Decentralised social media (DSM) platforms such as Mastodon offer community-governed alternatives to corporate social networks but place substantial governance burdens on volunteer operators. As interest grows in applying artificial intelligence (AI) to support this work, little is known about whether DSM operators want AI, what roles they consider appropriate, and what governance boundaries they require. We conducted semi-structured interviews with 20 operators across Mastodon, Pixelfed, PeerTube, Lemmy, Pleroma, and Funkwhale, using generative feature probes and speculative scenarios to explore their perceptions of AI. Operators rejected AI as an autonomous actor, instead envisioning it as governance infrastructure that provides contextual intelligence, supports cross-instance coordination, and sustains community and moderator well-being. They also articulated strict boundaries rooted in DSM values, including human accountability, reversibility, transparency, community-centred configuration, and strong data-governance constraints. We contribute empirical insights and design implications for AI compatible with decentralised, federated social media.

Social media has become a primary source of entertainment and education for children globally. While much attention has been given to children’s online well-being, a pressing concern often goes unnoticed: the pervasive data harvesting underlying social media and its manipulative impact on undermining children’s autonomy. In this paper, we present CHAITok, an Android mobile application designed to enhance children’s sense of autonomy over their data on social media. Through 27 user study sessions with 109 children aged 10–13, we offer insights into the current lack of data autonomy among children regarding their online information, and how we can foster children’s sense of data autonomy through a socio-technical journey. Our findings inspire design recommendations to respect children’s values, support children’s evolving autonomy, and design for children’s digital rights. We emphasize data autonomy as a fundamental right for children, call for further research, design innovation, and policy changes on this critical issue.

Children today are deeply immersed in the online world, where their activities are routinely tracked, analysed, and monetised. This exposes them to various datafication risks, including harmful profiling, micro-targeting and behavioural engineering. Most existing measures focus on immediate online threats, rather than informing children about these implicit risks. In this paper, we present The KOALA Hero Toolkit, a hybrid toolkit designed to help children and parents jointly understand the datafication risks posed by their mobile apps. Through user studies involving 17 families we evaluate how the toolkit influenced families’ thought processes, perceptions and decision-making regarding mobile datafication risks. Our findings show that KOALA Hero supports families’ critical thinking and promotes family engagement. We identify future design recommendations for family support, featuring ideas such as integrating triggering moments and bonding moments in toolkit designs. This work provides timely inputs on global efforts aimed at addressing datafication risks and underscores the importance of strengthening legislative and policy enforcement of ethical data governance.

Decentralised social media platforms are increasingly being recognised as viable alternatives to their centralised counterparts. Among these, Mastodon stands out as a popular alternative, offering a citizen-powered option distinct from larger and centralised platforms like Twitter/X. However, the future path of Mastodon remains uncertain, particularly in terms of its challenges and the long-term viability of a more citizen-powered internet. In this paper, following a pre-study survey, we conducted semi-structured interviews with 16 Mastodon instance administrators, including those who host instances to support marginalised and stigmatised communities, to understand their motivations and lived experiences of running decentralised social media. Our research indicates that while decentralised social media offers significant potential in supporting the safety, identity and privacy needs of marginalised and stigmatised communities, they also face considerable challenges in content moderation, community building and governance. We emphasise the importance of considering the community's values and diversity when designing future support mechanisms.

Errors in AI grading and feedback often have an intractable set of causes and are, by their nature, difficult to completely avoid. Since inaccurate feedback potentially harms learning, there is a need for designs and workflows that mitigate these harms. To better understand the mechanisms by which erroneous AI feedback impacts students’ learning, we conducted surveys and interviews that recorded students’ interactions with a short-answer AI autograder for “Explain in Plain English” code reading problems. Using causal modeling, we inferred the learning impacts of wrong answers marked as right (false positives, FPs) and right answers marked as wrong (false negatives, FNs). We further explored explanations for the learning impacts, including errors influencing participants’ engagement with feedback and assessments of their answers’ correctness, and participants’ prior performance in the class.

Deployment of AI assessment tools in education is widespread, but work on students’ interactions and attitudes towards imperfect autograders is comparatively lacking. This paper presents students’ perceptions surrounding a ∼ 90% accurate automated short-answer grader that determined homework and exam credit in a college-level computer science course. Using surveys and interviews, we investigated students’ knowledge about the autograder and their attitudes.