Abstract
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 this http URL (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(this https URL).
Abstract (translated)
生成人工智能在教育领域的迅速整合推动了在线教学的数字化转型，然而用户对AI教育应用程序的看法仍鲜有研究。本研究通过分析Google Play Store上顶级AI教育应用的用户评论的情感驱动评估来评价其有效性、挑战和教学法意义。我们的流程包括抓取应用数据与评论、使用RoBERTa进行二元情感分类、利用GPT-4o提取关键点，并借助GPT-5合成主要正面/负面主题。应用程序被分为七类（如家庭作业助手、数学解题器、语言工具），重叠类别反映了多功能设计的特点。研究结果显示，总体情绪偏向积极，其中像Edu AI (95.9%的正面评价)和另一个应用(92.7%的正面评价)这样的家庭作业应用程序在准确度、速度和个人化方面表现突出；而语言/LMS应用程序（如Teacher AI仅21.8%的正面评价）则因稳定性差和功能有限而落后。正面反馈主要集中在创意生成、问题解决以及互动性方面的效率，负面反馈则聚焦于付费墙、准确性低、广告过多及技术故障等问题。趋势显示家庭作业助手类应用优于专业工具，这凸显了AI在促进教育平等化中的潜力，同时也揭示了对依赖性和不平等等风险的担忧。 讨论部分提出了未来生态系统中混合AI与人类模型的应用、VR/AR技术在沉浸式学习中的作用，并为开发者（如自适应个性化）和政策制定者（货币化监管以实现包容性）制定了路线图。这强调了生成人工智能在推进在线教学方面的角色，通过促进伦理改进来创造公平且创新的学习环境。完整的数据集可在此处获取(该链接应替换为您提供的具体网址)。
URL
https://arxiv.org/abs/2512.11934
