We explore the foundational principles of computational thinking (CT) and their application across various academic disciplines, with a particular emphasis on non-STEM fields such as the humanities and social sciences. By employing a systematic approach, our study seeks to answer the central research question: “How do CT skills enhance problem-solving methodologies in non-STEM fields, and what challenges arise in integrating these skills into different curricula?” The primary components of CT—decomposition, pattern recognition, abstraction, and algorithmic thinking—are thoroughly analyzed. Additionally, we identify critical barriers to integrating CT, including faculty resistance, rigid curricula, and limited resources. Through an extensive literature review and qualitative analysis, the chapter sheds light on the benefits of CT and provides practical recommendations for future research and practice.

CT is increasingly recognized as a critical skill for students across all academic disciplines, extending beyond the traditional confines of computer science. Thinking like a computer scientist involves more than just programming; it requires working with multiple levels of abstraction and utilizing skills and mental models that enable problem framing and solution design in ways computers can process. Fundamental principles of CT include abstraction, automation, and analysis, which are crucial for developing algorithms and software solutions.

CT skills enable individuals to tackle complex problems by breaking them down into more manageable components through a process known as decomposition. After decomposition, the following steps involve identifying patterns, abstracting key elements, and eventually devising step-by-step solutions using algorithmic thinking. This structured approach is applicable across various disciplines, making CT valuable well beyond computer science. As a result, many scholars advocate for integrating CT into K-12 education, emphasizing its potential to enhance problem-solving skills in diverse fields. K-12 educators increasingly incorporate CT into their curricula, recognizing that it equips students with essential skills for future success. For example, coding platforms like Scratch offer opportunities for students to develop CT skills through creative coding projects that foster logical reasoning, creativity, and collaboration.

The transformative potential of computational thinking (CT) in enhancing problem-solving capabilities across various academic disciplines, including the humanities and social sciences. It explored the core components of CT—decomposition, pattern recognition, abstraction, and algorithmic thinking—and demonstrated how these principles can be incorporated into diverse curricula to foster critical thinking, adaptability, and innovation among students. While CT offers numerous benefits, integrating it into non-STEM disciplines poses challenges such as faculty resistance, curriculum rigidity, and resource constraints. Future research should focus on developing comprehensive models for integrating CT into non-STEM curricula, examining its long-term impact on student outcomes, and evaluating the effectiveness of various teacher training methods. Additionally, there is a need to investigate how educational technologies can support and enhance CT education.

Keywords: Computational Thinking (CT), Problem-Solving, Non-STEM Disciplines, Decomposition Pattern Recognition, Abstraction, Algorithmic Thinking, Curriculum Integration.