AI Framework for Synthesizing Qualitative User Feedback A Literature Review
DOI:
https://doi.org/10.33050/r1yewe47Keywords:
Qualitative Feedback, Actionable Insights, UX UI Design, Feedback Synthesis Framework, Usability TestingAbstract
The increasing reliance on user-centered design in digital product development has intensified the need for systematic approaches to transforming qualitative user feedback into actionable insights for UX and UI decision-making. Although qualitative feedback provides rich understanding of user motivations, frustrations, and contextual behaviors, product teams often face challenges such as data ambiguity, interpretive bias, information overload, and weak alignment between research outcomes and product strategy. This literature review aims to synthesize existing academic research and industry practices to propose a structured framework that bridges qualitative analysis and technology-driven product decisions. Using a qualitative research design based on framework analysis, this study reviews established methods including user interviews, usability testing, open-ended surveys, thematic analysis, and affinity-based synthesis. These approaches are integrated into a four-step framework consisting of feedback coding, theme identification, alignment with product objectives, and formulation of actionable insights. The findings of this review suggest that applying a structured synthesis process enhances analytical clarity, improves traceability between user feedback and design actions, and supports more consistent prioritization in UX/UI practices. Illustrative applications drawn from prior studies demonstrate how the framework can translate qualitative insights into concrete design recommendations without relying on empirical experimentation. This study concludes that qualitative user feedback delivers meaningful value only when processed through a systematic synthesis mechanism that connects user narratives with strategic and operational product decisions, providing a conceptual foundation for data-driven and AI-supported UX/UI design environments.
References
M. Hassenzahl, “User experience and experience design,” Foundations and Trends in Human–Computer Interaction, vol. 15, no. 1, pp. 1–134, 2022.
J. Sauro and J. R. Lewis, “Advances in measuring the user experience,” Human–Computer Interaction, vol. 37, no. 2, pp. 97–145, 2022.
S. Kujala, V. Roto, K. V¨a¨an¨anen, E. Karapanos, and A. Sinnel¨a, “Ux in agile development: A systematic literature review,” Information and Software Technology, vol. 145, 2022.
N. Lutfiani, A. Ivanov, N. P. L. Santoso, S. V. Sihotang, and S. Purnama, “E-commerce growth plan for msmes’ sustainable development enhancement,” CORISINTA, vol. 1, no. 1, pp. 80–86, 2024.
M. Bano, D. Zowghi, and F. da Rimini, “User involvement in software development: A qualitative synthesis,” IEEE Software, vol. 39, no. 1, pp. 72–79, 2022.
A. Holzinger et al., “Human-centered ai in practice: Design principles and challenges,” IEEE Computer, vol. 55, no. 8, pp. 30–38, 2022.
T. Liao, J. Bardzell, and S. Bardzell, “Data-driven ux: Opportunities and challenges,” ACM Transactions on Computer-Human Interaction, vol. 29, no. 4, 2022.
P. McCurdy, J. Gerdes, and M. A. Sedlmair, “A framework for actionable insights in visualization design,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 1, pp. 1–11, 2022.
Y. Wang, Y. Li, and J. Liu, “Mining user feedback for product design improvement using nlp,” Expert Systems with Applications, vol. 195, 2022.
C. Lukita, S. Wulandari, S. M. Wahid, and T. Widiyatmoko, “The impact of blockchain on business and economics: Analysis of theory and implementation,” Blockchain Frontier Technology, vol. 4, no. 1, pp. 58–62, 2024.
A. Calero Valdez and M. Ziefle, “Trust, explainability, and user-centered ai,” IEEE Technology and Society Magazine, vol. 41, no. 3, pp. 70–78, 2022.
S. Vermeulen, K. Luyten, C. Frisson, and K. Coninx, “Cross-disciplinary ux research synthesis,” Human Computer Interaction, vol. 38, no. 3, 2023.
Nielsen Norman Group, “From insights to action: Structuring qualitative ux findings,” Journal of Usability Studies, vol. 18, no. 2, 2023.
M. Alizadeh, A. F. Smeaton, and G. Jones, “Text analytics for large-scale user feedback,” Information Processing & Management, vol. 59, no. 4, 2023.
T. Suhara, C. S. Bangun et al., “Human resource competence and innovation of small and medium micro enterprises (umkm) tangerang regency,” APTISI Transactions on Management, vol. 6, no. 2, pp. 181–190, 2022.
F. Ricci, L. Rokach, and B. Shapira, “Recommender systems and user feedback loops,” ACM Computing Surveys, vol. 55, no. 6, 2023.
L. Chuang and A. Endert, “Human-in-the-loop analytics for decision support,” IEEE Computer Graphics and Applications, vol. 43, no. 1, pp. 86–96, 2023.
R. V. Yampolskiy et al., “Designing ai systems with human-centered values,” IEEE Access, vol. 11, pp. 1–15, 2023.
N. G. Taylor and L. M. Thomas, “Qualitative rigor in ux research,” Human–Computer Interaction, vol. 39, no. 1, 2024.
K. Diantoro, D. Supriyanti, Y. P. A. Sanjaya, S. Watini et al., “Implications of distributed energy development in blockchain-based institutional environment,” Aptisi Transactions on Technopreneurship (ATT), vol. 5, no. 2sp, pp. 209–220, 2023.
S. Amershi et al., “Guidelines for human-ai interaction,” Communications of the ACM, vol. 67, no. 1, pp. 68–77, 2024.
H. Liu, X. Zhang, and Y. Sun, “Explainable ai for user-centered systems,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 2, pp. 233–244, 2024.
M. Sedlmair and T. Munzner, “Design study methodology revisited,” IEEE Computer Graphics and Applications, vol. 44, no. 2, 2024.
K. C. Madathil, J. Greenstein, and A. Gramopadhye, “Bridging qualitative insights and system design,” Applied Ergonomics, vol. 109, 2024.
A. Abdul-Karim and M. Abdelrazek, “Data-driven decision-making in ux design,” Information and Software Technology, vol. 163, 2025.
A. Cahyono and Y. D. Nurcahyanie, “Identification and evaluation of logistics operational risk using the fmea method at pt. xzy,” Aptisi Transactions on Technopreneurship (ATT), vol. 5, no. 1Sp, pp. 1–10, 2023.
K. Hornbæk, M. Hertzum, and S. Bødker, “Theory-driven ux research in practice,” ACM Transactions on Computer-Human Interaction, vol. 31, no. 1, 2025.
Y. Chen and Q. Yang, “Large language models for qualitative data analysis,” IEEE Intelligent Systems, vol. 40, no. 1, 2025.
A. Abdul Rahman et al., “Ai-assisted qualitative analysis in ux research,” IEEE Access, vol. 12, pp. 1–13, 2024.
S. Knaflic, “Communicating insights effectively in data-driven organizations,” IEEE Computer, vol. 58, no. 3, 2025.
S. Setiawan, U. Rusilowati, A. Jaya, R. Wang et al., “Transforming human resource practices in the digital age: A study on workforce resilience and innovation,” Journal of Computer Science and Technology Application, vol. 2, no. 1, pp. 84–92, 2025.
S. Chatterjee and A. Kar, “Digital transformation and user-centered innovation,” Journal of Business Research, vol. 169, 2025.
L. Deng, M. Huang, and X. Li, “Human-centered nlp for feedback analysis,” IEEE Transactions on Affective Computing, vol. 16, no. 1, 2025.
T. Miller, “Explanation in artificial intelligence,” Artificial Intelligence, vol. 320, 2026.
R. Guidotti et al., “Explainability and actionability in ai systems,” IEEE Transactions on Knowledge and Data Engineering, vol. 38, no. 2, 2026.
S. Knaflic and M. Meyer, “From insights to decisions: Structuring qualitative analytics,” IEEE Computer Graphics and Applications, vol. 46, no. 1, 2026.
J. Jones, E. Harris, Y. Febriansah, A. Adiwijaya, and I. N. Hikam, “Ai for sustainable development: Applications in natural resource management, agriculture, and waste management,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 143–149, 2024.
J. A. Bargas-Avila and K. Hornbæk, “Old wine in new bottles or novel challenges? a critical analysis of ux evaluation methods,” Human–Computer Interaction, vol. 37, no. 4, 2022.
D. Petrelli, E. van den Hoven, and S. Whittaker, “Making sense of qualitative data in design research,” ACM Transactions on Computer-Human Interaction, vol. 29, no. 5, 2022.
S. Knaflic, “Turning analysis into action in data-driven design,” IEEE Computer, vol. 55, no. 11, 2022.
W. Xu, X. Ma, and F. Tian, “Integrating qualitative insights into data-driven design,” IEEE Computer Graphics and Applications, vol. 43, no. 3, 2023.
C. M. Gray and E. Boling, “Designerly ways of knowing in ux practice,” Human–Computer Interaction, vol. 38, no. 5, 2023.
C. Riedl and F. Davis, “Decision support systems for design analytics,” Decision Support Systems, vol. 162, 2023.
S. Sarker and S. Chatterjee, “Digital innovation through qualitative analytics,” Information Systems Journal, vol. 33, no. 4, 2023.
L. M. Putri Mulyaningsih, “The impact of product quality and brand image on repurchase intention through customer satisfaction,” APTISI Transactions on Management, vol. 8, no. 1, pp. 1–13, 2024.
T. Munzner, “Visualization analysis and design for qualitative insight,” IEEE Computer Graphics and Applications, vol. 43, no. 6, 2023.
S. Knaflic, “Communicating qualitative insights for organizational decisions,” Journal of Business Analytics, vol. 6, no. 2, 2023.
Y. Liu and S. Wang, “Qualitative ux analytics in ai-supported design,” IEEE Access, vol. 12, 2024.
M. Sedlmair and M. Meyer, “Reflection and rigor in design study research,” IEEE Computer Graphics and Applications, vol. 44, no. 4, 2024.
R. Amar and J. Stasko, “Knowledge tasks for decision-centered analytics,” IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 1, 2024.
A. Sutarman, U. Rahardja, F. P. Oganda, S. Millah, and N. N. Azizah, “The role of information technology in empowering the creative economy for sustainable tourism,” Aptisi Transactions on Technopreneurship (ATT), vol. 5, no. 2sp, pp. 175–185, 2023.
R. Zhang and C. Li, “Natural language processing for large-scale ux feedback,” Expert Systems with Applications, vol. 234, 2024.
M. T. Ribeiro and S. Singh, “Actionable explainability in ai systems,” Artificial Intelligence, vol. 321, 2026.
Ministry of Communication and Information Technology of the Republic of Indonesia, “Indonesia digital transformation roadmap,” Government Policy Document, 2022, available at kominfo.go.id.
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