The usability influence of user navigation paths regarding the integration of artificial intelligence in software platforms : an investigation through the lens of sustainability
White, Jacob (2025)
Diplomityö
2025
School of Engineering Science, Tietotekniikka
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2025081282231
https://urn.fi/URN:NBN:fi-fe2025081282231
Tiivistelmä
The prevalence of artificial intelligence (AI) has seen a dramatic rise in recent years, with its integration being implemented in a variety of software systems. Human computer interaction research, centred around the potential usability related influences that the integration of artificial intelligence has on software systems exists on a broad level. Despite this, a gap has been identified regarding the integration of guidance-oriented AI. This refers to AI whose goal is to help in guiding and assisting the user via the relevant steps to complete some task, rather than primarily providing them with a solution, or results. Therefore, it is an AI agent whose main focus is to ensure the user remains on the intended navigation path. Furthermore, another increasingly important and prevalent field is that of sustainability, with various individuals and organizations striving to be aware of their sustainability impacts. This research aimed to not only contribute to the understanding of the influence that the integration of such an AI has on a software system, but to also bridge the gap between these influences and the potential corresponding sustainability impacts.
To evaluate the usability influences, a guidance-oriented AI was created for this study. Existing literature was thoroughly examined to understand what integration aspects are the most impactful from a usability perspective, regarding guidance-oriented AI. These findings were used to create an AI chatbot that focused on assisting users by guiding and directing the path they took within some system. This AI was integrated, and specifically trained to assist users on a third-party site, which was made possible via a research-industry partnership. Two sets of usability tests, being a baseline and benchmark group, were conducted with different participants. This included providing participants with set tasks that they were to complete under observation, followed by oral interviews and question periods. Both qualitative data (such as perceived feelings of frustration, trust, and usefulness for example) were collected, as well as quantitative data (such as task completion time).
It was found that the integration of guidance and advisory oriented AI had a positive influence on the perceived system usability, with the perceived efficiency, satisfaction, usefulness, reliability, accuracy, and the rate of successful task completion improving. This came with a trade-off of perceived system trust and transparency decreasing, as well as the time spent on tasks lengthening. Furthermore, despite participants indicating they felt they required less help overall with the AI’s integration, in reality more assistance was requested.
These findings were mapped to the defined sustainability dimensions, which found that there was a strong perceived positive influence on the technical dimension, with the potential for negative perceived consequences regarding both the social and environmental dimensions. The environmental dimension findings are a strong avenue for future research, due to the complex relationship numerous variables could have on the results. The economical dimension was determined to be case dependent, with both positive and negative influences being present depending on the requirements and goals set. Lastly, a complex relationship was identified regarding the potential influence on the individual dimension, with no clear conclusion being present. It should be noted that the majority of identified influences were mapped to the individual and technical dimensions. Therefore, the conclusions drawn for these dimensions are the strongest.
To evaluate the usability influences, a guidance-oriented AI was created for this study. Existing literature was thoroughly examined to understand what integration aspects are the most impactful from a usability perspective, regarding guidance-oriented AI. These findings were used to create an AI chatbot that focused on assisting users by guiding and directing the path they took within some system. This AI was integrated, and specifically trained to assist users on a third-party site, which was made possible via a research-industry partnership. Two sets of usability tests, being a baseline and benchmark group, were conducted with different participants. This included providing participants with set tasks that they were to complete under observation, followed by oral interviews and question periods. Both qualitative data (such as perceived feelings of frustration, trust, and usefulness for example) were collected, as well as quantitative data (such as task completion time).
It was found that the integration of guidance and advisory oriented AI had a positive influence on the perceived system usability, with the perceived efficiency, satisfaction, usefulness, reliability, accuracy, and the rate of successful task completion improving. This came with a trade-off of perceived system trust and transparency decreasing, as well as the time spent on tasks lengthening. Furthermore, despite participants indicating they felt they required less help overall with the AI’s integration, in reality more assistance was requested.
These findings were mapped to the defined sustainability dimensions, which found that there was a strong perceived positive influence on the technical dimension, with the potential for negative perceived consequences regarding both the social and environmental dimensions. The environmental dimension findings are a strong avenue for future research, due to the complex relationship numerous variables could have on the results. The economical dimension was determined to be case dependent, with both positive and negative influences being present depending on the requirements and goals set. Lastly, a complex relationship was identified regarding the potential influence on the individual dimension, with no clear conclusion being present. It should be noted that the majority of identified influences were mapped to the individual and technical dimensions. Therefore, the conclusions drawn for these dimensions are the strongest.