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Collaborative design teaching through Project-based learning (PBL), both in developing the real-world collaboration skills of engineers and enhancing learning outcomes, has gained much attention over the last decades. However, obtaining effective involvement and collaboration of learners during PBL has remained one of the main challenges. In the span of four years of engineering PBL experiences, we conducted five iterations of the literature review, case study, and validation, to explore this issue further and suggest solutions based on state-of-the-art technology. Although outcome feedback is a common measure in PBL, the results of this study indicate that while sustained process feedback on active engagement moderates the poor involvement issues, a complementary conflict management strategy is needed to improve the outcomes. However, such interventions are facing scalability and limited resources problems. In addition, we examined a data-driven approach, and Machine Learning (ML) models to analyze text-based communication using Natural Language Processing (NLP) techniques. Finally, based on a proof-of-concept and analyzing existing evidence in the literature, we suggest that ML-NLP systems, using chatbots and Human-AI interaction, have the potential to play a significant role which opens the door for further research.