EcoLife：AI駆動の個別化インサイトを備えた学生生活管理とカーボンフットプリント追跡の統合型ゲーミフィケーション・モバイルアプリ
EcoLife: A Gamified Mobile Application for Integrated Student Lifestyle Management and Carbon Footprint Tracking with AI-Driven Personalised Insights (原題)
Pragyan Kumar, Mannan Jain, C. M. Sharma
🤖 gxceed AI 要約
日本語
本論文は、学生の睡眠・食事・運動・学習・スクリーンタイムを統合管理しつつ、移動・食・電力の炭素排出を可視化するモバイルアプリEcoLifeを提案する。教師あり機械学習とルールベース推薦を組み合わせたAI個別化エンジンと、健康的・環境配慮行動にポイントを付与するゲーミフィケーションを特徴とする。設計段階であり実験結果は未提示だが、要件・アーキテクチャ・炭素会計モデル・MLパイプライン・プライバシー配慮・実装計画を体系的に示す。
English
EcoLife is a cross-platform mobile app integrating student lifestyle tracking (sleep, diet, exercise, study, screen time) with a carbon calculator for transport, food and energy. It combines supervised ML with rule-based recommendations for personalised insights and a gamified reward system for healthy, eco-friendly actions. The paper details requirements, architecture, carbon accounting model, ML pipeline, privacy and a four-phase implementation plan, though it remains at the design stage with no experimental results.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
個人レベルの炭素可視化と行動変容をAIで支援する試みは、Scope 3の下流（消費者・従業員）排出把握やESG教育の文脈で日本企業・大学にも示唆がある。ただし設計段階の提案であり、実証データは今後の課題。
In the global GX context
Individual-level carbon tracking with AI-driven nudges connects to downstream Scope 3 consumer/employee emissions and climate education, an emerging area in ISSB/CSRD disclosure ecosystems. As a design-stage proposal it offers a reference architecture rather than validated evidence.
👥 読者別の含意
🔬研究者:AIとゲーミフィケーションを組み合わせた個人炭素会計の設計例として、行動変容研究の出発点になる。
🏢実務担当者:従業員・学生向けの排出可視化やサステナビリティ教育ツールの設計に応用できる。
🏛政策担当者:個人レベルの炭素可視化・気候教育政策の参考になるが、効果検証は未実施。
📄 Abstract（原文）
College students often have irregular sleep schedules, poor eating habits, low physical activity and too much screen time. Many are also unaware of the carbon emissions linked to their daily travel, food choices and electricity use. Most existing apps focus on these issues separately. Fitness and habit trackers focus on health, while carbon calculators do not consider students' health or academic routines. This paper presents EcoLife, a cross-platform mobile application with four main modules: (i) a lifestyle tracker for sleep, diet, exercise, study and screen time; (ii) a carbon emission calculator for transport, food and energy; (iii) an AI-based personalised insights engine that combines supervised machine learning with rule-based recommendations; and (iv) a gamification system where users earn points by completing healthy and eco-friendly tasks. These points can be exchanged inside the app for non-material rewards such as streak freezes, task-skip passes and break tokens. The system uses Flutter, Node.js/Express, MongoDB, Firebase and a Python scikit-learn service. It also uses the Carbon Interface API to obtain standardised emission factors. EcoLife is aligned with United Nations Sustainable Development Goals 3 (Good Health and Well-being), 4 (Quality Education) and 13 (Climate Action). This paper explains the system requirements, architecture, data model, carbon accounting model, machine learning pipeline, reward system, privacy considerations and four-phase implementation plan. It also proposes a pilot evaluation protocol. The project is currently at the design stage, so no experimental results are claimed.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.55041/ijsrem68284first seen 2026-10-26 04:51:45 · last seen 2026-10-31 05:06:39
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