SearcharxivSearch

arXiv · 2608.28604

The Brand War: A Gamified AI-Feedback System for Time-Limited EFL Writing

Abstract

Writing is cognitively demanding and anxiety-provoking for English as a Foreign Language (EFL) learners, especially under time pressure. This paper presents The Brand War, a web-based gamified writing application combining competitive game mechanics with iterative GPT-4.1-powered formative feedback for undergraduate EFL learners completing a timed narrative writing task. Students role-play as marketing interns competing for a job offer, using review passes to receive AI feedback, attack opponents, or shield their own passes while drafting a 500-word brand story. We conducted an exploratory single-session classroom study with 29 university EFL students in Taiwan to examine engagement patterns, whether iterative AI feedback improved writing performance across revisions, and how AI and human scores related to overall outcomes. Students wrote within 60 minutes, using up to five AI feedback passes before a final human-graded submission. Most (65.5%) used the AI feedback system, and within-student AI scores improved modestly across revisions (M = +3.7, SD = 7.4), with larger gains among students completing more cycles and significantly higher final- versus first-review scores among multi-cycle completers (p = .032). AI-assessed and human final scores showed strong convergent validity (r = 0.722, p < .001), and AI-feedback users scored descriptively, though not significantly, higher than non-users. Students maintained a high mean focus ratio (82.4%), and competitive mechanics were used sparingly, suggesting most prioritized writing over social interference even when available. Findings suggest embedding iterative AI scoring within a competitive game context is feasible and may scaffold writing improvement, with implications for EFL writing pedagogy and AI-mediated gamified learning design.

Explore related subjects

Keep this discovery

BibTeXRIS

Jing-Yuan Huang, Vivien Lin, Yujong Park, Yi Miao, Yun-Hua Hsiao, Michael Pin-Chuan Lin, Daniel Chang, Seong Min Park, Marco Ho, Michael S. Hsiao, Jeeho Ryoo. 2026-07-04. The Brand War: A Gamified AI-Feedback System for Time-Limited EFL Writing. https://arxiv.org/abs/2608.28604

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related discoveries

From Design Principles to Prototype: A Game for Students with ADHD and Learning Disabilities Transitioning to Post-Secondary Education

Students with Attention Deficit Hyperactivity Disorder (ADHD) and Learning Disabilities (LD) can face significant academic, social, and organizational challenges when transitioning to post-secondary education. This paper presents a literature-informed serious game prototype designed to support this transition. We synthesize prior work into design considerations for students with ADHD and LD and show how these considerations are instantiated in a story-driven game.

cs.MM

BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving

Large language model (LLM) serving creates environmental impacts beyond carbon and water, including ecosystem damage through biodiversity-related pathways. We present BIRDS, a framework for Biodiversity Impact of Request-Driven LLM Serving. BIRDS defines request-level functional units, quantifies operational and embodied biodiversity impact, and introduces Quality-Normalized Biodiversity Impact (QNBI) to jointly analyze ecological impact and response quality. Across diverse workloads, models, GPUs, and regions, BIRDS reveals that biodiversity impact accumulates at scale and exposes quality-aware serving tradeoffs. The code is available at https://github.com/TianyaoShi/BIRDS.

q-bio.OT

Experts Disagree on How to Fight AI Disinformation, but Agree That Health and Politics Need Different Solutions

When 54 international experts assessed AI-generated disinformation threats, they revealed a surprising pattern: while video deepfakes received the highest average threat ratings in the political domain (M = 6.31/7), the pattern differed in the health domain, where AI-generated text received the highest average rating (M = 5.80). Experts also diverge on what to do: government regulation drew both the most "most effective" (30%) and the most "least effective" (15%) votes, though rating distributions were contested rather than polarized, indicating disagreement over priorities rather than over efficacy. These findings offer an initial expert map of an AI-disinformation landscape that is still rapidly forming.

cs.CY