JOURNAL ARTICLE

Immersive Time Travel: Unleashing Historical Consciousness in Detroit Become Human.

  • Published In: International Journal of Critical Cultural Studies, 2025, v. 23, n. 1. P. 135 1 of 3

  • Database: Humanities Source Ultimate 2 of 3

  • Authored By: AlTaher, Bassmah B.; Al-Habashneh, Eman; Salaita, Osama; Khasawne, Deema; ElHindi, Dina 3 of 3

Abstract

This study explores the integration of historical intertextuality in the narrative of the interactive game Detroit: Become Human, showcasing its immersive depiction of key moments in African American history. The game, marked by realistic visual effects and compelling gameplay, invites players to navigate moral complexities while embodying androids in pursuit of freedom. Crafted by Quantic Dream, the game employs diverse storylines that intricately interlink with historical events, fostering heightened historical consciousness. The intertextual elements connect the androids' emancipation struggle to broader historical contexts, underscoring the importance of reasoned player actions. Through a comparative analysis of the game's narratives and their allusions to slavery and the African American Civil Rights Movement, a profound cultural homage is unveiled within the characters, setting, and actions. Detroit: Become Human prompts players to critically examine cultural identity, historical roots, and societal structures, encouraging a nuanced perspective. By reliving and scrutinizing iconic moments of the past, the game serves as a potent platform for questioning prevailing notions of racism, prejudice, and stereotypes. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Critical Cultural Studies. 2025/06, Vol. 23, Issue 1, p135
  • Document Type:Article
  • Subject Area:Arts and Entertainment
  • Publication Date:2025
  • ISSN:23270055
  • DOI:10.18848/2327-0055/CGP/v23i01/135-152
  • Accession Number:189836188
  • Copyright Statement:Copyright of International Journal of Critical Cultural Studies is the property of Common Ground Research Networks 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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