分析用户对教育类AI应用的评价，揭示其优劣与教育变革潜力。
Unveiling User Perceptions in the Generative AI Era: A Sentiment-Driven Evaluation of AI Educational Apps' Role in Digital Transformation of e-Teaching
- 通过情感分析和大模型提取用户评论中的核心观点
- 作业类应用正向评价超90%，语言类应用仅21.8%正面评价
- 指出付费墙、错误率高是主要痛点，适合教育科技开发者参考
生成式人工智能在教育领域的快速融合推动了在线教学的数字化转型，但用户对AI教育应用的认知仍不清晰。本研究通过对Google Play商店中顶级AI教育应用的用户评论进行情感驱动评估，分析其有效性、挑战及教学意义。采用爬取数据、RoBERTa进行二分类情感分析、GPT-4o提取关键点、GPT-5整合正负面主题的方法，将应用分为七类（如作业助手、数学解题工具、语言学习工具），部分存在多功能重叠。结果显示整体以积极情绪为主，作业类应用如Edu AI（95.9%正向）、Answer.AI（92.7%正向）在准确性、速度与个性化方面表现优异；而语言类及学习管理系统类应用（如Teacher AI仅21.8%正向）因不稳定性和功能有限而评分较低。正面反馈集中于头脑风暴、解题效率与互动性提升；负面反馈则聚焦于付费门槛、内容错误、广告干扰与系统崩溃。趋势显示，通用型作业助手优于专业工具，凸显生成式AI在教育普及中的潜力，但也带来依赖风险与不平等隐患。研究提出未来应构建人机协同生态、引入虚拟现实/增强现实技术，并为开发者提供自适应个性化建议，为政策制定者提供商业化监管路径，以实现伦理化改进，推动公平创新的在线教学环境。完整数据集见：https://github.com/erfan-nourbakhsh/GenAI-EdSent。
原文摘要 · Abstract (English)
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).
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