Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68435
Volume:
10 |
10 |
Oct 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Department of computer science and engineering
Kings College of Engineering

Department of computer science and engineering
Kings College of Engineering

Department of computer science and engineering
Kings College of Engineering

Department of computer science and engineering
Kings College of Engineering

Department of computer science and engineering
Kings College of Engineering
Learners differ in prior knowledge, pace, interests and goals, yet most classrooms and digital courses deliver the same content to everyone. Artificial intelligence (AI) can adapt learning to the individual, but many AI-based systems are built around available data and algorithms rather than around the real needs of learners and teachers. This paper proposes the use of the design thinking process (Empathize, Define, Ideate, Prototype and Test) as a human-centred framework for developing an AI-based personalized learning system. It describes how each stage can be applied, with example research methods, personas and “How might we” questions; presents a modular system architecture together with candidate learner-modelling and recommendation techniques; outlines an illustrative learner scenario, an implementation roadmap and an evaluation plan; and discusses ethical concerns such as privacy, bias, transparency and wellbeing. The paper is conceptual: it offers a design approach and does not report experimental results. It concludes that combining design thinking with AI helps create learning systems that are useful, trusted and adopted by their users.
KEYWORDS
This paper proposed a design thinking approach for developing an AI-based personalized learning system. By empathising with learners and teachers, defining the right problem, ideating the division of work between AI and humans, prototyping early and testing repeatedly, designers can build systems that are not only technically capable but also useful, understandable and fair. Future work should implement a prototype following this framework, compare different learner-modelling techniques, and evaluate the effect on learning outcomes, engagement and fairness with real learners.
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. Bloom, B. S. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6), 4–16. Brooke, J. (1996). SUS: A quick and dirty usability scale. In P. W. Jordan et al. (Eds.), Usability evaluation in industry (pp. 189–194). Taylor & Francis. Brown, T. (2008). Design thinking. Harvard Business Review, 86(6), 84–92. Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278. Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. Dorst, K. (2011). The core of ‘design thinking’ and its application. Design Studies, 32(6), 521–532. Floridi, L., Cowls, J., Beltrametti, M., et al. (2018). AI4People: An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707. Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Sage. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher-AI complementarity. Journal of Learning Analytics, 6(2), 27–52. Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudík, M., & Wallach, H. (2019). Improving fairness in machine learning systems: What do industry practitioners need? In Proceedings of CHI 2019. ACM. Kelley, J. F. (1984). An iterative design methodology for user-friendly natural language office information applications. ACM Transactions on Office Information Systems, 2(1), 26–41. Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(1), 42–78.
Publication Date
10/28/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
1
Total views since publication
PDF Downloads
0
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...