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The Role of Artificial Intelligence in computer science Education: A Systematic Review with a Focus on Database Instruction

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APPLIED scienceS-BASEL 2025年第7期15卷 3960-3960页

作者： Gaitantzi, Alkmini Kazanidis, Ioannis Democritus Univ Thrace Dept Informat Kavala 65404 Greece

The integration of artificial intelligence (AI) into computer science (CS) education is evolving, yet its specific application in database instruction remains underexplored. This systematic review analyzes 31 empirical studies published between 2020 and 2025, examining how AI applications support teaching and learning in CS, with an emphasis on database education. Following the PRISMA methodology, the review categorizes AI applications according to instructional design models, roles, actions, benefits, and challenges. Findings indicate that AI tools, particularly chatbots, intelligent tutoring systems, and code generators, effectively support personalized instruction, immediate feedback, and interactive problem-solving across CS and database-specific contexts. However, challenges persist, including AI inaccuracies, biases, student dependency in AI, and academic integrity risks. The review also identifies a shift in programming education as AI reshapes software development practices, prompting a need to align curricula with evolving industry expectations. Despite growing attention to AI applications in programming education, database-related research remains limited. This review highlights the necessity for further empirical investigations specifically in database instruction, including more extensive studies addressing AI-driven pedagogical strategies and their long-term impacts. The results suggest that careful integration of AI tools can complement traditional instruction, emphasizing the critical role of human educators in achieving meaningful and effective learning outcomes.

关键词： artificial intelligence generative AI ChatGPT computer science education programming education database instruction AI in education systematic review

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Effect of jigsaw-integrated task-driven learning on students' motivation, computational thinking, collaborative skills, and programming performance in a high-school programming course

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computer APPLICATIONS IN ENGINEERING EDUCATION 2024年第6期32卷

作者： Zhan, Zehui Li, Tingting Ye, Yaner South China Normal Univ Sch Informat Technol Educ Dept Educ Technol Guangzhou 510631 Peoples R China Univ Hong Kong Sch Educ Dept Educ Technol Hong Kong Peoples R China

computer programming has emerged as an important field in K-12 science, technology, engineering, and maths (STEM) education in the AI era. However, contemporary programming education is hindered by fragmented course content, high complexity, and difficulties in maintaining engagement, impeding smooth progress. More effective collaborative learning strategies need to be explored. This study constructed jigsaw-integrated task-driven learning (jigsaw-TDL) in a high school Python programming course under a STEM curriculum and verified its teaching effectiveness on students' learning motivation, computational thinking, collaborative skills, and programming performance both quantitatively and qualitatively. Nighty-nine high school students were randomly assigned to a jigsaw-TDL group and a general collaborative task-driven learning group (collaborative-TDL). During the experiment, a Python programming course was introduced over 7 weeks. Questionnaires, programming tasks, and semistructured interviews were comprehensively applied to examine students' learning outcomes. Finally, the jigsaw-TDL group showed significantly better performance than the collaborative-TDL group in learning motivation, computational thinking, and collaborative skills. However, it only led to better programming performance in the less complex tasks. The majority of students held a positive attitude toward the jigsaw-TDL model, acknowledging its benefits in group collaboration, programming knowledge acquisition, and application. This research provides empirical evidence and potential guidance for task organization and collaborative learning support in programming courses and STEM education.

关键词： collaborative learning high school jigsaw integrated task-driven learning programming education

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A Coding/programming Academy for 6th-Grade Females to Increase Knowledge and Interest in computer science 49

A Coding/Programming Academy for 6th-Grade Females to Increa...

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49th IEEE Frontiers in Education Conference (FIE)

作者： Wang, Sujing Andrei, Stefan Urbina, Otilia Sisk, Dorothy A. Lamar Univ Dept Comp Sci Beaumont TX 77710 USA Lamar Univ Dept Profess Pedag Beaumont TX 77710 USA Lamar Univ Dept Teacher Educ Beaumont TX 77710 USA

ISBN: (纸本)9781728117461

This Innovative Practice Full Paper discusses a coding/programming academy that used games and robotic programming as engaging hands-on approaches to teach 6thgrade (the first grade in secondary education in USA) females coding/programming concepts to increase their knowledge and interest in computer science. Careers in computer science continue to grow, but fewer women than men are even considering these careers. Increasing participation of women in coding/programming is necessary to meet the growing demand for computing professionals to develop a diverse workforce. Today, many organizations are implementing programming coding/programming academies/camps that attempt to engage students in computer science, at an early age, by exposing them to fun and interesting computer science skills in coding/programming. We have developed a coding/programming academy that uses educational robotics and hands-on game applications to demonstrate computing concepts to young females. To address pressing equity issues of the lack of females in computer science careers, the goal of this summer coding/programming academy was to educate and empower young females, at an early age, to discover computer science careers, which has been one of the first attempts to establish a coding/programming academy for females in our region. Our coding/programming academy differs from others in several ways. First, it was for 6th-grade females only, to take advantage of preferences of noncompetitive and social learning opportunities, in order to improve female participation. Second, we hired female instructors and invited female professionals from local industries to assist the academy by serving as mentors. Third, it introduced both robotic and game coding/programming to the females. Fourth, it adopted social learning, e.g., pair programming. A formal assessment of the 2018 academy found that the academy's female participants experienced a significant increase in knowledge and interest in computer scie

关键词： Coding/programming academy increasing female participation in computer science robotic programming game programming

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Design Recommendations for Using Textual Aids in Data-science programming Courses 2022

Design Recommendations for Using Textual Aids in Data-Scienc...

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53rd Annual ACM SIGCSE Technical Symposium on computer science Education (SIGCSE)

作者： Choi, Heeryung Mills, Caitlin Brooks, Christopher Doherty, Stephen Singh, Anjali Univ Michigan Ann Arbor MI 48109 USA Univ New Hampshire Durham NH USA Univ New South Wales Sydney NSW Australia

ISBN: (纸本)9781450390705

Despite a recent shift towards online learning, recommendations for multimedia design principles in programming-based instruction remain unclear. Specifically, how can we teach people to code, a text-heavy medium, properly in online instruction? This question is especially important since the text-based format of screencasts may interact with psychological mechanisms known to affect cognitive processing and learning. We investigate this question, and find that previous results from other domains do not necessarily hold in the programming education. We also explore how design changes in textual aids affect learners' performance in programming-based multimedia learning. Our results suggest that the redundancy effect does not significantly hinder learning, which conflicts with previous findings, and that the spatial contiguity effect occurs even between textual components. This work contributes to an evidence-based understanding of how to design more effective multimedia learning environments for programming-based instruction.

关键词： redundancy effect spatial contiguity effect multimedia learning

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Analysis of Sentiment Toward computer science in Pre-University Education

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IEEE ACCESS 2024年 12卷 71205-71218页

作者： Herrero-Alvarez, Rafael Callejas-Castro, Enrique Miranda, Gara Leon, Coromoto Univ Laguna Dept Ingn Informat & Sistemas San Cristobal de La Lagun 38200 Spain Univ Int La Rioja Fac Educ Logrono 26006 Spain

Emotions are essential drivers of the learning process, influencing motivation, performance, and problem-solving abilities. In the field of computer science, students often struggle with negative emotions during programming activities, impacting their performance and project quality. To address this challenge, there is a growing need to introduce Computational Thinking skills at the pre-university level. This study focuses on understanding the emotions experienced by primary and secondary school students during Computational Thinking activities, involving both plugged and unplugged tools. A significant performance difference was observed between primary and secondary education levels, with the latter outperforming the former. The research identifies specific associations between concepts and emotions, highlighting age-related differences with younger students exhibiting more positive emotions. While gender-based disparities in computer science perception exist in secondary education, there are no corresponding distinctions in emotional responses. The study reveals a gender-based effect, with girls showing reduced emotional responses and lower Computational Thinking performance than boys. In summary, this research underscores the profound role of emotions in learning, providing essential insights for tailoring educational strategies considering gender-specific and programming concept-related factors. It also connects lower emotional reactions to inferior results, emphasizing the importance of heightened emotional engagement.

关键词： Education programming profession Training Phase measurement Sorting Shape Instruments computer science computational thinking primary education secondary education gender studies

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Investigating the Role of Errors in programming Learning 29

Investigating the Role of Errors in Programming Learning

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29th Annual Conference on Innovation and Technology in computer science Education (ITiCSE)

作者： Sha, Alyssa Shuang Nunes, Bernardo Pereira Shen, Jiayi Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9798400706035

computer science students often perceive programming errors as weaknesses rather than learning opportunities, resulting in frustration, anxiety, and dropout. This study investigates how early programming errors can contribute to students' subsequent learning. We hypothesise that a certain number of errors made by students in the initial learning stage, which is distinct across different learning concepts, can enhance their future learning. Our hypothesis is tested using a real-world dataset consisting of 601 programming questions that were practised by 632 students. Additionally, we conduct a comprehensive clustering analysis on the error curves of students, revealing optimal error thresholds in various programming concepts. This approach brings novel perspectives on the significant role of "errors" in computer science education.

关键词： programming errors curve analysis

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Hip, Hip, Array: Teaching programming for Data science is the same as computer science-Just Different. 9

Hip, Hip, Array: Teaching Programming for Data Science is th...

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9th IEEE Integrated STEM Education Conference (ISEC)

作者： Buzydlowski, Jan W. Holy Family Univ Philadelphia PA 19114 USA

Teaching an introductory programming class to a data science major needs to be done in a different manner than with a computer science major. This paper will focus on the major differences between the two majors regar... 详细信息

ISBN: (纸本)9781728115023

关键词： data science introductory programming

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Learner stimulus intent: a framework for eye tracking data collection and feature extraction in computer programming education

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SCIENTIFIC REPORTS 2025年第1期15卷 1-19页

作者： Chandrika, K. R. Amudha, J. Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Bengaluru India

Eye tracking technology offers valuable insights into the cognitive processes of learners in computer programming education. This research presents a novel framework called the Learner Stimulus Intent that offers useful insights into learners' cognitive processes in computer programming education and has significant implications for assessment in computer science education. The comprehensive data collection, extraction of eye gaze and semantic features, and effective visualization techniques can be utilized to evaluate students' understanding and engagement, offering a more nuanced and detailed picture of their learning progress than traditional assessment methods. Furthermore, the four distinct datasets generated by the framework each offers unique perspectives on learner behavior and their cognitive traits. These datasets are outcomes of the framework's application, embodying its potential to revolutionize the way we understand and assess learning in computer science education. By utilizing this framework, educators and researchers can gain deeper insights into the cognitive processes of learners like cognitive workload, processing order of information, confusion in mind, attention etc, ultimately enhancing instructional strategies and improving learner outcomes.

关键词： Framework Data collection Eye tracking Semantic computer education

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A Bibliometric Exposition and Review on Leveraging LLMs for programming Education

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IEEE ACCESS 2025年 13卷 58364-58393页

作者： Amos, Joanah Pwanedo Amodu, Oluwatosin Ahmed Mahmood, Raja Azlina Raja Abdulqudus, Akanbi Bolakale Zakaria, Anies Faziehan Iyanda, Abimbola Rhoda Bukar, Umar Ali Mohd Hanapi, Zurina Elizade Univ Informat & Commun Engn Dept Ilara Mokin 340271 Nigeria Univ Putra Malaysia Dept Commun Technol & Network Serdang 43400 Selangor Malaysia Elizade Univ Dept Math & Comp Sci Ilara Mokin 340271 Nigeria Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Engn Educ Bangi 43600 Malaysia Obafemi Awolowo Univ Comp Sci & Engn Dept Ife 220005 Nigeria Taraba State Univ Fac Comp & Artificial Intelligence Dept Comp Sci Jalingo 660213 Nigeria Multimedia Univ Fac Informat Sci & Technol Ctr Intelligent Cloud Comp CICC Melaka 75450 Malaysia

The world is experiencing an AI revolution, with large language models (LLMs) transforming various industries, including education. Academics are striving to harness the potential of LLMs while also contending with their risks. This paper presents the first bibliometric analysis focused on LLM research in programming education, identifying leading countries, authors, and institutions while analyzing key terms and popular keywords in this field. Additionally, it highlights influential studies on topics such as introductory programming, computer science, computing, programming education, and prompt engineering, discussing key insights from these works. Findings indicate that LLMs could play a significant role in programming education and may be integrated into computer science curricula. However, careful consideration is needed to ensure their benefits outweigh their risks across various use cases. This study specifically examines ChatGPT as a representative LLM, exploring its benefits and limitations as both a learning aid for students and a support tool for professionals. It also evaluates the quality of ChatGPT-generated code and its effectiveness in simplifying programming concepts for beginners. Furthermore, the ethical implications of increasing reliance on LLMs for programming tasks, including concerns about dependency, plagiarism, and potential effects on critical thinking, are addressed. By contributing to the ongoing discourse on integrating AI tools like ChatGPT in programming education, this research emphasizes the importance of responsible and ethical usage to maximize benefits for students, educators, and the broader educational community.

关键词： Chatbots programming profession Education Bibliometrics Market research Large language models Codes Ethics Requirements engineering Mathematics ChatGPT code generation ethical concerns large language models (LLMs) introductory programming programming education prompt engineering

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Source Code Error Understanding Using BERT for Multi-Label Classification

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IEEE ACCESS 2025年 13卷 3802-3822页

作者： Amin, Md Faizul Ibne Watanobe, Yutaka Rahman, Md Mostafizer Shirafuji, Atsushi Univ Aizu Grad Dept Comp & Informat Syst Aizu Wakamatsu Fukushima 9658580 Japan Tulane Univ Dept Comp Sci New Orleans LA 70118 USA

programming is an essential skill in computer science and across a wide range of engineering disciplines. However, errors, often referred to as 'bugs' in code, can be challenging to identify and rectify for both students learning to program and experienced professionals. Understanding, identifying, and effectively addressing these errors are critical aspects of programming education and software development. To aid in understanding and classifying these errors, we propose a multi-label error classification approach for source code using fine-tuned BERT models (BERT_Uncased and BERT_Cased). The models achieved average classification accuracies of 90.58% and 90.80%, exact match accuracies of 48.28% and 49.13%, and weighted F1 scores of 0.796 and 0.799, respectively. Precision, Recall, Hamming Loss, and ROC-AUC metrics further evaluate the effectiveness of our models. Additionally, we employed several combinations of large language models (CodeT5, CodeBERT) with machine learning classifiers (Decision Tree, Random Forest, Ensemble Learning, ML-KNN), demonstrating the superiority of our proposed approach. These findings highlight the potential of multi-label error classification to advance programming education, software engineering, and related research fields.

关键词： programming programming profession Codes Multi label classification Accuracy Education Transformers Measurement computer bugs Data models Multi-label classification BERT CodeT5 CodeBERT decision tree random forest ML-KNN ensemble learning data analysis educational big data error classification learning analytics programming learning software engineering

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