Abstract:

The continuous evolution of information technologies requires higher education institutions to equip non-STEM (Science, Technology, Engineering, and Mathematics) students with analytical and problem-solving skills that support digital literacy and informed engagement in academic and societal contexts. From this perspective, this study examines how computational thinking (CT) can be effectively integrated into university programs within the Arts, Humanities, and Social Sciences (AHSS), and proposes a structured model tailored to the pedagogical needs of these fields.

The study is grounded in a systematic literature review of peer-reviewed publications indexed in five major academic databases (ACM Digital Library, IEEE Xplore, SpringerLink, ScienceDirect, and ERIC). The review covers studies published between 2010 and 2020. A multi-stage selection process reduced an initial corpus of 2,124 publications to 28 empirical studies conducted at the university level. The analysis was structured around eleven research questions addressing CT concepts, disciplinary contexts, pedagogical integration strategies, instructional tools and platforms, assessment and validation methods, scenario design, and implementation challenges.

The results indicate that most initiatives focus primarily on digital tools and platforms – such as programming environments, data analysis systems, and visualization tools – while nearly 90% of the reviewed studies do not explicitly identify the CT concepts involved.

Three recurring integration patterns emerged:
(1) initiation to domain-specific digital tools,
(2) theoretical or methodological reinforcement of disciplinary learning through implicit or explicit CT concepts, and
(3) training of future educators in CT. Recurrent difficulties include limited prior technical experience among AHSS students, the lack of pedagogical resources adapted to non-STEM contexts, and assessment constraints in large cohorts.

Building on these findings, the paper proposes a three-path integration model aligned with Jeannette Wing’s definition of computational thinking and structured according to the nature of the information-processing agent involved: machine-based (introduction to new technologies), human-based (reinforcement of teaching methods), and hybrid approaches (training of trainers).

To illustrate the model’s applicability, a practical case study is presented in an undergraduate Marketing program using Microsoft Power BI. This example demonstrates how CT concepts such as abstraction, decomposition, algorithmic thinking, modeling, and visualization can be meaningfully embedded within AHSS curricula, providing a replicable guide for instructors to integrate CT principles effectively into non-STEM courses.

The proposed model contributes a structured framework for integrating CT beyond STEM disciplines and offers practical guidance for educators as well as research-oriented perspectives to support educational innovation and interdisciplinary collaboration in higher education. Future work may extend the model to additional disciplines and assess its impact on student learning outcomes.

Keywords:

Computational Thinking, Non-STEM/AHSS University Programs, Computational Thinking Integration Model.