low-tech implementation in elementary school civic learning.

  • Published In: Convergence: The Journal of Research into New Media Technologies, 2025, v. 31, n. 1. P. 36 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Kuznetcova, Irina; Tilak, Shantanu; Wen, Ziye; Glassman, Michael; Anderman, Eric; Lin, Tzu-Jung 3 of 3

Abstract

This article focuses on the design and implementation of a low-tech immersive storytelling (IST) approach for elementary education using Google Slides to create psychological immersion through participatory learning. Addressing challenges of high-cost, technologically immersive tools like virtual and augmented reality, the study developed a 10-day Native American history unit that engaged 234 students across nine U.S. classrooms by combining narrative, interactive decision-making, and collaborative problem-solving grounded in culturally relevant pedagogy. Drawing inspiration from tabletop roleplaying games and leveraging familiar, accessible technology, the curriculum fostered student agency, empathy, and critical dialogue without requiring advanced hardware or software. Preliminary student feedback indicated high engagement and emotional connection, suggesting that low-tech IST can be a feasible, flexible alternative for immersive learning in resource-constrained educational settings.

Additional Information

  • Source:Convergence: The Journal of Research into New Media Technologies. 2025/02, Vol. 31, Issue 1, p36
  • Document Type:Article
  • Subject Area:Education
  • Publication Date:2025
  • ISSN:1354-8565
  • DOI:10.1177/13548565231199967
  • Accession Number:183687329
  • Copyright Statement:Copyright of Convergence: The Journal of Research into New Media Technologies is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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