The effectiveness of ChatGPT in assisting high school students in programming learning: Evidence from a quasi-experimental research
Open accessAbstract
Programming education gains importance in high schools as the digital age progresses. However, the openness and adaptability of programming languages present unique challenges for instructional practices compared to other subjects. While traditional instructional tools offer limited support, ChatGPT, a groundbreaking Generative Artificial Intelligence, has shown impressive capabilities in natural language processing and knowledge generation. This study explored whether ChatGPT can transcend existing limitations and improve programming education through a quasi-experimental approach with post-hoc interviews in high school classrooms. A total of 153 students participated, and the results from MANCOVA and ANCOVA analyses revealed that students using ChatGPT reported lower levels of flow experience, self-efficacy, and learning achievement compared to those utilizing conventional methods. Post-hoc interviews further revealed that students felt ChatGPT's effectiveness in facilitating their programming learning fell short of their initial expectations. These findings highlight the need to carefully consider the complexity of programming learning tasks and students' cognitive, affective, and interactive dimensions when integrating AI technologies into education. We discuss the implications and provide thorough pedagogical strategies, specifically the guidance–practice–transformation (G–P–T) mode, to maximize the potential of AI tools and support high school programming education, emphasizing the balance of technological innovation with learning best practices.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Some studies document confusion, frustration, and other negative emotions driven by mistrust, technical limits, or unmet expectations, and even declines in self-efficacy.
“Some studies document confusion, frustration, and other negative emotions driven by mistrust, technical limits, or unmet expectations [78,87,93,98], and even declines in self-efficacy [140].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [140]
Cites
Self-efficacy: The exercise of control
Bandura, A., Freeman, W. H., & Lightsey, R. (1999) · Journal of Cognitive Psychotherapy · doi:10.1891/0889-8391.13.2.158 · Cited by 46,835 · see citing works
Kallio, H., Pietilä, A., Johnson, M., & Kangasniemi, M. (2016) · Journal of Advanced Nursing · doi:10.1111/jan.13031 · Cited by 4,295 · see citing works
Lye, S. Y., & Koh, J. H. L. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.09.012 · Cited by 1,421 · see citing works
Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence
Cooper, G. (2023) · Journal of Science Education and Technology · doi:10.1007/s10956-023-10039-y · Cited by 1,263 · see citing works
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24750 · Cited by 919 · see citing works
Chatbots applications in education: A systematic review
Okonkwo, C. W., & Ade-Ibijola, A. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100033 · Cited by 879 · see citing works
Assessing computational thinking: A systematic review of empirical studies
Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020) · Computers & Education · doi:10.1016/j.compedu.2019.103798 · Cited by 647 · see citing works
Abstracts written by ChatGPT fool scientists
Else, H. (2023) · Nature · doi:10.1038/d41586-023-00056-7 · Cited by 611 · see citing works
ChatGPT for (Finance) research: The Bananarama Conjecture
Dowling, M., & Lucey, B. M. (2023) · Finance research letters · doi:10.1016/j.frl.2023.103662 · Cited by 611 · see citing works
Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., & Ghazi Saeedi, M. (2021) · Interactive Learning Environments · doi:10.1080/10494820.2018.1558257 · Cited by 605 · see citing works
Ibáñez, M. B., Serio, Á. D., Villarán, D., & Kloos, C. D. (2013) · Computers & Education · doi:10.1016/j.compedu.2013.09.004 · Cited by 591 · see citing works
Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse?
O’Connor, S., & ChatGPT (2023) · Nurse Education in Practice · doi:10.1016/j.nepr.2022.103537 · Cited by 585 · see citing works
Do <scp>AI</scp> chatbots improve students learning outcomes? Evidence from a meta‐analysis
Wu, R., & Yu, Z. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13334 · Cited by 576 · cited 2× · see citing works
“As previous studies suggest, real-time feedback can help students quickly identify their errors and adjust their strategies, thus improving learning efficiency and promoting self-correction that in turn boosts their self-efficacy (Wu & Yu, 2023).”
Dale, R. (2020) · Natural Language Engineering · doi:10.1017/s1351324920000601 · Cited by 461 · see citing works
A context-aware ubiquitous learning environment for conducting complex science experiments
Hwang, G., Yang, T., Tsai, C., & Yang, S. J. H. (2009) · Computers & Education · doi:10.1016/j.compedu.2009.02.016 · Cited by 435 · see citing works
Self-citationA Systematic Literature Review on Teaching and Learning Introductory Programming in Higher Education
Medeiros, R. P., Ramalho, G. L., & Falcão, T. P. (2018) · IEEE Transactions on Education · doi:10.1109/te.2018.2864133 · Cited by 389 · see citing works
Liu, M., Zhang, L. J., & Biebricher, C. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104977 · Cited by 372 · see citing works
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2023.100005 · Cited by 344 · see citing works
Computational thinking education: Issues and challenges
Angeli, C., & Giannakos, M. N. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.106185 · Cited by 343 · see citing works
Tikva, C., & Tambouris, E. (2021) · Computers & Education · doi:10.1016/j.compedu.2020.104083 · Cited by 296 · see citing works
Data-Driven Hint Generation in Vast Solution Spaces: a Self-Improving Python Programming Tutor
Rivers, K., & Koedinger, K. R. (2015) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0070-z · Cited by 258 · see citing works
Computational thinking development through creative programming in higher education
Romero, M., Lepage, A., & Lille, B. (2017) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-017-0080-z · Cited by 250 · see citing works
Can ChatGPT draft a research article? An example of population-level vaccine effectiveness analysis
Macdonald, C., Adeloye, D., Sheikh, A., & Rudan, I. (2023) · Journal of Global Health · doi:10.7189/jogh.13.01003 · Cited by 243 · see citing works
Exploring the computational thinking effects in pre-university education
García‐Peñalvo, F. J., & Mendes, A. J. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.12.005 · Cited by 228 · see citing works
Computational thinking in programming with Scratch in primary schools: A systematic review
Fagerlund, J., Häkkinen, P., Vesisenaho, M., & Viiri, J. (2020) · Computer Applications in Engineering Education · doi:10.1002/cae.22255 · Cited by 223 · see citing works
Why Not Robot Teachers: Artificial Intelligence for Addressing Teacher Shortage
Edwards, B. I., & Cheok, A. D. (2018) · Applied Artificial Intelligence · doi:10.1080/08839514.2018.1464286 · Cited by 220 · see citing works
Profiles of the sources of science self-efficacy
Chen, J., & Usher, E. L. (2012) · Learning and Individual Differences · doi:10.1016/j.lindif.2012.11.002 · Cited by 210 · see citing works
Wang, S., & Lin, S. S. J. (2006) · Computers in Human Behavior · doi:10.1016/j.chb.2006.03.005 · Cited by 195 · see citing works
The ebb and flow of online learning
Pearce, J., Ainley, M., & Howard, S. (2005) · Computers in Human Behavior · doi:10.1016/s0747-5632(04)00036-6 · Cited by 191 · see citing works
Psycharis, S., & Kallia, M. (2017) · Instructional Science · doi:10.1007/s11251-017-9421-5 · Cited by 188 · see citing works
Sun, D., Boudouaia, A., Zhu, C., & Li, Y. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00446-5 · Cited by 187 · see citing works
Chatbot application in a 5th grade science course
Deveci Topal, A., Dilek Eren, C., & Kolburan Geçer, A. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10627-8 · Cited by 182 · see citing works
ChatGPT: Challenges and Benefits in Software Programming for Higher Education
da Silva, C. A. G., Ramos, F. N., de Moraes, R. V., & dos Santos, E. L. (2024) · Sustainability · doi:10.3390/su16031245 · Cited by 179 · see citing works
Tsai, C. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.11.038 · Cited by 166 · see citing works
Sun, L., Hu, L., & Zhou, D. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104457 · Cited by 144 · see citing works
Yükseltürk, E., & Altıok, S. (2016) · British Journal of Educational Technology · doi:10.1111/bjet.12453 · Cited by 128 · see citing works
A comparison of lecture-based, active, and flipped classroom teaching approaches in higher education
Kay, R. H., MacDonald, T., & DiGiuseppe, M. (2018) · Journal of Computing in Higher Education · doi:10.1007/s12528-018-9197-x · Cited by 119 · see citing works
Scaffolding Computational Thinking With ChatGPT
Liao, J., Zhong, L., Zhe, L., Xu, H., Liu, M., & Xie, T. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3392896 · Cited by 113 · see citing works
Exploring ChatGPT for information of cardiopulmonary resuscitation
Ahn, C. (2023) · Resuscitation · doi:10.1016/j.resuscitation.2023.109729 · Cited by 112 · see citing works
Pereira, F. D., Oliveira, E., de Oliveira, D. B. F., Cristea, A. I., de Carvalho, L. S. G., Fonseca, S. C., Toda, A. M., & Isotani, S. (2020) · British Journal of Educational Technology · doi:10.1111/bjet.12953 · Cited by 102 · see citing works
Assessing Algorithmic and Computational Thinking in K-12: Lessons from a Middle School Classroom
Grover, S. (2017) · Springer eBooks · doi:10.1007/978-3-319-52691-1_17 · Cited by 89 · see citing works
Lai, X., & Wong, G. K. W. (2021) · British Journal of Educational Technology · doi:10.1111/bjet.13157 · Cited by 87 · see citing works
Durak, H. Y. (2018) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12281 · Cited by 85 · see citing works
İnce, E. Y., & Koç, M. (2020) · Computer Applications in Engineering Education · doi:10.1002/cae.22321 · Cited by 83 · see citing works
Jing, Y., Wang, H., Chen, X., & Wang, C. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-02751-w · Cited by 82 · see citing works
Ullah, Z., Lajis, A., Jamjoom, M. M., Altalhi, A. H., Al‐Ghamdi, A., & Saleem, F. (2018) · Computer Applications in Engineering Education · doi:10.1002/cae.21974 · Cited by 80 · see citing works
An affective and Web 3.0-based learning environment for a programming language
Cabada, R. Z., Estrada, M. L. B., González-Hernández, F., Bustillos, R. O., & Reyes-Garcia, C. A. (2017) · Telematics and Informatics · doi:10.1016/j.tele.2017.03.005 · Cited by 72 · see citing works
Liao, C., & Wu, J. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104754 · Cited by 71 · see citing works
Self-citationEffective measures to foster girls’ interest in secondary computer science education
Happe, J., Bühnová, B., Koziolek, A., & Wagner, I. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10379-x · Cited by 68 · see citing works
Challenges for enforcing editorial policies on AI-generated papers
Hu, G.-W. (2023) · Accountability in Research · doi:10.1080/08989621.2023.2184262 · Cited by 64 · see citing works
Litts, B. K., Kafai, Y. B., Lui, D., Walker, J. T., & Widman, S. A. (2017) · Journal of Science Education and Technology · doi:10.1007/s10956-017-9694-0 · Cited by 63 · see citing works
Koenka, A. C., & Anderman, E. M. (2019) · Middle School Journal · doi:10.1080/00940771.2019.1674768 · Cited by 63 · see citing works
Sun, J. C., & Hsu, K. Y.-C. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.01.036 · Cited by 62 · see citing works
JAICOB: A Data Science Chatbot
Carlander-Reuterfelt, D., Carrera, Á., Iglesias, C. Á., Araque, Ó., Sánchez-Rada, J. F., & Muñoz, S. (2020) · IEEE Access · doi:10.1109/access.2020.3024795 · Cited by 58 · see citing works
ChatGPT: temptations of progress
Doshi, R. H., Bajaj, S. S., & Krumholz, H. M. (2023) · The American Journal of Bioethics · doi:10.1080/15265161.2023.2180110 · Cited by 58 · see citing works
Daradoumis, Τ., Puig, J. M. M., Arguedas, M., & Calvet, L. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.09.021 · Cited by 57 · see citing works
Madariaga, L., Allendes, C., Nussbaum, M., Barrios, G., & Acevedo, N. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104664 · Cited by 57 · see citing works
Computer Programming Effects in Elementary: Perceptions and Career Aspirations in STEM
Tran, Y. K. (2018) · Technology Knowledge and Learning · doi:10.1007/s10758-018-9358-z · Cited by 56 · see citing works
Increasing Academic Self-Efficacy in Statistics With a Live Vicarious Experience Presentation
Bartsch, R. A., Case, K. A., & Meerman, H. (2012) · Teaching of Psychology · doi:10.1177/0098628312437699 · Cited by 53 · see citing works
Wu, J. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.104066 · Cited by 52 · see citing works
Self-citationKnight, V. F., Wright, J., Wilson, K. J., & Hooper, A. (2019) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-019-04033-w · Cited by 49 · see citing works
Wei, L., Liu, C., & Tseng, J. C. R. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11671-2 · Cited by 49 · see citing works
Designing an intelligent tutoring system for computer programing in the Pacific
Sharma, P., & Harkishan, M. (2022) · Education and Information Technologies · doi:10.1007/s10639-021-10882-9 · Cited by 47 · see citing works
Zinbarg, R., Suzuki, S., Uliaszek, A. A., & Lewis, A. R. (2010) · Journal of Abnormal Psychology · doi:10.1037/a0017552 · Cited by 46 · see citing works
Milana, M., Brandi, U., Hodge, S., & Hoggan-Kloubert, T. (2024) · International Journal of Lifelong Education · doi:10.1080/02601370.2024.2310448 · Cited by 46 · see citing works
Sharma, K., Torrado, J. C., Gómez, J., & Jaccheri, L. (2020) · Entertainment Computing · doi:10.1016/j.entcom.2020.100387 · Cited by 44 · see citing works
Recommender Systems and Scratch: An Integrated Approach for Enhancing Computer Programming Learning
Cobo, J., Puris, A., Hernández, P. N., Galindo, J. Á., & Benavides, D. (2019) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2019.2901457 · Cited by 43 · see citing works
A visual programming environment for introducing distributed computing to secondary education
Broll, B., Lédeczi, Á., Zare, H., Nguyen, D. M., Sallai, J., Völgyesi, P., Maróti, M., Brown, L. R., & Vanags, C. (2018) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2018.02.021 · Cited by 40 · see citing works
Fields, D. A., Kafai, Y. B., Morales‐Navarro, L., & Walker, J. T. (2021) · British Journal of Educational Technology · doi:10.1111/bjet.13079 · Cited by 40 · see citing works
Impact of Practical Skills on Academic Performance: A Data-Driven Analysis
Rahman, M. M., Watanobe, Y., Kiran, R. U., Thang, T. C., & Paik, I. (2021) · IEEE Access · doi:10.1109/access.2021.3119145 · Cited by 40 · see citing works
How difficult are exams?: a framework for assessing the complexity of introductory programming exams
Sheard, J., Simon, N. P., Carbone, A., Chinn, D. D., Clear, T., Corney, M., D’Souza, D., Fenwick, J., Harland, J., Laakso, M., et al. (2013) · QUT ePrints (Queensland University of Technology) · doi:10.5555/2667199.2667215 · Cited by 38 · see citing works
Automatic assessment of Java code
Insa, D., & Silva, J. (2018) · Computer Languages · doi:10.1016/j.cl.2018.01.004 · Cited by 38 · see citing works
Ou, Q., Liang, W., He, Z., Liu, X., Yang, R., & Wu, X. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e15530 · Cited by 34 · see citing works
Meta-analysis on effects of artificial intelligence education in K-12 South Korean classrooms
Lee, D., & Kwon, H. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12738-4 · Cited by 34 · see citing works
Sun, J. C., & Lin, H.-S. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104430 · Cited by 30 · see citing works
Ünal, A., & Topu, F. B. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10536-w · Cited by 27 · see citing works
Tsai, C.-W., Lin, M. Y.-C., Cheng, Y.-P., Lee, L.-Y., Chyr, W.-L., Lin, C.-H., Lin, J.-W., & Tsai, M.-C. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104849 · Cited by 25 · cited 1× · see citing works
“…insights into the experiences and perceptions of participants in using ChatGPT for programming learning and provided supplementary evidence for the quantitative results, thereby enabling us to comprehend the effects of using ChatGPT in programming education (Liao et al., 2024; Tsai et al., 2023).”
Does ChatGPT Help Novice Programmers Write Better Code? Results From Static Code Analysis
Haindl, P., & Weinberger, G. (2024) · IEEE Access · doi:10.1109/access.2024.3445432 · Cited by 25 · cited 1× · see citing works
“When recent research (Haindl & Weinberger, 2024) highlighted the necessity of considering the learning experience when attempting to explain the impact of ChatGPT on learning programming, the results of our research offers some insight.”
Cheng, L., Wei, L., & Tseng, J. C. R. (2021) · Interactive Learning Environments · doi:10.1080/10494820.2021.2006237 · Cited by 24 · see citing works
Jiang, H., Chugh, R., Turnbull, D., Wang, X., & Chen, S. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11277-0 · Cited by 24 · see citing works
Lai, R. P., & Ellefson, M. R. (2022) · Journal of Educational Computing Research · doi:10.1177/07356331221121052 · Cited by 24 · see citing works
The Development of Computational Thinking in a High School Chemistry Course
Matsumoto, P. S., & Cao, J. (2017) · Journal of Chemical Education · doi:10.1021/acs.jchemed.6b00973 · Cited by 22 · see citing works
A review on process-oriented approaches for analyzing novice solutions to programming problems
Villamor, M. M. (2020) · Research and Practice in Technology Enhanced Learning · doi:10.1186/s41039-020-00130-y · Cited by 21 · see citing works
The impact of chatbots using concept maps on correction outcomes–a case study of programming courses
Kuo, Y., & Chen, Y. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11506-6 · Cited by 21 · see citing works
Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence
Rico-Juan, J. R., Sánchez-Cartagena, V. M., Valero-Mas, J. J., & Gallego, A. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3239110 · Cited by 21 · see citing works
Chung, C.-C., & Lou, S. (2021) · Applied Sciences · doi:10.3390/app11041830 · Cited by 20 · see citing works
Coşkunserçe, O. (2023) · Computer Applications in Engineering Education · doi:10.1002/cae.22637 · Cited by 20 · see citing works
Science Self-Efficacy and Lifelong Learning: Emerging Adults in Science Museums
Gutwill, J. P. (2018) · Visitor Studies · doi:10.1080/10645578.2018.1503875 · Cited by 19 · see citing works
Wu, T., & Chen, J. (2021) · Journal of Educational Computing Research · doi:10.1177/07356331211039961 · Cited by 18 · see citing works
Wu, J., Yang, C. C. Y., Liao, C., & Nian, M.-W. (2021) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 17 · see citing works
Self-citationKong, S. C., & Lin, T. J. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104598 · Cited by 17 · see citing works
The Effect of Question Order on Evaluations of Test Performance: Can the Bias Dissolve?
Bard, G., & Weinstein, Y. (2016) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2016.1225108 · Cited by 15 · see citing works
Daramsenge, B., & Hsueh, N. (2021) · IEEE Access · doi:10.1109/access.2021.3126896 · Cited by 14 · see citing works
Wheel-Spinning Models in a Novice Programming Context
Flores, R. M., & Rodrigo, M. M. T. (2020) · Journal of Educational Computing Research · doi:10.1177/0735633120906063 · Cited by 12 · see citing works
Investigating the effects of a programming course using flipped learning
Chiu, P., Zhong, H.-X., & Lai, C.-F. (2022) · Innovations in Education and Teaching International · doi:10.1080/14703297.2022.2080097 · Cited by 11 · see citing works
Teaching High School Students Artificial Intelligence by Programming Chatbots
Nayak, J., Keane, T., Linden, T., & Molnar, A. (2023) · Springer eBooks · doi:10.1007/978-3-031-21970-2_17 · Cited by 10 · see citing works
Coding Across the Curriculum: Challenges for Non-specialist Teachers
Woo, K., & Falloon, G. (2023) · Springer eBooks · doi:10.1007/978-3-031-21970-2_16 · Cited by 9 · see citing works
Why Is Teaching Programming Difficult?
McDonald, C. (2018) · Springer eBooks · doi:10.1007/978-3-319-98590-9_6 · Cited by 6 · see citing works
Prediction versus production for teaching computer programming
Tucker, M. C., Wang, X., Son, J. Y., & Stigler, J. W. (2024) · Learning and Instruction · doi:10.1016/j.learninstruc.2023.101871 · Cited by 6 · see citing works
Investigation of Continuous Assessment of Correctness in Introductory Programming
Ferreira, D. J., da Silva, H. C., Melo, T. F. N., & Ambrosio, L. (2017) · Educational Technology & Society · Cited by 3 · see citing works
High school pre-engineering students’ engineering design cognition
Lammi, M., Wells, J. G., & Gero, J. S. (2020) · International Journal of Technology and Design Education · doi:10.1007/s10798-020-09614-w · Cited by 3 · see citing works
Development of CNN-Based Data Crawler to Support Learning Block Programming
Park, H., Kim, J., & Lee, W.-G. (2022) · Mathematics · doi:10.3390/math10132223 · Cited by 3 · see citing works
A Programming Disposition Scale for High School Students
Hu, C.-F., Lin, Y., Wu, C., & Chen, H. (2022) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 104 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Wang, J., & Fan, W. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04787-y · Cited by 322 · see citing works
RetractedGenerative AI use in K-12 education: a systematic review
Alfarwan, A. M. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1647573 · Cited by 131 · see citing works
Vallejo, U. C., ROMANI, G., Inca, K. S. M., Zegel, I. D. E. S., Loza, G. E. S., Gonzaga, U. N. S. L., FRANCO, A. M., Gonzaga, U. N. S. L., Choque, M. K. O., & Gonzaga, U. N. S. L. (2025) · ESPACIOS · doi:10.48082/espacios-a25v46n03p02 · Cited by 32 · see citing works
ChatGPT as a cognitive crutch: Evidence from a randomized controlled trial on knowledge retention
Barcaui, A. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.102287 · Cited by 51 · see citing works
Tao, S., Lan, M., Wang, M., & Li, H. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100561 · Cited by 31 · see citing works
Lin, X., & Tan, H. (2025) · Systems · doi:10.3390/systems13100840 · Cited by 22 · see citing works
The Impact of AI on Learners’ Self-Efficacy: A Meta-Analysis
Ren, L., Stephens, J. M., & Lee, K. M. (2026) · Behavioral Sciences · doi:10.3390/bs16010158 · Cited by 22 · see citing works
Raghu Raman, R., Achuthan, K., & Nedungadi, P. (2026) · Information · doi:10.3390/info17030241 · Cited by 15 · see citing works
Can Learners’ Use of GenAI Enhance Learning Engagement?—A Meta-Analysis
Wang, K., & Guo, Z. X. X. Z. P. (2025) · Education Sciences · doi:10.3390/educsci15121578 · Cited by 12 · see citing works
Stoyanova, D., Stoyanova-Petrova, S. V., Shotarova, S., Lyubomirov, S. Y., & Mileva, N. S. (2025) · Education Sciences · doi:10.3390/educsci16010019 · Cited by 12 · see citing works
Zeng, S., Rahim, N., & Xu, S. (2025) · IEEE Access · doi:10.1109/access.2025.3560021 · Cited by 10 · see citing works
Xu, J., Sun, D., Feng, Y., Shadiev, R., & Li, Y. (2026) · International Journal of STEM Education · doi:10.1186/s40594-026-00606-1 · Cited by 6 · see citing works
A Model for Computational Thinking Development in GeoGebra with the Support of ChatGPT
Yunianto, W., Galiç, S., Lavicza, Z., & Urhan, S. (2025) · Computers in the Schools · doi:10.1080/07380569.2025.2544062 · Cited by 5 · see citing works
Deng, H., Chen, H., & Dong, Y. (2026) · Journal of Educational Computing Research · doi:10.1177/07356331261424211 · Cited by 5 · see citing works
Demiralay, R. (2025) · The journal of academic social science studies/The journal of academic social science · doi:10.29228/jasss.80950 · Cited by 5 · see citing works
AI chatbots in higher education: Comparing expectations to evidence
Thoeni, A., & Fryer, L. K. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101061 · Cited by 4 · see citing works
Generative AI use and academic achievement: a cross-sectional study of Czech seventh-grade students
Lintner, T., Juhaňák, L., Žáková, M., Plch, L., & Ropovik, I. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14002-3 · Cited by 4 · see citing works
Meta-analysis on the influence of AI agents on K-12 student cognitive performance
Liu, J., Mbowe, A. E., Tahri, D., & Aziku, M. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.100973 · Cited by 3 · see citing works
Hussein, M. H., Aldeeb, F. H. A., Sulaiman, N. L., & Elaish, M. M. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70226 · Cited by 3 · see citing works
The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI
Oakley, B. A., Johnston, M. O., Chen, K.-Z., Jung, E., & B, T. J. S. A. (2026) · Springer eBooks · doi:10.1007/978-3-032-11814-1_22 · Cited by 3 · see citing works
Chakraborty, P. (2026) · Computer Applications in Engineering Education · doi:10.1002/cae.70223 · Cited by 3 · see citing works
Hasan, M. T., Jin, H. H., Mahmood, M., Hasan, M. T., Sarkar, J. B., Salleh, N. M., & Chowdhury, A. Y. (2025) · Multidisciplinary Reviews · doi:10.31893/multirev.2025338 · Cited by 3 · see citing works
Józsa, G., Oo, T. Z., Vallent, B., & Józsa, K. (2026) · Education Sciences · doi:10.3390/educsci16020240 · Cited by 2 · see citing works
Purnajaya, A. R. (2026) · Jurnal Teknologi informasi dan Ilmu Komputer · doi:10.65258/jutekom.v2.i2.52 · Cited by 2 · see citing works
Emotional Reliance on Generative AI Among Vocational High School Students: An AEDTAM-Based Analysis
Yao, K.-C., Liang, J.-W., Chiang, S., & Chang, S.-H. (2026) · Sustainability · doi:10.3390/su18105148 · Cited by 2 · see citing works
Sousa, S. A., Bukhari, S. S. U. H., Tang, M., Man, Y. K., Hobi, S., & Steagall, P. V. M. (2026) · Journal of Veterinary Medical Education · doi:10.3138/jvme-2025-0198 · Cited by 2 · see citing works
Deckker, D., & Sumanasekara, S. (2025) · doi:10.36227/techrxiv.174742826.67489697/v1 · Cited by 2 · see citing works
The Memory Paradox: Why Our Brains Need Knowledge in an Age of AIPreprint
Oakley, B. A., Johnston, M. O., Chen, K.-Z., Jung, E., & B, T. J. S. A. (2025) · doi:10.31234/osf.io/3xye5_v3 · Cited by 2 · see citing works
Transforming Learning or Empty Promise? A Meta-Analysis of Generative AI in EducationPreprint
Tang, X., Wang, X., Dong, L., & Zhang, J. C. (2025) · Research Square · doi:10.21203/rs.3.rs-7577394/v1 · Cited by 2 · see citing works
R, N. K., M, J., & B G, S. (2026) · Journal of Research in Innovative Teaching & Learning · doi:10.1108/jrit-08-2025-0202 · Cited by 1 · see citing works
Lu, J. (2025) · International Conference on Education, Knowledge and Information Management (ICEKIM) · doi:10.1145/3756580.3756630 · Cited by 1 · see citing works
류진솔, & Kim, K. (2026) · The Journal of Korean Association of Computer Education · doi:10.32431/kace.2026.29.3.002 · Cited by 1 · see citing works
How Large Language Models Shape Programming Skill Development Beyond Task Completion
Orehovački, T. (2026) · AI · doi:10.3390/ai7070252 · Cited by 1 · see citing works
Cheng, Y., Hsiao, I., & Starčič, A. I. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98197-5_30 · Cited by 1 · see citing works
ProtectTech: a ChatGPT generated scenario activity to foster student decision-making capability
Allison, J. (2025) · Journal of Further and Higher Education · doi:10.1080/0309877x.2025.2583091 · Cited by 1 · see citing works
Is ChatGPT detrimental to innovation? A field experiment among university students
Hassan, M., Amin, E., Mansour, S., & Kelani, Z. (2026) · Seikei ronsō/Review of economics and political science · doi:10.1108/reps-08-2024-0045 · Cited by 1 · see citing works
Addiucci, L., & Temperini, M. (2026) · Journal of Information Technology Education Research · doi:10.28945/5773 · Cited by 1 · see citing works
Chen, P., Ma, Z., Liang, Y., & Lu, H. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2677726 · Cited by 1 · see citing works
A scoping review of artificial intelligence literacy in secondary education
Ghosh, R., Gardia, A., & Singha, J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01408-8 · Cited by 1 · see citing works
Lin, X., Li, S., Zhu, Y., Ju, H., & Ding, G. (2026) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-026-00632-7 · Cited by 1 · see citing works
Banquez-Maturana, A.-G. (2026) · Journal of Trends and Challenges in Artificial Intelligence · doi:10.61552/jai.2026.03.002 · Cited by 1 · see citing works
Maheux, A. J., & Maes, C. (2026) · Perspectives on Psychological Science · doi:10.1177/17456916261460771 · Cited by 1 · see citing works
Wu, M., & Ouyang, F. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10211-x
The Memory Paradox: Why Our Brains Need Knowledge in an Age of AIPreprint
Oakley, B. A., Johnston, M. O., Chen, K.-Z., Jung, E., & B, T. J. S. A. (2025) · doi:10.31234/osf.io/3xye5_v2
Lin, K. Y., Cheng, P., & Chen, L. (2025) · doi:10.36227/techrxiv.174910010.00866598/v1
Impact of AI Debugging on Student Code Complexity and Readability: A Literature Review
Thang, H. V., & Vi, T. L. (2025) · International STEM Education Conference (iSTEM-Ed) · doi:10.1109/istem-ed65612.2025.11129285
Zahirah, K. F., & Yusuf, E. (2025) · EDUTECH Jurnal Inovasi Pendidikan Berbantuan Teknologi · doi:10.51878/edutech.v5i4.8077
Redefining Student Engagement in the Age of AI
Hınız, G. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7569-4.ch007
Kokubun, K., Yamakawa, Y., Yoshida, A., & Sanji, S. (2025) · Brain Sciences · doi:10.3390/brainsci16010058
Exploring Communication Literacy Levels and ChatGPT Usage Among Senior High School Students
Fidel, D. M. C., Pangilinan, J. C., Dator, M. E. M., Coronel, M. R. D., & Tutor, B. O. D. (2025) · International Workshop on Artificial Intelligence and Education (WAIE) · doi:10.1109/waie67422.2025.11381146
Are LLMs Magic for Learning? The Impact of LLM-Support on the Development of Programming SkillsPreprint
YILDIRIM, D., & Keskin, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6314977
Nguyen, P. D. A. (2025) · Journal of Thu Dau Mot University · doi:10.37550/tdmu.ejs/2025.04.686
Contreras, A. U., & Alonso, R. F. (2026) · doi:10.36006/16458-1
Integrating GenAI Tools Into Software Engineering Education
Simić, M., Jolović, M., Naumović, T., Lukovac, P., & Despotovic-Zrakic, M. S. (2026) · Computer Applications in Engineering Education · doi:10.1002/cae.70177
Faizal, M. S. B. M., & Osman, K. (2026) · International Journal of Modern Education · doi:10.35631/ijmoe.829041
Yao, K.-C., Chiang, S., Yen, F.-C., & Lin, W. Y. (2026) · Research Square · doi:10.21203/rs.3.rs-9155865/v1
Li, J., Pang, X., Song, Y., Liu, Y., Ye, J., & Fan, X. (2026) · International Conference on AI in Education Technology and Applications (AIETA) · doi:10.1109/aieta69357.2026.11508953
Alzahrani, A., & Rafi, A. S. M. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2683034
Yazılım Mühendisliği Eğitiminde Üretken Yapay Zekânın Bibliyometrik Analiz Yöntemi ile İncelenmesi
Baz, F. Ç., & Keleş, Ş. (2026) · Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.47495/okufbed.1766554
He, Z., Pang, P. C., Siu, K.-M., & Ou, Q. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29763-1_4
Branthôme, M., Kirouchenassamy, B., Lallé, S., Jolivet, S., Muratet, M., & Yessad, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29763-1_17
Herrera-Carrión, Á. P., Hernández, N. M. H., Cedillo, M. V. E., & Portero, S. Y. R. (2026) · Ciencias y Saberes · doi:10.59602/cys.v4i2.73
Xu, L., Chen, S. H., Su, L.-F., Hwang, G., Wu, H.-H., Liu, H., & Wu, W. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14078-x
Tsou, P.-H., Ou, S.-J., & Chien, Y.-C. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-23104-8_27
Marnika, D., Kovačić, B., & Marković, M. G. (2026) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro70003.2026.11591986
Wyk, R. J. V., & Chaipah, K. (2026) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse68839.2026.11597033
Setiyawan, A., Soeharto, S., Wijaya, T. T., & Lavicza, Z. (2026) · Technology Knowledge and Learning · doi:10.1007/s10758-026-10016-5
Cheng, Y., Guo, Y.-J., Lee, P.-H., & Starčič, A. I. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-32112-1_16
Behavioral Drivers of ChatGPT-assisted TOEIC Reading: Evidence from an Extended UTAUT Model
Chiang, Y.-H., Tseng, H.-P., Lu, C.-H., & Huang, T. (2026) · International Journal of Information and Education Technology · doi:10.18178/ijiet.2026.16.7.2659
From tutor to test: A new framework for AI literacy that preserves critical thinking
Kentz, M., & Skidmore, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36775-5.00007-2
ChatGPT as scaffold: quiz performance across session complexity
Kabba, F. E., & Ejbari, Z. (2026) · International Journal of Evaluation and Research in Education (IJERE) · doi:10.11591/ijere.v15i4.39915
Gupta, S., Dewangan, R. L., Longkumer, I., & Pathak, S. (2026) · Romanian Journal of Applied Psychology · doi:10.2478/rjap-2026-0008
A Meta-Analysis of the Impact of GAI-Based Programming Teaching on Students' Computational Thinking
Yiting, S., Yuan, T., Liu, B., Zhuang, X., & Liu, B. (2026) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste69562.2026.11649597
Ding, Y., Liu, X., He, L., Zhao, Z., & Jia, P. (2026) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste69562.2026.11649578
Omarbekova, A., Miłosz, M., Bekmanova, G., Rakymkan, Y., Nazyrova, A., & Lamasheva, Z. (2026) · Education Sciences · doi:10.3390/educsci16091382
An AI-Driven Platform for Learning Diverse IoT Protocols: Integrating Theory and Practice
Zhu, Z., Funabiki, N., Kyaw, H. H. S., & Kao, W.-C. (2026) · International Conference on Computer Communication and the Internet (ICCCI) · doi:10.1109/iccci70321.2026.11666517
Sarma, N. (2026) · Asian Journal of Education and Social Studies · doi:10.9734/ajess/2026/v52i93314
GENERATIVE AI REDUCES PERIPHERAL BUT NOT MENTAL COGNITIVE LOAD IN PROGRAMMING EDUCATION
Аркабаев, Н. К., Ойчуева, Р., & Ajibekova, A. (2026) · Jurnal Ilmiah Ilmu Terapan Universitas Jambi|JIITUJ| · doi:10.22437/jiituj.v10i4.58071
Shi, Y., Zheng, Y., Wei, Y., Ji, X., & Ke, Z. (2026) · Creativity Research Journal · doi:10.1080/10400419.2026.2726242
Generative AI, algorithmic subjectivation and the conditions of thought
Hamamra, B. (2026) · Subjectivity · doi:10.1057/s41286-026-00248-2
Lee, S., Beom, S., Kang, A., Lee, J.-E., & Choe, H. (2026) · The Journal of Korean Association of Computer Education · doi:10.32431/kace.2026.29.8.008
Hawari, M. F., Putri, A. M., Yantama, A. A., & Syahsudarmi, S. (2026) · Business System & Innovation Journal · doi:10.67955/bsij.v1i2.19
Adini, M. H., Purba, H. S., Wiranda, N., Ardhabilly, M., & Adawiyah, R. (2026) · Jurnal Teknologi Informasi dan Pendidikan · doi:10.24036/jtip.v19i2.1088
Zhao, Q., & Li, S. (2026) · Behavioral Sciences · doi:10.3390/bs16091626
Lyu, M., Wang, C., Cheng, M., & Ji, F. (2026) · Journal of Educational Computing Research · doi:10.1177/07356331261487830
The Impact of Generative AI on Self-Efficacy: Evidence From a Meta-Analysis
Heshmati, H., Li, A., & Tullis, J. G. (2026) · Journal of Educational Computing Research · doi:10.1177/07356331261483943
Fuhrmann, T. R., Sotério, C., & Blikstein, P. (2026) · Proceedings. · doi:10.22318/icls2026.505618
Ramaila, S. M. (2026) · Eurasia Journal of Mathematics Science and Technology Education · doi:10.29333/ejmste/19478
LLM-Enabled Educational Artefacts in Computer Science Education: A Systematic Mapping Study
Sanchez, F. L., Alario‐Hoyos, C., Guamán, D. S., & Caiza, J. C. (2026) · Applied Sciences · doi:10.3390/app16199778
Incorporación de la IA generativa en el bachillerato: perspectiva de la NEM y el modelo TPACK
Mendoza, K. K. R., & Cetzal, R. S. P. (2026) · doi:10.61728/ae26002163
Tang, Y., & Wu, L. (2026) · Journal of Intelligence · doi:10.3390/jintelligence14100241
GENERATIVE AI AVAILABILITY AND METACOGNITIVE CALIBRATION: EVIDENCE FROM HIGHER EDUCATION
(2026) · Issues in Information Systems · doi:10.48009/1_iis_2026_109
Huang, S. Y. B. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2744897
Al‐Barakat, A. A., AlAli, R. M., Aboras, A., Alribdi, N. I., Abdullatif, A. K. A., & Zaher, A. M. N. (2026) · Scientific Reports · doi:10.1038/s41598-026-73410-0
Mendoza, F. E. M. (2026) · Aglafía Revista Interdisciplinaria de Educación Tecnología Ciencias Sociales y Humanidades · doi:10.66769/agla.22
Generative AI and Academic Well-Being of Senior Secondary Students: An Integrative Review
Singh, K., & Gautam, N. (2026) · Vagisha International Multidisciplinary Research Journal · doi:10.67185/vimrj.v1i4.34
Khodjaev, B. K., Masalimova, A. R., Chistyakov, A. A., & Ay, Y. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1971476
Beimel, D., Amzalag, M., Zviel-Girshin, R., & Voloch, N. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14178-8
Troussas, C., Papakostas, C., Krouska, A., Phivos Mylonas, B., & Sgouropoulou, C. (2026) · International Journal of Intelligent Systems · doi:10.1155/int/9444834
- Authors
- Yang, T.-C., Hsu, Y.-C., & Wu, J.-Y.
- Affiliations
- National Yang Ming Chiao Tung University (TW)
- Tainan First Senior High School (TW)
- Southern Methodist University (US)
- Corresponding author
- Yang, Tzu-Chi
- Venue
- Interactive Learning Environments
- Publisher
- Informa UK Limited
- Published
- 2025-01-16
- Volume
- 33
- Issue
- 6
- Pages
- 3726-3743
- ISSN
- 1049-4820 (ISSN-L)
- 1744-5191
- OpenAlex
- W4406474705
- Cited by
- 80
- References
- 104
- Citations by year
- 2026: 63 · 2025: 17
- FWCI
- 71.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Interactive Learning Environments (publishedVersion, cc-by)
- Funders
- National Science and Technology Council (111-2410-H-A49-066-MY3)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- ChatGPT, generative AI, programming education, high school students, flow experience, self-efficacy, academic performance, artificial intelligence in education, student perceptions of AI tools
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
