Fostering Novice Collaboration in ML-Based System Design Through Visual Languages and Touch Interfaces

Artificial Intelligence is driving society toward an increasingly algorithmic future, advancing innovation through data-driven predictive analytics. At the core of this transformation lie advanced Machine Learning-based systems, which hold significant societal potential but often remain inaccessible to domain specialists due to their complexity. This reliance on computing experts limits the integration of domain knowledge and raises concerns about transparency and inclusivity. Drawing from research in HCI, Computer-Supported Cooperative Work, and Cognitive Load Theory, this work explores how Visual Programming Languages (VPLs) and touch-based interfaces can support novice collaboration in ML-based system design. We evaluate PyFlowML Touch, a touch-enabled extension of the previously developed PyFlowML system, which enhances node layout and introduces interactive feedback mechanisms tailored to co-located novice collaboration. Designed for domain specialists, PyFlowML guides users through the ML process and incorporates Explainable AI techniques to improve understanding of model behavior [81, 82]. In this exploratory study, a multifaceted evaluation combining cognitive walkthrough and heuristic inspection yields promising insights, suggesting that the touch-enabled interface can reduce intrinsic and extraneous cognitive load while promoting schema construction and collective ML understanding through interactive visualizations.