Artificial Intelligence Applications in Behavioral Analysis for Advancing User Experience Design
Keywords:
Artificial Intelligence (AI), Behavioral Analysis, User Experience (UX) Design, Machine Learning, Natural Language Processing (NLP), Personalization, Predictive Analytics, Ethical AI, Data Privacy, Human-Computer InteractionAbstract
Artificial intelligence (AI) has emerged as a transformative force in behavioral analysis, significantly advancing the field of user experience (UX) design. This research explores how AI techniques, such as machine learning, natural language processing, and computer vision, are used to analyze user behaviors, predict preferences, and optimize digital interfaces. By synthesizing findings from original research and case studies, the study highlights the impact of AI on personalization, user satisfaction, and the reduction of friction points in user journeys. Despite these advancements, challenges such as data quality, model interpretability, and ethical considerations—especially regarding privacy and bias—persist. This paper identifies research gaps and outlines opportunities for future exploration, emphasizing the need for transparent and inclusive AI systems that balance innovation with ethical integrity. By addressing these challenges, AI can continue to drive the development of user-centered designs that enhance satisfaction and engagement across diverse platforms.
References
Nilsson, N. J. (1998). Artificial Intelligence: A New Synthesis. San Francisco: Morgan Kaufmann. Quinlan, J. R. (1996). Induction of decision trees. Machine Learning, 1(1), 81–106.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. (2017). Social media in marketing: A review and analysis of the existing literature. Journal of Enterprise Information Management, 30(1), 117–138.
Sharma, A., & Gera, N. (2019). Chatbots for customer service: A review. Asia Pacific Journal of Marketing and Logistics, 32(1), 12–24. https://doi.org/10.1108/APJML-12-2018-0496
Leventhal, L., Teasley, S., & Eberts, R. E. (2018). User experience in digital environments: Visualizing behavior. Behaviour & Information Technology, 37(1), 1–10.
Sarker, I. H. (2019). Machine learning: Algorithms, real-world applications and research directions. Knowledge-Based Systems, 206, 106369.
Grewal, D., Roggeveen, A. L., & Nordfält, J. (2019). The future of retailing. Journal of Business Research, 100, 69–85.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2019). A survey on bias and fairness in machine learning. International Journal of Human-Computer Studies, 135, 102385.
Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1).
Pine, B. J., & Gilmore, J. H. (2011). The Experience Economy. Boston: Harvard Business Review Press.
Lipton, Z. C. (2016). The mythos of model interpretability. arXiv preprint arXiv:1606.03490.
Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.
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Copyright (c) 2021 Sagar Vishnubhai Sheta (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
