The use of generative AI (genAI) in higher education is rapidly evolving, provoking both optimism and concern among educators. While some students embrace genAI tools as learning aids, others, including many educators, remain cautious about their implications for critical thinking and academic integrity. Accepting that genAI is readily available and banning its use is not feasible, we explore how it might be integrated meaningfully into pedagogy through the lens of Transformative Learning Theory (TLT). This study investigates how genAI tools influence student learning in an IT Forensics course using diverging assessments, a form of assessment-as-learning where students receive the same authentic tasks but unique data inputs. We examine three research questions addressing genAI's impact on learning strategies, its role in supporting assessment-as-learning tasks, and how it fosters critical reflection and transformation in student learning. Drawing on interviews with 14 students, our findings suggest that, when scaffolded appropriately, genAI use within diverging assessments can catalyze transformative learning by provoking disorienting dilemmas, encouraging reflection, and reshaping problem-solving approaches. Finally, implications for teaching practice and assessment design are discussed.

The emerging concept of data storytelling (DS) suggests that enhancing visualisations with annotations and narratives can make complex data more insightful than conventional visualisations. Previous works found that DS-enhanced visualisations are more effective than conventional visualisations for simple tasks like identifying key data points or the main message. However, no previous work has explored the extent to which DS enhancements influence task completion across different levels of cognitive complexity. We address this gap by presenting the results of a study where 128 participants completed tasks based on four visualisations (two line charts and two choropleth maps, either with or without DS elements) spanning a range of complexity based on Bloom's taxonomy, which has been applied in data visualisation to categorise tasks hierarchically from lower to higher-order thinking. Results suggest that while DS-enhanced visualisations effectively support lower-order tasks (finding data points and understanding insights), they don't necessarily aid the correct completion of higher-order tasks (application, analysis, evaluation and creation). However, DS enhancements improve how efficiently participants complete complex tasks.

Learning analytics dashboards (LADs) simplify complex learner data into accessible visualisations, providing actionable insights for educators and students. However, their educational effectiveness has not always matched the sophistication of the technology behind them. Explanatory and interactive LADs, enhanced by generative AI (GenAI) chatbots, hold promise by enabling dynamic, dialogue-based interactions with data visualisations and offering personalised feedback through text. Yet, the effectiveness of these tools may be limited by learners' varying levels of GenAI literacy, a factor that remains underexplored in current research. This study investigates the role of GenAI literacy in learner interactions with conventional (reactive) versus scaffolding (proactive) chatbot-assisted LADs. Through a comparative analysis of 81 participants, we examine how GenAI literacy is associated with learners' ability to interpret complex visualisations and their cognitive processes during interactions with chatbot-assisted LADs. Results show that while both chatbots significantly improved learner comprehension, those with higher GenAI literacy benefited the most, particularly with conventional chatbots, demonstrating diverse prompting strategies. Findings highlight the importance of considering learners' GenAI literacy when integrating GenAI chatbots in LADs and educational technologies. Incorporating scaffolding techniques within GenAI chatbots can be an effective strategy, offering a more guided experience that reduces reliance on learners' GenAI literacy.

Emerging research has begun to explore the incorporation of data storytelling (DS) elements to enhance the design of learning analytics (LA) dashboards. This involves using visual features, such as text annotations and visual highlights, to help educators and learners focus their attention on key insights derived from data and act upon them. Previous studies have often overlooked the perspectives of educators and other stakeholders on the potential value and risks associated with implementing DS in LA to guide attention. We address this gap by presenting a case study examining how educators perceive the: i) potential value of DS features for teaching and learning design; ii) role of the visualisation designer in delivering a contextually appropriate data story; and iii) ethical implications of utilising DS to communicate insights. We asked educators from a first-year undergraduate program to explore and discuss DS and the visualisation designer by reviewing sample data stories using their students’ data and crafting their own data stories. Our findings suggest that educators were receptive to DS features, especially meaningful use of annotations and highlighting important data points to easily identify critical information. Every participant acknowledged the potential for DS features to be exploited for harmful or self-serving purposes.