The Role of Instructional Guidance in Generative AI-Assisted Learning: Empirical Evidence from Construction Engineering Education
arXiv:2606.05509v1 Announce Type: cross Abstract: Generative artificial intelligence (AI) is increasingly used to support self-directed learning, yet student interaction with such systems often remains unstructured, limiting engagement in deeper cognitive processes. This study examines how instructional guidance shapes student and AI interaction in construction education. A five-step prompting framework grounded in Generative Learning Theory (GLT) is introduced to guide learner interaction during review activities. A controlled experiment compares three learning conditions: slide-based learnin
The proliferation of generative AI tools necessitates an understanding of effective pedagogical integration to maximize their educational potential, moving beyond casual use.
This study provides empirical evidence for designing targeted instructional guidance frameworks for generative AI, enhancing learning outcomes and efficiency in specialized fields like engineering.
The focus shifts from merely providing AI tools to students to deliberately structuring their interaction with these tools for deeper cognitive engagement and improved learning.
- · Educational technology providers
- · Generative AI developers
- · Construction engineering educators
- · Students
- · Unstructured AI learning platforms
Increased adoption of structured prompting frameworks for educational generative AI.
Development of adaptive AI tutors that incorporate customized instructional guidance based on learner interaction patterns.
Re-evaluation of traditional pedagogical methods as AI-assisted learning becomes more sophisticated and demonstrably effective.
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Read at arXiv cs.AI