Sep 2026· Jurnal Pendidikan Progresif· 0 citations· 32 references
Abstract
This research describes the implementation and evaluates the effectiveness of an Artificial Intelligence (AI)-based digital gamification strategy, the 'Level Up Numeracy' program, in revitalizing high school students’ multiplication skills. The study specifically aimed to measure improvements in computational efficiency and explore students' motivational and emotional responses to the intervention. The study used a pre-experimental, one-group pretest-posttest design with a cohort of 47 tenth-grade students at SMAN 4 Cibinong. The intervention was a multi-level educational game developed with Canva AI, featuring leaderboards, badges, and instant feedback. Data were collected through automated time tracking of student performance across two sessions (pretest and posttest) and a qualitative reflective questionnaire validated using Lawshe's Content Validity Ratio (CVR = 1.00). Quantitative data were analyzed using disaggregated paired-samples t-tests and two-way cross-tabulation, while qualitative feedback was examined through thematic analysis using Braun and Clarke's framework. The intervention yielded a statistically significant improvement in students' computational speed. The average task completion time decreased from 812.62 seconds (SD = 295.51) in the pretest to 512.55 seconds (SD = 226.43) in the posttest, t(46) = 10.62, p < .001. This represents a 36.92 % increase in cognitive efficiency. Qualitatively, 87.2% of students reported highly positive emotional shifts, describing the gamified experience as motivating, challenging, and engaging, which fostered a competitive yet supportive learning environment. AI-based digital gamification proves to be a highly effective, scalable, and cost-efficient strategy for enhancing fundamental numeracy skills and learning engagement among high school students. Generative AI empowers educators to create customized, interactive learning tools, offering a promising avenue for addressing foundational learning gaps in various educational contexts. Keywords: numeracy, digital gamification, artificial intelligence, mathematics learning, Canva AI.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
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A novel threat is unveiled in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base, enabling the attacker to steer the response without altering the user input or modifying the RAG weights.
Jiaqi Xue, Meng Zheng, Yebowen Hu et al.· arXiv.org· 109 citations· ⚡8
This work revisits schema linking when using the latest generation of large language models (LLMs) and finds empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones.
Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz et al.· arXiv.org· 109 citations· ⚡19
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
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