Generative Artificial Intelligence for teachers’ Efficacy in Teaching Computational Thinking - Bangun Panduko Johan; Cucuk Budiyanto; Jamaludin Salim (Information Technology Education Journal, 2026)
Purpose – This study aims to investigate the development and application of GenAI in CT education, and to analyse the challenges of using GenAI for CT learning. Design/methods/approach – Systematic Literature Review (SLR) was adopted in this research The keywords “Generative AI,” “educators,” and “Computational Thinking” were used to nominate 22 articles from Scopus and DOAJ published between 2022 to 2026. Under PRISMA procedure, the data were organised in a concept matrix and analysed with thematic analysis to identify recurring patterns, development approaches, implementation practices and challenges. Findings – The results show that GenAI applications support CT education by integrating with Learning Management Systems, generating quizzes and lesson content through APIs, using prompt engineering, providing coding assistance, giving automated feedback, decomposing problems, enabling adaptive interaction, and supporting personalised learning. Thematic analysis revealed three major categories of challenges: inaccuracies and biases in AI-generated output; pedagogical limitations in terms of teacher competence and over-reliance by students; and ethical and equity issues involving plagiarism, data privacy, unequal access, and responsible use. Research implications/limitations – GenAI facilitate CT education when used as a pedagogical adjunct, not a replacement for educators. To be effective, it needs to be supervised by teachers, validated by humans, driven by ethical awareness, supported by better AI literacy, and guided by sound pedagogical principles. Originality/value – Computational Thinking (CT) has been introduced as the core competency to improve students’ problem-solving skills. The implementation of CT, however, remains challenging due to limited educator readiness, difficulties in designing CT-based learning activities, misconceptions that CT is mainly related to programming, and the need for adequate pedagogical support. Generative Artificial Intelligence (GenAI) has emerged as a promising technology to help educators in instructional-material development, feedback generation, programming assistance and problem-solving activities . But its use raises questions about output’s accuracy, dependency, plagiarism, data privacy, and unequal access concerns.