生成式AI时代用户认知透视：基于情感分析的AI教育应用在数字化教学转型中的作用评估

Unveiling User Perceptions in the Generative AI Era: A Sentiment-Driven Evaluation of AI Educational Apps' Role in Digital Transformation of e-Teaching

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快速融合正推动电子教学的数字化转型，但用户对AI教育应用的认知仍待深入探索。本研究通过对Google Play商店头部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。研究流程包括采集应用数据与评论、使用RoBERTa进行二元情感分类、GPT-4o提取关键观点、GPT-5综合正负面主题。应用被划分为七类（如作业助手、数学解题工具、语言学习应用），多功能设计导致类型存在交叉。结果显示用户情感以积极为主，其中作业类应用（如Edu AI积极率95.9%、Answer.AI达92.7%）在准确性、响应速度与个性化方面领先，而语言/LMS类应用（如Teacher AI积极率仅21.8%）因系统不稳定和功能局限表现不佳。积极评价聚焦于头脑风暴、问题解决和互动参与的高效性；负面反馈则集中于付费墙、答案错误、广告干扰及技术故障。趋势表明作业助手类应用优于专业化工具，凸显AI在促进教育普惠性的同时存在依赖性与公平性风险。讨论提出未来应发展人机协同教学模式、结合VR/AR实现沉浸式学习，并为开发者（自适应个性化）和政策制定者（保障包容性的盈利机制监管）提供路线图。这印证了生成式AI通过伦理优化推动公平创新环境，进而促进电子教学发展的关键作用。完整数据集详见：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).