In 2025 and 2026, researchers have turned a spotlight on how Artificial Intelligence is reshaping Computational Thinking (CT)—the cognitive ability to solve problems, design systems, and understand human behavior through computer science concepts.
A seminal systematic literature review (e.g., Rahimi & Maathuis, 2025) titled “AI in Computational Thinking Education in Higher Education” examines how AI tools are moving beyond simple coding assistants to become core components of the “CT mindset.”
1. The Intersection: AI and CT Components
The review highlights that AI does not just “help” with CT; it redefines its core components for university students.
| CT Component | AI’s Role in Higher Education | Effect on Learning |
| Abstraction | AI tools help students identify high-level patterns in massive datasets. | Shifts focus from syntax to architectural thinking. |
| Algorithmic Design | Generative AI suggests multiple algorithmic paths for a single problem. | Encourages comparative analysis and optimization. |
| Decomposition | AI-driven “solvers” break complex engineering problems into modular tasks. | Helps students manage cognitive load in advanced projects. |
| Debugging | Intelligent Tutoring Systems (ITS) provide “scaffolded” hints rather than just fixes. | Promotes metacognition (thinking about the “why” behind errors). |
2. Emerging AI Techniques in CT Education
The meta-analysis identifies specific AI methodologies currently being used in university labs:
- Predictive Student Modeling: Using machine learning to create a profile of a student’s CT background. This allows platforms to adapt the difficulty of coding challenges in real-time.
- Explainable AI (XAI) as a Tutor: New systems (e.g., Conati et al., 2025) explain why an AI provided a specific hint, helping students understand the underlying logic of the solution.
- Multimodal Analytics: Capturing students’ eye movements, keystrokes, and verbal explanations to assess “CT-in-action,” providing a much richer assessment than a final exam score.
3. Benefits vs. The “Creativity Dip”
The 2025 review presents a nuanced view of the impact of AI on the student experience.
The Promises:
- Personalization: AI addresses the issue of “diverse backgrounds,” allowing students with no prior coding experience to catch up through adaptive paths.
- Motivation: The use of social robots and AI-driven gamification has been shown to significantly promote the acquisition of CT skills.
The Pitfalls:
- Creativity Erosion: A significant finding in recent studies (e.g., Liang, 2025) suggests that over-reliance on Generative AI can diminish a student’s ability to find “out-of-the-box” solutions, as they default to the “statistically average” answer provided by the model.
- Complexity Barrier: Many educators report that the knowledge required to develop and manage these AI-CT tools is a major hurdle for universities with limited technical budgets.
4. Key Recommendations for 2026
The systematic review concludes that for AI to successfully “bridge the gap” in CT education, institutions must:
- Move to “White-Box” AI: Use tools that allow students to see and audit the AI’s logic, rather than “Black-Box” tools that simply output code.
- Foster Human-AI Symbiosis: Redesign curricula where the goal is not to “compete” with AI, but to use AI as a collaborator to tackle “wicked” problems that neither could solve alone.
- Address Invalid Assumptions: The review warns that many current AI tools assume all students learn at a “normal” pace; 2026 models must account for neurodiversity and varied prior knowledge.