What are some topics for a final year dissertation relating to UX and technology

Explanation: Adaptive and personalized interfaces—systems that change layout, content, or functionality based on inferred user preferences, behavior, or context—promise improved efficiency and satisfaction. However, they create tensions for two core UX values: autonomy and privacy. - Autonomy: Personalization can support autonomy by reducing cognitive load and surfacing relevant choices. But when adaptations are opaque, overly prescriptive, or based on coarse inferences, they can subtly steer decisions (choice architecture) and erode users’ sense of control. Dark patterns (e.g., hiding opt-outs, preemptively limiting options) and algorithmic bias can further restrict meaningful agency. Key UX concerns include transparency of why the interface changed, ease of overriding or customizing adaptations, and preserving meaningful choice. - Privacy: Personalization relies on data—behavioral logs, demographics, location, psychometrics—raising risks of unwanted exposure, profiling, and mission creep. Users may tradeoff convenience for privacy unknowingly if data practices are unclear. From a UX perspective, privacy issues manifest as trust loss, reluctance to engage, and altered behavior (privacy-preserving avoidance). Designers must consider data minimization, clear consent flows, intelligible explanations of data use, and controls that are discoverable and effective. Design implications and research directions: - Transparency and explainability: Test how different explanation types (simple labels, justifications, control panels) affect perceived control and trust. - Control affordances: Evaluate granular vs. coarse controls for personalization and their impact on user satisfaction and effort. - Consent and data-use UX: Study consent presentation, notice timing, and the effect of defaults on willingness to share data. - Behavioral effects: Measure whether personalization changes decision diversity, exploration, or long-term preferences. - Vulnerable populations: Investigate harms where personalization amplifies biases or reduces accessibility. Relevant references: - Susser, D., Roessler, B., & Nissenbaum, H. (2019). "Technology, Autonomy, and Manipulation." Ethics and Information Technology. - Eslami, M. et al. (2015). "I always assumed that I wasn't really that close to [her]": Reasoning about Invisible Algorithms in News Feeds. CHI. - Nissenbaum, H. (2004). "Privacy as Contextual Integrity." Washington Law Review. This topic is well-suited for qualitative user studies, lab experiments, mixed-methods evaluations, or design interventions assessing trade-offs between personalization benefits and autonomy/privacy costs.