期刊名：AI

期刊主页：https://www.mdpi.com/journal/ai

生成式人工智能的快速迭代正推动教育技术从“工具辅助”向“认知协作”与“可问责中介”的范式转变。本专题汇集了发表于 AI 的系列研究，系统探讨了AI在教育多元场景中的协作机制与影响：从知识创造中的提示工程与思维链推理、知识建构型AI的认识论设计原则，到计算机科学教育中生成式AI赋能的个性化学习与代码审查；在反馈与评估层面，涵盖了LLM作为反馈提供者与专家/新手的质量比较、AI作为教师心智化训练练习伙伴的可行性；在学科教学层面，包括早期儿童教育与计算机科学本科生的比较研究、小学文学文本改编的伦理—教学验证框架；在学业表现与青少年发展层面，涉及深度学习与强化学习对大学生学业表现的评估与提升、以及AI对青少年认知、教育与行为影响的双重性分析。这些研究共同勾勒出一幅从“试点探索”走向“实践制度化”的教育AI图景，其核心张力在于：如何在释放AI个性化与规模化潜能的同时，守护学习者的认知能动性、伦理安全与教育公平。

1. Prompt Engineering for Knowledge Creation: Using Chain-of-Thought to Support Students’ Improvable Ideas

面向知识创造的提示工程：运用思维链支持学生可改进的想法

https://www.mdpi.com/2673-2688/5/3/69

Lee, A.V.Y.; Teo, C.L.; Tan, S.C. Prompt Engineering for Knowledge Creation: Using Chain-of-Thought to Support Students’ Improvable Ideas. AI 2024, 5, 1446-1461.

2. Harnessing Generative Artificial Intelligence for Digital Literacy Innovation: A Comparative Study between Early Childhood Education and Computer Science Undergraduates

利用生成式人工智能推动数字素养创新：早期儿童教育与计算机科学本科生之间的比较研究

https://www.mdpi.com/2673-2688/5/3/68

Kazanidis, I.; Pellas, N. Harnessing Generative Artificial Intelligence for Digital Literacy Innovation: A Comparative Study between Early Childhood Education and Computer Science Undergraduates. AI 2024, 5, 1427-1445.

3. The Promises and Pitfalls of Large Language Models as Feedback Providers: A Study of Prompt Engineering and the Quality of AI-Driven Feedback

大型语言模型作为反馈提供者的前景与陷阱：提示工程与AI驱动反馈质量研究

https://www.mdpi.com/2673-2688/6/2/35

Jacobsen, L.J.; Weber, K.E. The Promises and Pitfalls of Large Language Models as Feedback Providers: A Study of Prompt Engineering and the Quality of AI-Driven Feedback. AI 2025, 6, 35.

4. Deep Learning and Reinforcement Learning for Assessing and Enhancing Academic Performance in University Students: A Scoping Review

深度学习和强化学习用于评估与提升大学生学业表现：一项范围综述

https://www.mdpi.com/2673-2688/6/2/40

Stasolla, F.; Zullo, A.; Maniglio, R.; Passaro, A.; Di Gioia, M.; Curcio, E.; Martini, E. Deep Learning and Reinforcement Learning for Assessing and Enhancing Academic Performance in University Students: A Scoping Review. AI 2025, 6, 40.

5. Artificial Intelligence in Medical Education: A Narrative Review

人工智能在医学教育中的应用：一项叙述性综述

https://www.mdpi.com/2673-2688/6/12/322

Michalczak, M.; Zgoda, W.; Michalczak, J.; ??d?o, A.; Nasser, A.; Tokarek, T. Artificial Intelligence in Medical Education: A Narrative Review. AI 2025, 6, 322.

6. Designing an AI-Supported Framework for Literary Text Adaptation in Primary Classrooms

设计一个AI支持的小学课堂文学文本改编框架

https://www.mdpi.com/2673-2688/6/7/150

Chatzichristofis, S.A.; Tsopozidis, A.; Kyriakidou-Zacharoudiou, A.; Evripidou, S.; Amanatiadis, A. Designing an AI-Supported Framework for Literary Text Adaptation in Primary Classrooms. AI 2025, 6, 150.

7. From Pilots to Practices: A Scoping Review of GenAI-Enabled Personalization in Computer Science Education

从试点到实践：生成式AI赋能计算机科学教育个性化学习的范围综述

https://www.mdpi.com/2673-2688/7/1/6

Reihanian, I.; Hou, Y.; Sun, Q. From Pilots to Practices: A Scoping Review of GenAI-Enabled Personalization in Computer Science Education. AI 2026, 7, 6.

8. Epistemic Agency in the Age of Large Language Models: Design Principles for Knowledge-Building AI

大型语言模型时代的认知能动性：知识建构型AI的设计原则

https://www.mdpi.com/2673-2688/7/3/99

Woodruff, E.; Hewitt, J. Epistemic Agency in the Age of Large Language Models: Design Principles for Knowledge-Building AI. AI 2026, 7, 99.

9. Artificial Intelligence and Youth: Cognitive, Educational, and Behavioral Impacts

人工智能与青少年：认知、教育与行为影响

https://www.mdpi.com/2673-2688/7/4/121

Giansanti, D.; Cosenza, C. Artificial Intelligence and Youth: Cognitive, Educational, and Behavioral Impacts. AI 2026, 7, 121.

10. AI as a Practice Partner: A Feasibility Study of MentaClassAI, a Conversational LLM Tool for Training Educators’ Mentalizing Responses to Child Dysregulation

AI作为练习伙伴：MentaClassAI——一款用于训练教育者应对儿童失调的心智化反应对话式LLM工具的可行性研究

https://www.mdpi.com/2673-2688/7/8/309

Chelouche-Dwek, G.; Fonagy, P. AI as a Practice Partner: A Feasibility Study of MentaClassAI, a Conversational LLM Tool for Training Educators’ Mentalizing Responses to Child Dysregulation. AI 2026, 7, 309.

期刊介绍

主编：Prof. Dr. Kenji Suzuki, Tokyo Institute of Technology

AI（ISSN 2673-2688）是一个国际性、跨学科的开放获取英文学术期刊，聚焦人工智能领域的前沿研究，广泛覆盖机器学习、自然语言处理、智能机器人等所有AI子方向。期刊现已被ESCI（Web of Science）、Scopus和DOAJ等权威数据库收录，并展现出强劲的学术影响力：最新影响因子已达6.5，年增长30%，在JCR“计算机科学：跨学科应用”和“计算机科学：人工智能”两个领域中均位列Q1；CiteScore为7.3，2025年SCImago期刊排名（SJR）亦位于人工智能领域Q1。

2025 Impact Factor：6.5

2025 CiteScore：7.3

Time to First Decision：20.4 Days

Acceptance to Publication：5.6 Days