Advanced AI Methodologies for Enhancing User Experience in Human-Computer Interaction

Background: Unprecedented growth in artificial intelligence (AI) is changing the way people interact with computers- the concept known as human-computer interaction (HCI), several AI approaches like machine learning or natural language processing have boosted the user experience (UX). Nevertheless, there are some ethical concerns and differences in users’ awareness of AI systems that make it difficult to consider the general advantages of AI in HCI. Objective: Therefore, this research seeks to explore the impact of enhanced AI techniques on the level of UX in HCI with a focus on the facet of user satisfaction, trust, and usefulness of AI-based personalization techniques. It also solves the ethical problem associated with the AI systems. Methods: A quantitative research method was used with the use of structured questionnaires with 250 participants. It also captured the user engagement with AI systems, as well as their satisfaction and trust levels as well as their concerns relating to AI ethics. Descriptive analysis was used to examine the collected data through statistical tests such as; the Shapiro-Wilk test for normality and Cronbach’s Alpha for reliability. Results: It is seen that most of the users are happy with the AI technologies integrated into their lives, with special emphasis on the aspect of personalization. However, the data was nonparametric (Nomography = 0 05) and so was the internal consistency (Cronbach alpha -0. 033 on the Likert scale items were needed for survey tools of higher specific focus. The results of the survey showed that ethical considerations running across the respondents as the most significant factor of discrepancy between satisfaction and trust in areas of tension. Conclusion: In the context of AI methodologies, most are perceived to have positive effects in enhancing the UX regarding individualized engagements. However, ethical issues should be met and addressed while determining the reliability of the tools used in ascertaining the impact of AI. There is a need for future studies to address these issues and improve the techniques used for evaluating AI’s diverse position in HCI.