Publicação
Designing Intelligent Feedback: An AI-Augmented Feedback Framework for Prototyping in Design Thinking
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Victorino, Guilherme Hidalgo Barata Martins | |
| dc.contributor.author | Calidonio, Sebastián Lema | |
| dc.date.accessioned | 2025-11-07T10:39:18Z | |
| dc.date.available | 2025-11-07T10:39:18Z | |
| dc.date.issued | 2025-10-27 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Marketing Intelligence | pt_PT |
| dc.description.abstract | This study explores how Artificial Intelligence (AI) can enhance the feedback process during the prototyping phase of Design Thinking. While AI has gained traction in design-related fields, especially in ideation, automation, and data analysis, its application in the process of feedback collection, synthesis, and decision-making remains underexplored. Through a qualitative methodology rooted in Design Science Research, this project investigates how professionals working with Design Thinking experience the feedback process and evaluates how AI might address common challenges such as time constraints, participant recruitment, scattered data, and large volumes of qualitative data to process. The research process included thirteen in-depth interviews, a co-design session with six participants, and expert evaluation by two senior professionals. Findings reveal that while AI is not yet trusted for strategic or empathy-driven decisions, it is perceived as a powerful support tool for operational and analytical tasks. Prompt engineering has emerged as a key skill, highlighting the need for design professionals to refine their approach to structuring interactions with generative AI. The main output of this research is a practitioner-oriented framework that proposes three AI integration paths: 1) AI-Led Feedback Loops, 2) AI-Augmented Feedback Tools, and 3) AI-Assisted Synthesis. Mapped across five key feedback stages. Unlike broader conceptual models, this framework shows potential for application; its feasibility and usability require further testing in live projects and broader industry contexts. Future research should examine prompt literacy, custom AI agent deployment, and ethical safeguards for preserving human-centered design values in AI-supported workflows. | pt_PT |
| dc.identifier.tid | 204071747 | |
| dc.identifier.uri | http://hdl.handle.net/10362/190256 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Design Thinking | pt_PT |
| dc.subject | Prototype | pt_PT |
| dc.subject | Feedback | pt_PT |
| dc.subject | Artificial Intelligence | pt_PT |
| dc.title | Designing Intelligent Feedback: An AI-Augmented Feedback Framework for Prototyping in Design Thinking | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Marketing Analítico, especialização em Marketing Intelligence | pt_PT |