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Designing Intelligent Feedback: An AI-Augmented Feedback Framework for Prototyping in Design Thinking
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Resumo(s)
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.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Marketing Intelligence
Palavras-chave
Design Thinking Prototype Feedback Artificial Intelligence