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.

Requirements Engineering (RE) is an essential part of Software Engineering (SE). As RE activities rely heavily on people, the success of RE is influenced by the human aspects of the team involved. In this research, we develop a prototype application, called Motive Metrics, to improve RE activities by allowing managers to track developers’ personality and motivation and monitor their impact on developer performance and satisfaction. The tool takes the form of an extension to Jira and was developed through rapid prototyping. The effectiveness and usability of the tool were evaluated by student teams split into managers and team members. When evaluating Motive Metrics, 45.5% of participants rated 4 out of 5 for the application’s effectiveness in capturing personalities, but only 9.1% of participants rated 4 out of 5 for capturing motivations. Motive Metrics is likely ineffective in monitoring satisfaction and performance, as 45% of participants rated 1 out of 5 in comfort in sharing their responses. Our evaluation results also show that Motive Metrics might not be beneficial in tracking the influence of motivation on the outcome but slightly more beneficial in tracking the influence of personality on RE task performance.