A Generative AI-Driven Predictive Analytics Framework for Modelling Creativity and Performance in Engineering Design Systems

Engineering education is increasingly shifting toward data-driven and creativity-centred pedagogies that foster innovation, communication, ethical awareness, and teamwork. However, traditional Problem-Based Learning and Design Thinking approaches rely heavily on subjective evaluation and lack scalable mechanisms for monitoring learning progression and creativity development. These pedagogical limitations highlight the need for data-driven approaches that can support iterative learning processes, continuous feedback, and objective evaluation of creativity and performance. This study proposes a Generative Artificial Intelligence (GenAI)-driven predictive analytics framework for modelling student performance and creativity in engineering design systems. The framework integrates deep learning architectures, including Long Short-Term Memory (LSTM) networks and Transformer-based multimodal fusion, to analyze temporal and heterogeneous learning data. The novel Creativity Index (CI) is introduced to quantify design innovation by combining novelty and feasibility metrics derived from AI-assisted interactions and project milestones. The model was evaluated on a longitudinal dataset comprising 450 students across 10 semesters (~5400 time-series observations). Experimental results demonstrate strong predictive performance, achieving 89% classification accuracy and RMSE of 3.8. Comparative analysis shows significant improvements in engineering design (+15%), communication (+16%), ethical awareness (+17%), and teamwork (+16%) (p < 0.01). The proposed framework enables real-time feedback, early risk detection, and adaptive learning optimization. These findings highlight the potential of integrating generative AI and predictive analytics to develop scalable, data-driven intelligent learning systems.