Generative AI is revolutionizing content creation and has the potential to enable real-time, personalized educational experiences. We investigated the effectiveness of converting textbook chapters into AI-generated podcasts and explored the impact of personalizing these podcasts for individual learner profiles. We conducted a 3x3 user study with 180 college students in the United States, comparing traditional textbook reading with both generalized and personalized AI-generated podcasts across three textbook subjects. The personalized podcasts were tailored to students’ majors, interests, and self-described instructional preferences. Our findings show that students found the AI-generated podcast format to be more enjoyable than textbooks and that personalized podcasts led to significantly improved learning outcomes, although this was subject-specific. These results highlight that AI-generated podcasts can offer an engaging and effective modality transformation of textbook material, with personalization enhancing content relevance. We conclude with design recommendations for leveraging AI in education, informed by student feedback.
As story writing requires diverse resources, a single system combining these resources could improve personalization. We leverage the broad capabilities of generative AI to support both more general story writing needs and an understudied but essential aspect: reflection on the moral (lesson) conveyed. Through a formative study (N=12), a user study (N=14), and external evaluation (N=19), we designed, implemented, then studied a prototype plugin for FigJam supporting visualization of the story structure through customizable node graph editing, LLM audience impersonation (chatbot and non-chatbot interfaces), and image and audio generative AI features. Our findings support writers' preference for leveraging unique interplays of our breadth of features to satisfy shifting needs across writing processes, from conveying a moral across audience groups to story writing in general. We discuss how our tool design and findings can inform model bias, personalized writing support, and visualization research.
Intelligent agents coexisting with humans often need to interact with human-shared objects in environments. Thus, agents should plan their interactions based on objects' affordances and the current situation to achieve acceptable outcomes. How to support intelligent agents' planning of affordance-based interactions compatible with human perception and values in real-world contexts remains under-explored. We conducted a formative study identifying the physical, intrapersonal, and interpersonal contexts that count to household human-agent interaction. We then proposed ACKnowledge, a computational framework integrating a dynamic knowledge graph, a large language model, and a vision language model for affordance-based interaction planning in dynamic human environments. In evaluations, ACKnowledge generated acceptable planning results with an understandable process. In real-world simulation tasks, ACKnowledge achieved a high execution success rate and overall acceptability, significantly enhancing usage-rights respectfulness and social appropriateness over baselines. The case study's feedback demonstrated ACKnowledge's negotiation and personalization capabilities toward an understandable planning process.
Understanding how AI recommendations work can help the younger generation become more informed and critical consumers of the vast amount of information they encounter daily. However, young learners with limited math and computing knowledge often find AI concepts too abstract.
To address this, we developed Briteller, a light-based recommendation system that makes learning tangible. By exploring and manipulating light beams, Briteller enables children to understand an AI recommender system's core algorithmic building block, the dot product, through hands-on interactions. Initial evaluations with ten middle school students demonstrated the effectiveness of this approach, using embodied metaphors, such as "merging light" to represent addition. To overcome the limitations of the physical optical setup, we further explored how AR could embody multiplication, expand data vectors with more attributes, and enhance contextual understanding.
Our findings provide valuable insights for designing embodied and tangible learning experiences that make AI concepts more accessible to young learners.
As personalized recommendation algorithms become integral to social media platforms, users are increasingly aware of their ability to influence recommendation content. However, limited research has explored how users provide feedback through their behaviors and platform mechanisms to shape the recommendation content. We conducted semi-structured interviews with 34 active users of algorithmic-driven social media platforms (e.g., Xiaohongshu, Douyin). In addition to explicit and implicit feedback, this study introduced intentional implicit feedback, highlighting the actions users intentionally took to refine recommendation content through perceived feedback mechanisms. Additionally, choices of feedback behaviors were found to align with specific purposes. Explicit feedback was primarily used for feed customization, while unintentional implicit feedback was more linked to content consumption. Intentional implicit feedback was employed for multiple purposes, particularly in increasing content diversity and improving recommendation relevance. This work underscores the user intention dimension in the explicit-implicit feedback dichotomy and offers insights for designing personalized recommendation feedback that better responds to users' needs.
The advancement of large language models (LLMs) now allows users to actively interact with conversational recommendation systems (CRS) and build their own personalized recommendation services tailored to their unique needs and goals. This experience offers users a significantly higher level of controllability compared to traditional RS, enabling an entirely new dimension of recommendation experiences. Building on this context, this study explored the unique experiences that LLM-powered CRS can provide compared to traditional RS. Through a three-week diary study with 12 participants using custom GPTs for music recommendations, we found that LLM-powered CRS can (1) help users clarify implicit needs, (2) support unique exploration, and (3) facilitate a deeper understanding of musical preferences. Based on these findings, we discuss the new design space enabled by LLM-powered CRS and highlight its potential to support more personalized, user-driven recommendation experiences.
Algorithms have played a central role in personalized recommendations on social media. However, they also present significant obstacles for content creators trying to predict and manage their audience reach. This issue is particularly challenging for marginalized groups seeking to maintain safe spaces. Our study explores how women on Xiaohongshu (rednote), a recommendation-driven social platform, proactively re-appropriate hashtags (e.g., #宝宝辅食, Baby Supplemental Food) by using them in posts unrelated to their literal meaning. The hashtags were strategically chosen from topics that would be uninteresting to the male audience they wanted to block. Through a mixed-methods approach, we analyzed the practice of hashtag re-appropriation based on 5,800 collected posts and interviewed 24 active users from diverse backgrounds to uncover users' motivations and reactions towards the re-appropriation. This practice highlights how users can reclaim agency over content distribution on recommendation-driven platforms, offering insights into self-governance within algorithmic-centered power structures.