Luigi De Russis

Tommaso Calò, Luigi De Russis

Design mockups are essential instruments for visualizing and testing design ideas. However, the process of generating mockups can be time-consuming and challenging for designers. In this article, we present and evaluate two different modalities for generating mockup ideas to support designers in their work: (1) a sketch-based approach to generate mockups based on hand-drawn sketches, and (2) a semantic-based approach to generate interfaces based on a set of predefined design elements. To evaluate the effectiveness of these two approaches, we conducted a series of experiments with 13 participants in which we asked them to generate mockups using each modality. Our results show that sketch-based generation was more intuitive and expressive, while semantic-based generative AI obtained better results in terms of quality and fidelity. Both methods can be valuable tools for UI designers looking to increase their creativity and efficiency.

Tommaso Calò, Luigi De Russis

Deep Learning (DL) developers come from different backgrounds, e.g., medicine, genomics, finance, and computer science. To create a DL model, they must learn and use high-level programming languages (e.g., Python), thus needing to handle related setups and solve programming errors. This paper presents DeepBlocks, a visual programming tool that allows DL developers to design, train, and evaluate models without relying on specific programming languages. DeepBlocks works by building on the typical model structure: a sequence of learnable functions whose arrangement defines the specific characteristics of the model. We derived DeepBlocks' design goals from a 5-participants formative interview, and we validated the first implementation of the tool through a typical use case. Results are promising and show that developers could visually design complex DL architectures.

Giuseppe Arbore, Andrea Sillano, Luigi De Russis

Recent advances in agentic AI are shifting automation from discrete tools to proactive multi-agent systems that coordinate multi-specialized capabilities behind unified interfaces. However, today's agent systems typically rely on hard-coded agent architectures with fixed roles, coordination patterns, and interaction flows that limit end-user personalization and make adaptation to individual needs and contexts difficult. Given this limitation, we argue that on-demand persona-based agent generation offers a promising path towards more efficient and contextually appropriate interaction within agentic workflows. By dynamically crafting agents and personas at run-time to match user characteristics, task demands, and workflow context, agentic platforms can move beyond one-size-fits-all configurations. We present a pipeline for on-demand persona generation in agentic platforms, detailing how real-time crafting of AI personas can be systematically integrated within agent systems, aiming to open new possibilities in agentic platform design paradigms.