Guilherme Guerino

Guilherme Guerino, Luiz Rodrigues, Bruna Capeleti et al.

Heuristic evaluation is a widely used method in Human-Computer Interaction (HCI) to inspect interfaces and identify issues based on heuristics. Recently, Large Language Models (LLMs), such as GPT-4o, have been applied in HCI to assist in persona creation, the ideation process, and the analysis of semi-structured interviews. However, considering the need to understand heuristics and the high degree of abstraction required to evaluate them, LLMs may have difficulty conducting heuristic evaluation. However, prior research has not investigated GPT-4o's performance in heuristic evaluation compared to HCI experts in web-based systems. In this context, this study aims to compare the results of a heuristic evaluation performed by GPT-4o and human experts. To this end, we selected a set of screenshots from a web system and asked GPT-4o to perform a heuristic evaluation based on Nielsen's Heuristics from a literature-grounded prompt. Our results indicate that only 21.2% of the issues identified by human experts were also identified by GPT-4o, despite it found 27 new issues. We also found that GPT-4o performed better for heuristics related to aesthetic and minimalist design and match between system and real world, whereas it has difficulty identifying issues in heuristics related to flexibility, control, and user efficiency. Additionally, we noticed that GPT-4o generated several false positives due to hallucinations and attempts to predict issues. Finally, we highlight five takeaways for the conscious use of GPT-4o in heuristic evaluations.

Karoline Romero, Igor Wiese, Renato Balancieiri et al.

This paper investigates User Experience (UX) with prototypes generated by Generative Artificial Intelligence (GenAI) tools. An empirical survey with 92 participants evaluated AI-generated and human-created prototypes without prior identification of authorship. We measured UX using the UEQ-S, covering pragmatic and hedonic dimensions. Results indicate positive evaluations in pragmatic aspects, such as usability and efficiency, and neutral or negative evaluations in hedonic aspects, including originality and innovation. We concluded that GenAI can produce functional interfaces but tends to reinforce visual and structural patterns that affect perceptions of originality.

Guilherme Guerino, Luiz Rodrigues, Luana Bianchini et al.

This study explores the integration of Augmented Intelligence (AuI) in Intelligent Tutoring Systems (ITS) to address challenges in Artificial Intelligence in Education (AIED), including teacher involvement, AI reliability, and resource accessibility. We present MathAIde, an ITS that uses computer vision and AI to correct mathematics exercises from student work photos and provide feedback. The system was designed through a collaborative process involving brainstorming with teachers, high-fidelity prototyping, A/B testing, and a real-world case study. Findings emphasize the importance of a teacher-centered, user-driven approach, where AI suggests remediation alternatives while teachers retain decision-making. Results highlight efficiency, usability, and adoption potential in classroom contexts, particularly in resource-limited environments. The study contributes practical insights into designing ITSs that balanceuser needs and technological feasibility, while advancing AIED research by demonstrating the effectiveness of a mixed-methods, user-centered approach to implementing AuI in educational technologies.