揭示生成式AI时代的用户认知：基于情感驱动的AI教育应用在e教学数字化转型中的作用评估
Unveiling User Perceptions in the Generative AI Era: A Sentiment-Driven Evaluation of AI Educational Apps' Role in Digital Transformation of e-Teaching
AI总结：
本研究通过分析用户评论，评估AI教育应用在e教学数字化转型中的作用，揭示其积极与消极影响，并提出未来发展的方向。
摘要：
生成式人工智能快速融入教育领域，推动了e教学的数字化转型，但用户对AI教育应用的看法仍缺乏深入研究。本研究通过分析Google Play Store上顶级AI教育应用的用户评论，进行基于情感驱动的评估，以评估其有效性、挑战及教学意义。我们的流程包括刮取应用数据和评论，使用RoBERTa进行二元情感分类，GPT-4o提取关键点，GPT-5综合出最积极和消极的主题。应用被分为七类（例如，作业助手、数学求解器、语言工具），重叠反映多功能设计。结果显示，整体情绪偏向积极，作业类应用如Edu AI（95.9%积极）和Answer.AI（92.7%积极）在准确性、速度和个性化方面领先，而语言/LMS类应用（如Teacher AI，21.8%积极）由于不稳定和功能有限而落后。积极评价强调在头脑风暴、问题解决和参与方面的效率；消极评价集中在付费墙、不准确、广告和故障上。趋势显示作业助手优于专用工具，突显了AI在民主化潜力方面的潜力，同时突显了依赖性和不平等的风险。讨论提出未来生态系统，包括混合AI-人类模型、VR/AR用于沉浸式学习，以及开发者（自适应个性化）和政策制定者（监管货币化以促进包容性）的路线图。这强调了生成式AI在推进e教学中的作用，通过促进伦理改进，营造公平、创新的环境。完整数据集可通过此处获取（https://github.com/erfan-nourbakhsh/GenAI-EdSent）
英文摘要：
The rapid integration of generative artificial intelligence into education has driven digital transformation in e-teaching, yet user perceptions of AI educational apps remain underexplored. This study performs a sentiment-driven evaluation of user reviews from top AI ed-apps on the Google Play Store to assess efficacy, challenges, and pedagogical implications. Our pipeline involved scraping app data and reviews, RoBERTa for binary sentiment classification, GPT-4o for key point extraction, and GPT-5 for synthesizing top positive/negative themes. Apps were categorized into seven types (e.g., homework helpers, math solvers, language tools), with overlaps reflecting multifunctional designs. Results indicate predominantly positive sentiments, with homework apps like Edu AI (95.9% positive) and Answer.AI (92.7%) leading in accuracy, speed, and personalization, while language/LMS apps (e.g., Teacher AI at 21.8% positive) lag due to instability and limited features. Positives emphasize efficiency in brainstorming, problem-solving, and engagement; negatives center on paywalls, inaccuracies, ads, and glitches. Trends show that homework helpers outperform specialized tools, highlighting AI's democratizing potential amid risks of dependency and inequity. The discussion proposes future ecosystems with hybrid AI-human models, VR/AR for immersive learning, and a roadmap for developers (adaptive personalization) and policymakers (monetization regulation for inclusivity). This underscores generative AI's role in advancing e-teaching by enabling ethical refinements that foster equitable, innovative environments. The full dataset is available here(https://github.com/erfan-nourbakhsh/GenAI-EdSent).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
