Published in: Journal of Computer Science and Engineering in Innovations and Research (ISSN No: 3049-1762 online)
Publication Date: June 15, 2025
Problem-solving is an significant skill to experimental today's complex, dynamic, and uncertain surroundings. Computational Thinking was introduced by Wing in 2006 signifies a organized approach to solving problems in stages such as decomposition, pattern recognition, abstraction, and algorithmic thinking. Despite of growing interest in research, the connection between CT and problem-solving remains inadequately articulated. The systematic literature review on 37 peer reviewed articles recognized through the Web of Science database reflects that CT has been frequently discussed in the context of problem solving and its stages resemble well with traditional problem-solving frameworks. Developments in AI, especially Deep Learning, further illustrates computational methods' already proven ability to solve complex nonlinear issues. Algorithms such as DL excel in clinical diagnostics, robotics, computer vision, and neuroimaging applications. Additional important note is to be given in integration with sustainable computing, for instance, Green Computing. It is likely to develop intelligent systems based on computational intelligence to optimize performance with minimum ecological impact. This review identifies how these technologies CT, CI, and AI complement each other in enlightening problem-solving skills in educational, scientific, and real-world applications. The results form the basis for future research and practical applications of computational methods in effective problem-solving.
Computational Thinking, Problem-Solving, Deep Learning, Artificial Intelligence, Computational Intelligence, Green Computing, Optimization, Algorithmic Thinking, Educational Technology, Intelligent Systems.
Scientific, technological, and social problems in the 21st century have occupied a steep rise in their scale of complexity. In fact, most fashionable challenges will have a vital core of nonlinearity, developing behaviors, and consistent workings that traditional procedures of study cannot capture. This transformation has moved the spotlight towards computational methods, which merge algorithmic reasoning with simulation and data-driven approaches to solve complex, dynamic systems in an effective and efficient manner. Computational intelligence-a field mainly encompassed of evolutionary algorithms, swarm intelligence, and neural networks-has solved many threatening optimization problems in so diverse areas as engineering, logistics, healthcare, and bioinformatics. These methods show the control of structured problem-solving strategies in high-dimensional, indeterminate, and dynamic environments.
The problem solving, in this view, has become one of the focal human cognitive services in arranging and investigation of assumed procedures for solution accomplishments in complex situations. The classical theories on problem-solving include, but are not limited to, Dewey's reflective thinking model, Polya's stepwise approach, Guilford's creative problem-solving framework, and Stacey's entry-attack-review model. These theoretical approaches are signaled by a shift in more systematic cognitive approaches to increasingly complicated problems. Therefore, these frameworks underscore systematic phases such as problem understanding, planning, reasoning, testing solutions, and reflecting on the outcomes, hence focusing on the connection between cognition, strategy, and effective solution generation. [6]
Computational Thinking, as introduced by Wing and conceptually by foreshadowed by Papert, extends problem-solving into the digital era. CT involves higher-order cognitive skills such as decomposition, pattern recognition, abstraction, and algorithmic thinking that enabled individuals to approach complex challenges in a systematic and computational manner. As a problem-solving technique, CT offers a systematic method to outline problems, develop solutions, and realize strategies within education and real life. Previous research indicated that the CT would enhance the problem-solving competence of the learners across several fields, including the STEM education, game-based learning, and the interdisciplinary studies.[1]
While there is an increasing interest in the CT as a paradigm for problem-solving, little attention has been directed toward the specific elements of CT that are related to established problem-solving techniques. Many such studies adopts various CT components in diverse contexts, which complicates any attempt to collate the evidence regarding how these elements support structured problem-solving. This therefore indicates a necessity for a systematic synthesis of existing studies identifying, categorizing, and evaluating techniques operationalizing CT as a problem-solving skill.
The current study will fill this gap by performing a Systematic Literature Review of peer-reviewed studies that have so far investigated CT and its application for problem-solving. The PRISMA methodological approach is adopted for this review, which identifies, quantifies, and correlates specific problem-solving techniques from available studies in CT to their applications across various educational and practical settings. The synthesis of these findings means that this study attempts to derive a general view on CT as an organized problem-solving method that provides insights for educators, researchers, and practitioners committed to higher-order thinking and computational problem-solving skills.[3]