Project 5 - Technology for Mental Illness (UX Journey with Generative AI)

Published: 24 July 2023| Version 2 | DOI: 10.17632/h9kznbnbvy.2
Wahyu Kusuma,


A UX journey case study involves a comprehensive analysis of the user experience for a specific product or service. The case study would likely include a description of the problem or opportunity that the product/service is intended to address, the target user group, and the context in which the product/service is used. The case study would also include a detailed description of the design process, including user research, prototyping, testing, and iteration. This would involve identifying user needs and preferences, creating wireframes and mockups, and conducting user testing to refine the design. This data is a dataset from a pilot experiment to analyze the productivity of novice developers. In Project 5 with the topic Technology for Mental Illness, developers are asked to do an analysis using UX Journey and AI Generative. The structure of this data set consists of: 1. Original file: Initial analysis file from novice developer. 2. Revision 1: The result of the first improvement of several substantial notes. 3. Revision 2: The result of the second improvement from several substantial notes. 4. Revision 3: The result of the third improvement from several substantial notes. 5. Evaluation Rubric: Tests conducted by novice developers, through self-reviews, peer reviews, and walkthrough evaluations. 6. Self Efficacy: Results of the General Self Efficacy Scale questionnaire 7. Time and User Stories: Recap the time the respondents need to work on and the results of the user stories collected.


Steps to reproduce

To use this dataset, you can use the guide in There are several things you can do with this dataset, you can produce the same dataset using the common elicitation method or with UX Journey. You can freely publish or reproduce for non-commercial purposes. Besides that, you can also use the available datasets for analysis on various research topics such as productivity, self-efficacy, software requirements, and analysis of requirements results.


Universitas Muhammadiyah Malang, Universiti Putra Malaysia


Computer Science, Requirement Engineering, Learning Experience, Human-Computer Interaction, Self-Efficacy, Productivity, User Experience