Liu, Z., & Guo, R., & Jiao, X., & Gao, X., & Oh, H., & Xing, W. (2024, June), How AI Assisted K-12 Computer Science Education: A Systematic Review  Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--47532

\bibitem{asee_peer_47532}
  Liu, Z., \& Guo, R., \& Jiao, X., \& Gao, X., \& Oh, H., \& Xing, W. (2024, June), \emph{How AI Assisted K-12 Computer Science Education: A Systematic Review}
  Paper presented at 2024 ASEE Annual Conference \& Exposition, Portland, Oregon.
  10.18260/1-2--47532

Zifeng Liu, Rui Guo, Xinyue Jiao, Xueyan Gao, Hyunju Oh, and Wanli Xing.  "How AI Assisted K-12 Computer Science Education: A Systematic Review".  2024 ASEE Annual Conference & Exposition, Portland, Oregon, 2024, June.  ASEE Conferences, 2024.  https://peer.asee.org/47532 Internet.  10 May, 2025

\bibitem{asee_peer_47532}
  Zifeng Liu, Rui Guo, Xinyue Jiao, Xueyan Gao, Hyunju Oh, and Wanli Xing.
  "How AI Assisted K-12 Computer Science Education: A Systematic Review".
  \emph{2024 ASEE Annual Conference \& Exposition, Portland, Oregon, 2024, June}.
  ASEE Conferences, 2024.
  https://peer.asee.org/47532 Internet.  10 May, 2025

@INPROCEEDINGS{asee_peer_47532
author = "Zifeng Liu, Rui Guo, Xinyue Jiao, Xueyan Gao, Hyunju Oh, and Wanli Xing"
title = "How AI Assisted K-12 Computer Science Education: A Systematic Review"
booktitle = "2024 ASEE Annual Conference \& Exposition"
year = "2024"
month = "June"
address = "Portland, Oregon"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/47532}
number = {10.18260/1-2--47532}
}

TY  - CPAPER
AB  - Although computational thinking is critical in education, not only to enhance students' problem-solving and logical thinking skills but also to broaden their creativity and understanding of systems design, challenges such as inadequate educational resources, lack of teaching experience, and abstract nature of programming principles continue to hinder the promotion and implementation of high-quality computer science (CS) education. Artificial intelligence (AI) holds promise in addressing these issues. Yet, the specific impact of AI on K-12 CS education has to be discussed. Existing reviews have focused on the broad spectrum of AI applications in education, with relatively little focus on topics related to CS education and programming instruction, with most of these studies focusing on a single type of AI, such as automated evaluation systems or visual programming, and failing to fully cover the various categories of AI, including machine learning, deep learning, and robotics, especially in the K-12 field.
The primary goal of this study is to conduct a systematic review of the current literature concerning the role, impact, and constraints of AI in CS education, with a specific focus on K-12 education. The review process follows the PRISMA principle. A total of 24 articles published between 2013 and 2023 were selected, comprehensively reviewed, and analyzed. The coding scheme mainly includes four aspects: (1) Research background, (2) Research design, (3) AI technologies, and (4) Research outcomes and limitations. Each aspect contains specific dimensions to be coded. 
The study discovered that AI plays a significant role in K-12 CS as learning content and developing programming platforms. These adaptive learning platforms give personalized programming education and real-time feedback, relieving teachers' workload while giving students personalized curricular information tailored to their needs. Additionally, AI is usually used as a data analytics tool to predict student performance. The reviewed articles focus on AI's cognitive and affective impact on students and found positive effects on those variables. At the same time, AI allows for better analysis and utilization of data on student behavior while programming. Limitations in the current reviewed articles on AI in K-12 CS education include insufficient attention to theoretical adoption, ethical concerns, and methodological issues like small sample sizes. This review highlights the critical role of AI in K-12 CS education and illuminates directions for a more personalized, interactive, and practical learning experience in K-12 CS education in the future.

AU  - Zifeng Liu
AU  - Rui Guo
AU  - Xinyue Jiao
AU  - Xueyan Gao
AU  - Hyunju Oh
AU  - Wanli Xing
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - How AI Assisted K-12 Computer Science Education: A Systematic Review
UR  - https://peer.asee.org/47532
DO  - 10.18260/1-2--47532
ER  -