Putting Humans First: Reimagining Undergraduate Data Analysis and Visualisation Through Data Justice, Critical AI Literacy, and Open Practices

This paper reports on the redesign, delivery, and evaluation of a Level 6 undergraduate module in data analysis and visualisation —for criminology, sociology, and psychology students—, showcasing approaches for embedding data justice and critical AI literacy within undergraduate curricula to develop socially responsible students capable of interrogating and challenging the datafied logics shaping contemporary society.  Originally focused on statistical methods and reliant on traditional slide-based teaching and proprietary software, the module was comprehensively reimagined in 2023. The new design places human-centred data justice principles at its core to equip students with the conceptual, ethical, and technical competencies needed to interrogate data and AI and the infrastructures that underpin them. The module is delivered across a four-week-block with three three-hour sessions per week. This format supports collaborative inquiry, and rapid prototyping through data journalism approaches. These methods help counter common anxieties about maths, stats, coding, and quantitative reasoning by fostering collective experimentation and critical curiosity. Central to the redesign is the recognition that understanding data is a prerequisite for understanding AI; without the ability to scrutinise data provenance, quality, and representation, students cannot meaningfully assess AI fairness or accountability. The curriculum draws on a significant body of scholarship in critical data and AI literacy. It is grounded in epistemic data justice and critical pedagogical frameworks (Atenas et al., 2020, 2023, 2025) showcasing the need for HE to foster critical and creative data and AI literacies. Research on surveillance, inequality, and structural marginalisation (Dencik & Sanchez-Monedero, 2022; Dencik et al., 2016, 2019; Heeks & Swain, 2018), provide a further theoretical foundation. The curriculum incorporates insights from feminist data studies and data feminism (D’Ignazio & Klein, 2020; Leurs, 2017) and Indigenous Data Sovereignty (Kukutai & Taylor, 2016), to examine how data practices shape power, representation, and epistemic inclusion or exclusion. Engagement with contemporary research on AI in education (Holmes et al., 2025; Pangrazio et al., 2024; Picasso et al., 2024; Taylor, 2017) situates the module within broader debates about the cultural, ethical, and democratic implications of algorithmic systems. Together, these perspectives frame learners as critically empowered agents capable of interrogating the sociopolitical conditions through which data and AI systems are produced and legitimised. The module is structured around five drivers: (1) data theory, analysis, storytelling, and visualisation foundations; (2) data ethics, privacy, and sovereignty; (3) data justice, data feminism, and Indigenous Data Sovereignty; (4) narrative-driven data storytelling and use of R emphasising responsibility and contextual interpretation; and (5) algorithmic justice, including bias, auditing, and harms. Students work extensively with open data ecosystems, drawing on School of Data tutorials for practical skills and R-Ladies resources to support learning in R, facilitated by a specialist data trainer. Classroom debates focus on discipline-specific applications of AI, helping students connect misconceptions about AI to underlying weaknesses in data literacy. The evaluation of the module has been very positive and shows improvements in students’ self-confidence, critical insight, and methodological agency. Many students have subsequently produced stronger dissertations and some have transitioned into roles requiring data-related expertise. 

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