Effect Gamification on Efficiency: An Analysis
Research Article
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Effect Gamification on Efficiency: An Analysis

Beilin Han 1*
1 Shanghai Shangde Experimental School, Shanghai, China, 201315
*Corresponding author: Luke3oo0@163.com
Published on 20 July 2025
Volume Cover
ACE Vol.175
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-237-9
ISBN (Online): 978-1-80590-238-6
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Abstract

Gamification refers to the application of game - design elements in non - game contexts. It has become popular in fields such as education and business because it has the potential to improve efficiency. This paper critically analyzes the impact of gamification on efficiency, covering both its strengths and limitations. The analysis in this essay is based on literature reviews, sources range from academic papers to well-known real-life examples like the popular language learning app Duolingo. Studies finds that gamification can increase efficiency by enhancing motivation, interactivity, and attention on task, but they also reveal that poorly designed gamification strategies may undermine intrinsic motivation, distract users from core goals, and even induce addiction. The essay concludes that the effectiveness of gamification depends on careful implementation aligned with psychological principles and user context., otherwise it may lead to adverse effects such as distractions and misled goals. In order to successfully use gamification as a tool, practitioners need to fully understand its nature.

Keywords:

gamification, efficiency, gamification strategies, motivation

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Han,B. (2025). Effect Gamification on Efficiency: An Analysis. Applied and Computational Engineering,175,37-41.

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Cite this article

Han,B. (2025). Effect Gamification on Efficiency: An Analysis. Applied and Computational Engineering,175,37-41.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-CDS 2025 Symposium: Application of Machine Learning in Engineering

ISBN: 978-1-80590-237-9(Print) / 978-1-80590-238-6(Online)
Editor: Marwan Omar, Mian Umer Shafiq
Conference website: https://www.confcds.org
Conference date: 19 August 2025
Series: Applied and Computational Engineering
Volume number: Vol.175
ISSN: 2755-2721(Print) / 2755-273X(Online)