RAIL: A Reasoning-Aware AI Learning Framework to Preserve Critical Thinking in AI-Assisted Engineering Education

Abstract

The integration of Artificial Intelligence (AI) into engineering education has significantly altered students' processes for accessing information, finding solutions and grasping intricate ideas. Even though educational AI-powered tools present substantial benefits regarding accessibility, efficiency and individualized aid, over-reliance upon immediate-answer systems has the potential to compromise learners' critical thinking, logical reasoning and independent problem-solving ability. This problem has grown and highlighted the importance of designing educational AI systems that support learning without fostering cognitive dependence. In this work, the role of reasoning-aware AI systems is examined in seeking a healthy balance between leveraging technology support and promoting cognitive growth for undergraduate engineering students. Through a review of available research on the implications of cognitive offloading, active learning, reflective practice, adaptive learning, and explainable AI, key elements influencing students' learning strategies are highlighted. Results illustrate how guided reasoning, reflective practice-based learning, adaptive learning, and clear interactions with AI can bolster knowledge retention, improve engagement and boost students' independent problem-solving skill while avoiding reliance on direct solutions from an automated source. To this end, this study proposes the RAIL (Reasoning-Aware Intelligent Learning) Framework, an AI-supported educational design model that promotes critical thinking in AI-augmented learning situations. It comprises five interdependent components: Problem Interpretation, Guided Reasoning, Reflection Validation, Adaptive Intelligence, and Transparency and Trust. Instead of merely supplying answers, RAIL encourages active engagement by providing cues, guiding the reasoning process, facilitating reflection, and offering tailored instruction. By incorporating learning-focused processes into AI-aided instruction, the model attempts to nurture independence, improve learning longevity, and encourage responsible AI use in engineering education. The model is designed to provide insight toward developing sustainable AI learning systems that enhance human intellect rather than replacing it.

Keywords

Applied Science - Computer Science

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