Guided Learning with AI: A Didactic Strategy Using Sequential Tutoring via ChatGPT for Object-Oriented Programming
Keywords:
object-oriented programming; generative artificial intelligence; adaptative learning; instructional design; AI tutoring systemsAbstract
This study investigated the pedagogical effectiveness of generative artificial intelligence (AI) tools—specifically ChatGPT—in the teaching of object-oriented programming (OOP) through a structured, AI-assisted sequential tutoring strategy. The research adopted a mixed-methods experimental design to compare learning outcomes between two student groups: one that followed traditional instructional methods and another that received AI-mediated guidance through a didactic model based on progressive scaffolding. The methodology included pre- and post-tests, perception surveys, and performance evaluations across theoretical and practical tasks. Results showed that the AI-assisted approach enhanced students’ practical programming skills, engagement and perceived self-efficacy. However, theoretical content understanding remained comparable between groups, with a slight advantage observed in the control group. The study also synthesised findings from 33 peer-reviewed sources, framing the intervention within established pedagogical theories such as Bloom’s Taxonomy, constructivism, and adaptive learning. The integration of ChatGPT enabled personalised feedback, real-time error correction and metacognitive reinforcement, particularly during complex coding tasks. Findings supported the adoption of generative AI as a complementary instructional tool rather than a substitute for human teaching. Embedding AI into coherent, goal-driven didactic frameworks allowed for scalable, adaptive and engaging learning environments in computer science education. The study concluded by offering guidelines for future implementations and emphasising the need for continued research on responsible AI integration in programming instruction.
https://doi.org/10.26803/ijlter.24.7.35
References
Albayati, A. (2024). Exploring students’ acceptance of ChatGPT as a learning tool. Computers & Education, 205, Article 104870. https://doi.org/10.1016/j.compedu.2023.104870
Alonso, J., & Carrió, M. (2023). IA en la educación universitaria: desafíos éticos y didácticos [AI in university education: Ethical and didactic challenges]. Revista de Educación a Distancia [Distance Education Journal], 23(72), 1–19.
Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.
Ayala, C., & Aguilar, J. (2023). Chatbots for programming education: Opportunities and challenges. Education and Information Technologies, 28, 2493–2508. https://doi.org/10.1007/s10639-023-11610-4
Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning Analytics (pp. 61–75). Springer. https://doi.org/10.1007/978-1-4614-3305-7_4
Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K?12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48–54. https://doi.org/10.1145/1929887.1929905
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.
du Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10(21), e39630. https://doi.org/10.1016/j.heliyon.2024.e39630
Elkhodr, M., et al. (2023). Investigating the impact of ChatGPT in ICT education: A case study. Journal of Computer Assisted Learning, 39(5), 1241–1257. https://doi.org/10.1111/jcal.12786
Essel, H. B., et al. (2024). Can AI improve higher?order thinking? Evidence from ChatGPT?supported classes. Educational Technology Research and Development, 72(1), 85–104. https://doi.org/10.1007/s11423-023-10282-0
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage Publications.
Feuerriegel, S., et al. (2023). Large language models in education: Opportunities and pitfalls. Nature Human Behaviour, 7(3), 347–360. https://doi.org/10.1038/s41562-023-01578-8
Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). McGraw Hill.
Gall, M. D., Gall, J. P., & Borg, W. R. (2007). Educational research: An introduction (8th ed.). Pearson Education.
Groothuijsen, S., van den Beemt, A. A. J., Remmers, J. J. C., & van Meeuwen, L. W. (2024). AI chatbots in programming education: Students’ use in a scientific computing course and consequences for learning. Computers & Education: Artificial Intelligence, 7, 100290. https://doi.org/10.1016/j.caeai.2024.100290
Grant, M. M., & Davis, K. A. (1997). Selection and use of content reading strategies: Considerations for professional development. The Reading Teacher, 50(6), 512–515.
Husain, M. (2024). Teachers’ perspectives on generative AI in programming classes: Benefits, risks, and pedagogical guidance. Computers & Education Open, 5, 100131. https://doi.org/10.1016/j.caeo.2023.100131
IEEE. (2017). IEEE global public policy: Artificial intelligence research, development and regulation. IEEE.
INEE. (2021). Marco para la evaluación de los aprendizajes en educación media superior [Framework for assessment of learning in upper secondary education]. Instituto Nacional para la Evaluación de la Educación.
ISO. (2022). ISO/IEC 22989 Artificial intelligence concepts and terminology.
Kapakos, W. A., & Fulk, H. K. (2024). GitHub Copilot: Introducción a la herramienta de inteligencia artificial en un curso de sistemas de información [GitHub Copilot: Introduction of the AI tool in an information systems course]. Problemas de Sistemas de Información, 25(4), 106–117. https://doi.org/10.48009/4_iis_2024_108
Kasneci, E., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kestin, G., Miller, K., Klales, A., Milbourne, T., & Ponti, G. (2024). AI tutoring outperforms active learning. Research Square. https://doi.org/10.21203/rs.3.rs-4243877.v1
Lauren, P., & Watta, P. (2023). Work-in-progress: Integrating generative AI with evidence-based learning strategies in computer science and engineering education. In 2023 IEEE Frontiers in Education Conference (FIE) (pp. 1–5). IEEE. https://doi.org/10.1109/FIE58773.2023.10342970
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. https://doi.org/10.1111/j.1744 6570.1975.tb01393.x
Liao, Q., et al. (2024). Scaffolding computational thinking with AI: A design framework. ACM SIGCSE Bulletin, 56(1), 390–396. https://doi.org/10.1145/3626252.3627550
Llerena Izquierdo, M., et al. (2023). A didactic AI-driven approach to teaching programming through guided steps. IEEE Transactions on Education, 66(2), 215–223. https://doi.org/10.1109/TE.2023.3243748
Menon, P. (2023). Explorando la asistencia de GitHub Copilot para trabajar con clases en un curso de programación [Exploring GitHub Copilot assistance for working with classes in a programming course]. Problemas de Sistemas de Información [Problems of Information Systems],, 24(4), 66–81. https://doi.org/10.48009/4_iis_2023_106
Monib, W. K., Qazi, A., Apong, R. A., Azizan, M. T., De Silva, L., & Yassin, H. (2024). Generative AI and future education: A review, theoretical validation, and authors’ perspective on challenges and solutions. PeerJ Computer Science, 10, e2105. https://doi.org/10.7717/peerj-cs.2105
Msambwa, M. M., Kangwa, D., & Lianyu, C. (2024). Integration of information and communication technology in secondary education for better learning: A systematic literature review. Social Sciences & Humanities Open, 10(1), 101203. https://doi.org/10.1016/j.ssaho.2024.101203
Pintrich, P. R. (2002). The role of metacognitive knowledge in learning, teaching, and assessing. Theory into Practice, 41(4), 219–225. https://doi.org/10.1207/s15430421tip4104_3
Rahman, M. M., & Watanobe, Y. (2023). Review of ChatGPT in education: Potentials and limitations. Journal of Educational Computing Research, 61(6), 1224–1246. https://doi.org/10.1177/07356331231174866
Reiser, B. J. (2004). Scaffolding complex learning: The mechanisms of structuring and problematizing student work. Journal of the Learning Sciences, 13(3), 273–304. https://doi.org/10.1207/s15327809jls1303_2
Salvatierra, F., & Fernández Laya, N. (2024). Construir el futuro: La IA en las políticas educativas [Building the future: AI in educational policies]. IIPE–UNESCO
Sullivan, G. M., & Feinn, R. (2012). Using effect size—or why the p-value is not enough. Journal of Graduate Medical Education, 4(3), 279–282. https://doi.org/10.4300/JGME D 12 00156.1
Sun, D., Boudouaia, A., Zhu, C., & Li, Y. (2024). Would ChatGPT-facilitated programming mode impact college students’ programming behaviors, performances, and perceptions? An empirical study. International Journal of Educational Technology in Higher Education, 21, Article 14. https://doi.org/10.1186/s41239-024-00446-5
Sun, Y., et al. (2024). Effects of ChatGPT on students' programming behavior: A classroom study. Computers & Education, 208, 104902. https://doi.org/10.1016/j.compedu.2023.104902
UNESCO. (2022). Guidance for generative AI in education and research. United Nations Educational, Scientific and Cultural Organization.
UNESCO. (2023). Guidelines for the ethics of artificial intelligence in education. United Nations Educational, Scientific and Cultural Organization.
Y?lmaz, R. M., & Karao?lan, F. G. (2023). The effect of generative AI tools on programming self efficacy and motivation. Computers in Human Behavior Reports, 9, 100112. https://doi.org/10.1016/j.chbr.2023.100112
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