During the empathize stage of this project, our main objective was to better understand how college students manage their time and how they interact with scheduling tools in their everyday lives. Rather than focusing on features right away, we wanted to explore potential design challenges around productivity, flexibility, and balance. We were interested in how students actually behave, not just what they say they would prefer in theory. This stage was about identifying patterns in frustrations, habits, and unmet needs before preliminary designing phases.<\/p>\n\n\n\n
Throughout this process, I used ChatGPT as a generative AI tool to support both preparation and analysis. One of the first ways I used AI was to role-play interview scenarios to test whether our questions were open-ended and neutral. For example, I prompted:<\/p>\n\n\n\n
Prompt:<\/strong> AI Response:<\/strong> Using AI in this way helped me decide if the questions were useful based on the responses. It also showed me that a single question could reveal both logistical and emotional concerns.<\/p>\n\n\n\n I also used AI to help analyze interview transcripts and generate empathy maps. I would paste transcripts and prompt it to categorize what users say, think, feel, and do. This helped me organize large amounts of data quickly. Additionally, I used AI to assist with thematic analysis by asking it to identify recurring patterns across all 9 interviews. While the tool grouped responses into themes, I analyzed them to make sure they accurately reflected our participants\u2019 voices.<\/p>\n\n\n\n The benefits of using AI were primarily efficiency and structure. It helped condense long transcripts, highlight patterns, and clarify connections between interviews. However, there were challenges. Some outputs were overly formal or generalized, and I had to adjust the way I asked question to generate the type of responses I was looking for. I also had to be careful to keep insights grounded strictly in participant responses rather than AI-generated assumptions.<\/p>\n\n\n\n Overall, I learned to view AI as a support tool rather than a replacement for research. My team and I conducted the interviews, interpreted context, and determined what insights were meaningful. AI helped with organization and idea refinement, but the judgment and direction remained ours. The division of labor felt fairly balanced since we provided the human understanding, and AI supported the analytical side.<\/p>\n\n\n\n <\/p>\n","protected":false},"excerpt":{"rendered":" During the empathize stage of this project, our main objective was to better understand how college students manage their time and how they interact with scheduling tools in their everyday lives. Rather than focusing on features right away, we wanted to explore potential design challenges around productivity, flexibility, and balance. We were interested in how […]<\/p>\n","protected":false},"author":13,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-243","post","type-post","status-publish","format-standard","hentry","category-blog"],"_links":{"self":[{"href":"https:\/\/sova.ourcloud.ou.edu\/tarynhall\/wp-json\/wp\/v2\/posts\/243","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sova.ourcloud.ou.edu\/tarynhall\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sova.ourcloud.ou.edu\/tarynhall\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sova.ourcloud.ou.edu\/tarynhall\/wp-json\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"https:\/\/sova.ourcloud.ou.edu\/tarynhall\/wp-json\/wp\/v2\/comments?post=243"}],"version-history":[{"count":2,"href":"https:\/\/sova.ourcloud.ou.edu\/tarynhall\/wp-json\/wp\/v2\/posts\/243\/revisions"}],"predecessor-version":[{"id":256,"href":"https:\/\/sova.ourcloud.ou.edu\/tarynhall\/wp-json\/wp\/v2\/posts\/243\/revisions\/256"}],"wp:attachment":[{"href":"https:\/\/sova.ourcloud.ou.edu\/tarynhall\/wp-json\/wp\/v2\/media?parent=243"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sova.ourcloud.ou.edu\/tarynhall\/wp-json\/wp\/v2\/categories?post=243"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sova.ourcloud.ou.edu\/tarynhall\/wp-json\/wp\/v2\/tags?post=243"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\u201cWould seeing shared availability make planning easier or more stressful for you? Why?\u201d<\/p>\n\n\n\n
\u201cSeeing shared availability would mostly make planning easier for me. It would reduce the back-and-forth messaging and make it clearer when there\u2019s actually overlap between everyone\u2019s schedules. That alone would lower the mental effort it takes to suggest plans.
At the same time, it could become stressful if shared availability didn\u2019t account for energy levels or personal boundaries. If I technically looked \u2018free\u2019 but was actually exhausted or needed downtime, I might feel pressure to commit anyway, so it would work best if it also allowed me to protect rest time.\u201d<\/p>\n\n\n\n