生成式AI時代用戶認知透視：基於情感分析的AI教育應用程序在數字化教學轉型中的作用評估
Unveiling User Perceptions in the Generative AI Era: A Sentiment-Driven Evaluation of AI Educational Apps' Role in Digital Transformation of e-Teaching
December 12, 2025
作者: Adeleh Mazaherian, Erfan Nourbakhsh
cs.AI
摘要
生成式人工智能在教育领域的迅速融合，正驱动着电子教学的数字化转型，然而用户对AI教育应用程式的感知仍待深入探究。本研究通过对Google Play商店热门AI教育应用的用户评论进行情感分析，评估其效能、挑战及教学意义。研究流程包括爬取应用数据与评论、使用RoBERTa进行二元情感分类、通过GPT-4o提取关键观点，并利用GPT-5整合核心正负面主题。应用程式被划分为七大类（如作业助手、数学解题工具、语言学习应用），功能重叠反映了多元整合的设计趋势。
研究结果显示整体情感以正面为主：作业类应用（如Edu AI正面评价达95.9%，Answer.AI达92.7%）在准确性、响应速度与个性化方面表现突出，而语言学习及教学管理系统类应用（如Teacher AI仅21.8%正面评价）因系统不稳定与功能局限评价偏低。正面反馈聚焦于头脑风暴、问题解决的高效性与学习参与度提升；负面批评则集中于付费墙、答案不准确、广告干扰及技术故障。趋势表明，作业助手类应用表现优于专业化工具，凸显AI在促进教育普惠性的同时，也存在助长依赖性与加剧数字鸿沟的风险。
讨论部分提出未来教育生态系统的构想：结合AI与人类的混合教学模式、利用VR/AR实现沉浸式学习，并为开发者（自适应个性化技术）和政策制定者（促进包容性的商业化规范）提供发展路线图。本研究强调生成式AI通过伦理优化推动电子教学发展的重要性，为创建公平、创新的学习环境提供实践依据。完整数据集可访问：https://github.com/erfan-nourbakhsh/GenAI-EdSent
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).