Generative AI (GenAI) tools are transforming information seeking, but their fluent, authoritative responses risk overreliance and discourage independent verification and reasoning. Rather than replacing the cognitive work of users, GenAI systems should be designed to support and scaffold it. Therefore, this paper introduces an LLM-based conversational copilot designed to scaffold information evaluation rather than provide answers and foster digital literacy skills. In a pre-registered, randomised controlled trial (N=261) examining three interface conditions including a chat-based copilot, our mixed-methods analysis reveals that users engaged deeply with the copilot, demonstrating metacognitive reflection. However, the copilot did not significantly improve answer correctness or search engagement, largely due to a "time-on-chat vs. exploration" trade-off and users’ bias toward positive information. Qualitative findings reveal tension between the copilot’s Socratic approach and users’ desire for efficiency. These results highlight both the promise and pitfalls of pedagogical copilots, and we outline design pathways to reconcile literacy goals with efficiency demands.

Many users struggle with effective online search and critical evaluation, especially in high-stakes domains like health, while often overestimating their digital literacy. Thus, in this demo, we present an interactive search companion that seamlessly integrates expert search strategies into existing search engine result pages. Providing context-aware tips on clarifying information needs, improving query formulation, encouraging result exploration, and mitigating biases, our companion aims to foster reflective search behaviour while minimising cognitive burden. A user study demonstrates the companion’s successful encouragement of more active and exploratory search, leading users to submit 75% more queries and view roughly twice as many results, as well as performance gains in difficult tasks. This demo illustrates how lightweight, contextual guidance can enhance search literacy and empower users through micro-learning opportunities. While the vision involves real-time LLM adaptivity, this study utilises a controlled implementation to test the underlying intervention strategies.

This study explores whether privacy literacy, self-efficacy, and concerns mediate the age-related effects on privacy decision behavior. To study privacy decision behavior, we designed an experiment that integrates both heuristic and cognitive manipulations in the decision scenario. 625 old and younger adults participated in the experiment and used our web-based application, “RecipeDigger.” The application recorded users’ privacy decision behavior in the form of accepting or rejecting cookies, which offered a personalized service. Our findings indicate that some of the differences in privacy decision-making between older and younger adults can be traced to having different levels of privacy literacy. Older and younger adults with higher privacy literacy can better align their privacy preferences with their disclosure behavior. By bridging the gap between psychological theories and privacy research, this study provides a comprehensive understanding of the factors influencing privacy decisions among older and younger adults, offering methodological, theoretical, and policy implications.

Echo Chambers and Information Cocoons have become the subject of a multifaceted academic debate – ranging from the proper conceptualization and delineation of related concepts, to questions about their prevalence and uniqueness in the online environment, to arguments about their societal impact and the role of digital technologies. This study presents a systematic literature review that analyzes the existing research to synthesize relevant findings and build the missing foundations of these phenomena. This study follows a hermeneutic analytical approach to the literature to clarify and model the distinction between information cocoons and echo chambers. Furthermore, we summarize the selected literature and identify existing knowledge gaps to outline future research opportunities.

To counter the side effect brought by the proliferation of social media platforms, hate speech detection (HSD) plays a vital role in halting the dissemination of toxic online posts at an early stage. However, given the ubiquitous topical communities on social media, a trained HSD classifier can easily become biased towards specific targeted groups (e.g.,female andblack people), where a high rate of either false positive or false negative results can significantly impair public trust in the fairness of content moderation mechanisms, and eventually harm the diversity of online society. Although existing fairness-aware HSD methods can smooth out some discrepancies across targeted groups, they are mostly specific to a narrow selection of targets that are assumed to be known and fixed. This inevitably prevents those methods from generalizing to real-world use cases where new targeted groups constantly emerge (e.g., new forums created on Reddit) over time. To tackle the defects of existing HSD practices, we propose Generalizable target-aware Fairness (GetFair), a new method for fairly classifying each post that contains diverse and even unseen targets during inference. To remove the HSD classifier's spurious dependence on target-related features, GetFair trains a series of filter functions in an adversarial pipeline, so as to deceive the discriminator that recovers the targeted group from filtered post embeddings. To maintain scalability and generalizability, we innovatively parameterize all filter functions via a hypernetwork. Taking a target's pretrained word embedding as input, the hypernetwork generates the weights used by each target-specific filter on-the-fly without storing dedicated filter parameters. In addition, a novel semantic gap alignment scheme is imposed on the generation process, such that the produced filter function for an unseen target is rectified by its semantic affinity with existing targets used for training. Finally, experiments are conducted on two benchmark HSD datasets, showing advantageous performance of GetFair on out-of-sample targets among baselines.