From knowledge consumption to human-AI co-creation: an empirical study on GenAI-empowered engineering education

Generative Artificial Intelligence (GenAI) in engineering education often serves limited functions, failing to address issues such as superficial cognitive engagement, inadequate representation of dynamic mechanisms, and a disconnect between learning outputs and competency-based evaluation. A new five-stage AI-Enhanced Knowledge Construction (AEKC) model is proposed to reorganize engineering learning as a structured process, moving beyond GenAI as merely an auxiliary tool to foster human-AI co-creation and address these persistent challenges.

Why it matters

This research highlights the critical need to evolve the application of artificial intelligence in specialized educational contexts from simple aid to a more integrated, constructive role. Institutions and organizations involved in education and training must consider how to leverage AI more strategically to enhance complex learning, ensuring deeper engagement and more robust competency development among learners.

Key insights

• GenAI's current application in higher engineering education is often restricted to providing answers and procedural support.
• This limited use of GenAI does not adequately resolve three core issues in engineering courses: superficial cognitive engagement, lack of manipulable representations for dynamic mechanisms, and weak links between learning outcomes and competency evaluation.
• The study introduces a five-stage AI-Enhanced Knowledge Construction (AEKC) model.
• The AEKC model is theoretically grounded in social constructivism, Cognitive Load Theory, and Outcome-Based Education principles.
• The model advocates for GenAI's role to evolve from an auxiliary tool to an integral part of a staged topic structuring process for human-AI co-creation.

Source

Educational Technology Research and Development — https://link.springer.com/article/10.1007/s11423-026-10696-x