AI in Computational Thinking Education in Higher Education: A Systematic Literature Review
Ebrahim Rahimi, Clara Maathuis
TL;DR
This systematic literature review investigates how AI is applied to Computational Thinking education in higher education, focusing on 2017–2022 publications from ACM and IEEE (excluding Generative AI/LLMs). It identifies benefits such as personalized learning and strategies like gamification, adaptive learning, and project-based/experiential approaches, while noting challenges around creativity, CT-background characterization, and educator expertise. The authors map CT components using Brennan and Resnick’s framework and reveal an expansion toward data-centric practices and the notion of computational empowerment, including decoding of AI and digital technologies. AI techniques span ML, deep learning, conversational AI, data analytics, and AR/robotics-enabled learning, offering evidence of AI’s potential to tailor CT education but also underscoring the need for more rigorous testing, broader publication venues, and future work on Generative AI-enabled CT education.
Abstract
Computational Thinking (CT) is a key skill set for students in higher education to thrive and adapt to an increasingly technology-driven future and workplace. While research on CT education has gained remarkable momentum in K12 over the past decade, it has remained under-explored in higher education, leaving higher education teachers with an insufficient overview, knowledge, and support regarding CT education. The proliferation and adoption of artificial intelligence (AI) by educational institutions have demonstrated promising potential to support instructional activities across many disciplines, including CT education. However, a comprehensive overview outlining the various aspects of integrating AI in CT education in higher education is lacking. To mitigate this gap, we conducted this systematic literature review study. The focus of our study is to identify initiatives applying AI in CT education within higher education and to explore various educational aspects of these initiatives, including the benefits and challenges of AI in CT education, instructional strategies employed, CT components covered, and AI techniques and models utilized. This study provides practical and scientific contributions to the CT education community, including an inventory of AI-based initiatives for CT education useful to educators, an overview of various aspects of integrating AI into CT education such as its benefits and challenges (e.g., AI potential to reshape CT education versus its potential to diminish students creativity) and insights into new and expanded perspectives on CT in light of AI (e.g., the decoding approach alongside the coding approach to CT).