ScholarOne - Integrating Artificial Intelligence and Computational
Thinking in Educational Contexts: A Systematic Review of Instructional
Design and Student Learning Outcomes
Abstract
A growing body of research is focusing on integrating artificial
intelligence (AI) and computational thinking (CT) to enhance student
learning outcomes. Many researchers have designed instructional
activities to achieve various learning goals within this field. Despite
the prevalence of studies focusing on instructional design and student
learning outcomes, how instructional efforts result in the integration
of AI and CT in students’ learning processes remains unclear. To address
this research gap, we conducted a systematic literature review of
empirical studies that have integrated AI and CT for student
development. We collected 18 papers from four prominent research
databases in the fields of education and AI technology: Web of Science,
Scopus, IEEE, and ACM. We coded the collected studies, highlighting the
students’ learning processes in terms of research methodology and
context, learning tools and instructional design, student learning
outcomes, and the interaction between AI and CT. The integration of AI
and CT occurs in two ways: the integration of disciplinary knowledge and
leveraging AI tools to learn CT. Specifically, we discovered that AI-
and CT-related tools, projects, and problems facilitated
student-centered instructional designs, resulting in productive AI and
CT learning outcomes.