AI in Education

Understanding how learners process data visualizations with seductive details is essential for improving comprehension and engagement. This study examined the influence of task-relevant and task-irrelevant seductive details on attentional distribution and comprehension in the context of data story learning, using COVID-19 data visualizations as experimental materials. A gaze-based methodology was applied, using eye-movement data and saliency maps to visualize learners’ attentional patterns while processing bar graphs with varying embellishments. Results showed that task-relevant seductive details supported comprehension for learners with higher visuospatial abilities by guiding attention toward textual information, while task-irrelevant details hindered comprehension, particularly for those with lower visuospatial abilities who focused disproportionately on visual elements. Working memory capacity emerged as a significant predictor of attentional distribution. Additionally, repeated exposure to data visualizations enhanced participants’ ability to recognize visualization types, improving efficiency and reducing reliance on legends and supplementary text. Overall, this study highlights the cognitive mechanisms underlying visualization processing in data story learning and provides practical implications for education, human–computer interaction, and adaptive technology design, emphasizing the importance of tailoring visualization strategies to individual learner differences.

Statistical education is a crucial yet often overlooked aspect of AI in higher education. However, traditional approaches usually focus heavily on procedural knowledge, leaving students anxious about statistics and less confident in applying concepts to real-world problems. This study examines a method that enhances statistical learning outcomes by integrating data visualization and gamification strategies. Students were randomly assigned to either a control group (CG) or an intervention group (IG), and each group was further divided into teams. The curriculum was enhanced in a college statistics course offered for both engineering and math majors. IG students applied data visualization and gamification in a hands-on group project aimed at solving a real-world problem and competed as they presented their results. The effectiveness of this approach was assessed through statistical analyses comparing the performance of IG and CG in surveys, final grades, and project grades. The results from evaluation methods indicated that IG students outperformed CG students, demonstrating a positive impact of gamification on statistics education.

We surveyed 297 STEM undergraduates at a single English-medium Sino–UK joint institution to document perceptions of AI chatbots for learning. Students reported high willingness to adopt AI chatbots (78%; 95% CI: 73.1–82.4) alongside concerns about over-reliance (67%; 95% CI: 61.4–72.1), content quality (52%; 95% CI: 46.2–57.5), and reduced human interaction (42%; 95% CI: 36.5–47.8). Over half (52%; 95% CI: 46.3–57.7) requested language/terminology support features, whereas only 16.8% reported language-related barriers. We attempted exploratory factor analysis and k-means clustering, but neither met the inclusion criteria; therefore, we report item-level frequencies only. The findings are descriptive and not generalisable (53% first-year, 80% male convenience sample). These patterns generate testable hypotheses about verification scaffolds, language support utility, and human–AI balance that warrant investigation through controlled studies.