From Set Theory To Marketplace Applications Development: An Ai-Assisted Case-To-Project Learning Approach In Discrete Mathematics
DOI:
https://doi.org/10.65254/ijese.v3i4.87Keywords:
Discrete Mathematics, Set Theory, Case-to-Project Learning, Generative AI, Marketplace Application Development, Computational ThinkingAbstract
Purpose: Set Theory is a fundamental yet abstract topic in Discrete Mathematics that often challenges students in connecting mathematical concepts with real-world applications. This study proposes an AI-Assisted Case-to-Project Learning approach that transforms Set Theory learning into marketplace application development while fostering interdisciplinary and digital competencies.
Design/methodology/approach: A convergent mixed-methods design was employed involving 50 undergraduate vocational students organised into ten project teams. Students first analysed Set Theory through the Shopee marketplace, then investigated different domestic and international marketplaces before developing simplified marketplace applications using HTML, CSS, JavaScript, and Generative AI. The project integrated Programming, Database Systems, Computer Architecture, and Marketing, with Set Theory serving as the conceptual foundation. Data were collected through competency-based assessment rubrics, project documentation, and application analysis.
Findings: All project teams successfully developed functional marketplace applications featuring product catalogues, seller and buyer interactions, digital payment options, and delivery services. The findings demonstrate that students effectively translated abstract Set Theory concepts into authentic digital solutions while Generative AI supported mathematical modelling, programming, debugging, and iterative problem solving.
Research limitations/implications: The study was conducted in a single vocational higher education institution, limiting the generalisability of the findings.
Practical implications: The proposed approach provides an authentic instructional framework that connects mathematical reasoning with interdisciplinary software development and computational thinking.
Originality/value: This study introduces an AI-Assisted Case-to-Project Learning approach that positions Set Theory as the conceptual backbone of marketplace application development, enabling students to integrate mathematical reasoning, Generative AI, and interdisciplinary knowledge in solving authentic problems.
Paper type: Research paper
Downloads
References
Aboutajedyne, I., Alj, Y.S. and Aboutajeddine, A. (2021) “A Context-centered Visual Tool for the Design of Engineering Education Solutions,” ASEE Annual Conference and Exposition, Conference Proceedings. Available at: https://doi.org/10.18260/1-2--36570.
Ahmad, S.T. et al. (2023) “Project-based Learning in Vocational Education: A Bibliometric Approach,” International Journal of Modern Education and Computer Science, 15(4). Available at: https://doi.org/10.5815/ijmecs.2023.04.04.
Ajjawi, R. et al. (2024) “From authentic assessment to authenticity in assessment: broadening perspectives,” Assessment and Evaluation in Higher Education, 49(4). Available at: https://doi.org/10.1080/02602938.2023.2271193.
Balart, T. and Shryock, K.J. (2024) “A Framework for Integrating AI into Engineering Education, Empowering Human-Centered Approach for Industry 5.0,” IEEE Global Engineering Education Conference, EDUCON. Available at: https://doi.org/10.1109/EDUCON60312.2024.10578796.
Beldad, A.D. and Miedema, H.A.T. (2025) “Introducing a framework for designing an interdisciplinary engineering curriculum: educating new engineers for complex sociotechnical challenges,” European Journal of Engineering Education [Preprint]. Available at: https://doi.org/10.1080/03043797.2025.2507243.
Bîzoi, A.C. and Bîzoi, C.G. (2026) “Business ethics for industry 5.0: Creative pedagogies for human-centric futures,” International Journal of Management Education, 24(1). Available at: https://doi.org/10.1016/j.ijme.2025.101316.
Blayone, T.J.B. et al. (2018) “Ready for digital learning? A mixed-methods exploration of surveyed technology competencies and authentic performance activity,” Education and Information Technologies, 23(3). Available at: https://doi.org/10.1007/s10639-017-9662-6.
Cañavate, J., Martínez-Marroquín, E. and Colom, X. (2025) “Engineering a Sustainable Future Through the Integration of Generative AI in Engineering Education,” Sustainability (Switzerland), 17(7). Available at: https://doi.org/10.3390/su17073201.
Dias Rasteiro, D.M.L. and Thiele, K. (2024) “AI and ICT Integration in Mathematics Engineering Education: Enhancing Learning and Teaching,” SEFI 2024 - 52nd Annual Conference of the European Society for Engineering, Proceedings: Educating Responsible Engineers. Available at: https://doi.org/10.5281/zenodo.14260983.
Friesel, A. (2025) “Analyzing Strengths and Weaknesses in Using AI in Engineering Education,” Proceedings of 2025 34th Annual Conference of the European Association for Education in Electrical and Information Engineering, EAEEIE 2025. Available at: https://doi.org/10.1109/EAEEIE65428.2025.11136591.
Garcia, K.F. et al. (2025) “Engineering students’ perceptions and actual use of AI-based math tools for solving mathematical problems,” Acta Psychologica, 256. Available at: https://doi.org/10.1016/j.actpsy.2025.105004.
Goos, M., Carreira, S. and Namukasa, I.K. (2023) “Mathematics and interdisciplinary STEM education: recent developments and future directions,” ZDM - Mathematics Education, 55(7). Available at: https://doi.org/10.1007/s11858-023-01533-z.
Haas, B. et al. (2023) “Can you create? Visualising and modelling real-world mathematics with technologies in STEAM educational settings,” Current Opinion in Behavioral Sciences. Available at: https://doi.org/10.1016/j.cobeha.2023.101297.
How, M.L. and Hung, W.L.D. (2019) “Educing AI-thinking in science, technology, engineering, arts, and mathematics (STEAM) education,” Education Sciences, 9(3). Available at: https://doi.org/10.3390/educsci9030184.
Karabulut-Ilgu, A. and Jahren, C. (2016) “Evaluation of hybrid learning in a construction engineering context: A mixed-method approach,” Advances in Engineering Education, 5(3).
Katz-Buonincontro, J. (2024) “Convergent mixed methods designs,” How to mix methods: A guide to sequential, convergent, and experimental research designs. Available at: https://doi.org/10.1037/0000404-005.
Kolova, S.M. and Belkina, O. V. (2021) “Hybrid Project-Based Learning in Engineering Education,” Proceedings of the 2021 IEEE International Conference “Quality Management, Transport and Information Security, Information Technologies”, T and QM and IS 2021. Available at: https://doi.org/10.1109/ITQMIS53292.2021.9642883.
Kraft, V. and Marco-Almagro, L. (2022) “EMPOWERING MASTER STUDENTS TO SOLVE REAL-WORLD ENGINEERING PROBLEMS,” SEFI 2022 - 50th Annual Conference of the European Society for Engineering Education, Proceedings. Available at: https://doi.org/10.5821/conference-9788412322262.1432.
Lim, T. et al. (2023) “Analytics-enabled authentic assessment design approach for digital education,” Education and Information Technologies, 28(7). Available at: https://doi.org/10.1007/s10639-022-11525-3.
Liu, C., Wang, G.C. and Wang, H.F. (2025) “The Application of Artificial Intelligence in Engineering Education: A Systematic Review,” IEEE Access, 13. Available at: https://doi.org/10.1109/ACCESS.2025.3532595.
Malik, P. and Behera, S. (2024) “The Transformative Power of Experiential Learning: Bridging Theory and Practice,” The International Journal of Indian Psychology , 12(2). Available at: https://doi.org/DOI:10.25215/1202.007.
Matsunaga, H. and Miyata, K. (2026) “Extending Computer Graphics Education Through Reverse Engineering: Further Enhancements in Visual Communication and Creativity,” International Journal of Art and Design Education [Preprint]. Available at: https://doi.org/10.1111/jade.70010.
Mudinillah, A. et al. (2024) “Optimizing Project-Based Learning in Developing 21st Century Skills: A Future Education Perspective,” Qubahan Academic Journal , 4(2). Available at: https://doi.org/10.48161/qaj.v4n2a352.
Novalia, R. et al. (2025) “Project-based learning: For higher education students’ learning independence,” Social Sciences and Humanities Open, 11. Available at: https://doi.org/10.1016/j.ssaho.2025.101530.
Olusegun, S. (2015) “Constructivism Learning Theory: A Paradigm for Teaching and Learning,” IOSR Journal of Research & Method in Education Ver. I, 5(6). https://iosrjournals.org/iosr-jrme/papers/Vol-5%20Issue-6/Version-1/I05616670.pdf
Paulauskaite-Taraseviciene, A. et al. (2022) “Assessing Education for Sustainable Development in Engineering Study Programs: A Case of AI Ecosystem Creation,” Sustainability (Switzerland), 14(3). Available at: https://doi.org/10.3390/su14031702.
Quinlan, K.M., Sellei, G. and Fiorucci, W. (2025) “Educationally authentic assessment: reframing authentic assessment in relation to students’ meaningful engagement,” Teaching in Higher Education, 30(3). Available at: https://doi.org/10.1080/13562517.2024.2394042.
Ramirez-Montoya, M.S. et al. (2024) “Complex competencies for leader education: artificial intelligence analysis in student achievement profiling,” Cogent Education, 11(1). Available at: https://doi.org/10.1080/2331186X.2024.2378508.
Roach, K., Tilley, E. and Mitchell, J. (2018) “How authentic does authentic learning have to be?,” Higher Education Pedagogies, 3(1). Available at: https://doi.org/10.1080/23752696.2018.1462099.
Stefanova, T. and Georgiev, S. (2024) “POSSIBILITIES FOR USING AI IN MATHEMATICS EDUCATION,” Mathematics and Education in Mathematics, 53. Available at: https://doi.org/10.55630/mem.2024.53.117-125.
Suhendi, A., Purwarno, P. and Chairani, S. (2021) “Constructivism-Based Teaching and Learning in Indonesian Education,” KnE Social Sciences [Preprint]. Available at: https://doi.org/10.18502/kss.v5i4.8668.
Tachie-Menson, E.L. and Rosca, E.V. (2025) “Promoting Deep and Meaningful Learning for Engineering Students: The Authentic Assessment Strategy in Teaching Differential Equations,” 2025 Annual IEEE Conference on Information Communication Technology and Society, ICTAS 2025 - Proceedings. Available at: https://doi.org/10.1109/ICTAS64866.2025.11155707.
Tang, X. and Guo, H. (2024) “Factors Influencing the Professional Development of Engineering Students Under the ‘Plan for Educating and Training Outstanding Engineers,’” ECNU Review of Education, 7(3). Available at: https://doi.org/10.1177/20965311231206835.
Vidalis, S. and Subramanian, R. (2024) “Impact of AI Tools on Engineering Education.” Available at: https://doi.org/10.18260/1-2--45122.
Younas, A., Fàbregues, S. and Creswell, J.W. (2023) “Generating metainferences in mixed methods research: A worked example in convergent mixed methods designs,” Methodological Innovations, 16(3). Available at: https://doi.org/10.1177/20597991231188121.
Zhang, K. and Aslan, A.B. (2021) “AI technologies for education: Recent research & future directions,” Computers and Education: Artificial Intelligence. Available at: https://doi.org/10.1016/j.caeai.2021.100025.
Zhang, W., Wang, S. and Xu, P. (2024) “THE DEVELOPMENT OF THE SCALE FOR AUTHENTIC ENGINEERING LEARNING MODEL IN DIVERSE CONTEXTS,” SEFI 2024 - 52nd Annual Conference of the European Society for Engineering, Proceedings: Educating Responsible Engineers. Available at: https://doi.org/10.5281/zenodo.14254768.
Zheng, J.H., Lee, M.F. and Zhang, B.L. (2025) “Bridging the Gap: Aligning Vocational Graduates’ Competencies with Employer Expectations Using the KSAO Model,” International Journal of Integrated Engineering, 17(4). Available at: https://doi.org/10.30880/jtet.2025.17.04.016.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Iswanti Iswanti, Idhawati Hestiningsih, Oman Sumantri, Prihatiningsih Prihatiningsih
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
