Generative AI-enhanced learning experiences for computational thinking: A systematic scoping review and design guidelines

Computational thinking (CT) skills are crucial to teach. The rise of Generative AI could potentially revolutionize the teaching of these skills by changing learning experiences and outcomes. Educators are rapidly integrating these tools into their educational activities; however, the best practices for doing so remain unclear. Existing reviews on GenAI are either too broad in regard to studying specific tools such as ChatGPT for learning, or too limited with terms of studying computational thinking with a focus on programming. As such, this paper aims to summarize existing knowledge and extract best practices and avenues for future research and practice around GenAI and computational thinking skills. Our contributions are two-fold: 1) Insights from a systematic scoping review of studies examining the use of GenAI to support the teaching of CT skills in diverse contexts and their reported learning outcomes, 2) Design guidelines to inform effective use of GenAI in CT education for pedagogical practice. Our results reveal a young but rapidly growing research field, with most interventions focusing on undergraduate students and basic programming tasks, often using off-the-shelf tools with limited integration. GenAI is typically used as a coder, tutor, debugger, or ideator, with mixed effects on learning outcomes. A key challenge is the tension between overreliance by beginners, who may offload thinking to GenAI, and under-utilization by advanced learners in complex projects. We derive seven guidelines to provide actionable recommendations from the findings to effectively integrate GenAI for CT while minimizing associated risks and guiding responsible AI use in education. We also suggest directions to inform the design of future systems and CT pedagogies.