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AI-powered educational tools (AIEd) include early warning systems (EWS) to identify at-risk undergraduates, offering personalized assistance. Revealing students' subjective experiences with EWS could contribute to a deeper understanding of what it means to engage with AI in areas of human life, like teaching and learning. Our investigation hence explored students' subjective experiences with EWS, characterizing them according to students’ profiles, self-efficacy, prior experience, and perspective on data ethics. The results show that students, largely senior workers with strong academic self-efficacy, had limited experience with this method and minimal expectations. But, using the EWS inspired meaningful reflections. Nonetheless, a comparison between the Computer Science and Economics disciplines demonstrated stronger trust and expectation regarding the system and AI for the former. The study emphasized the importance of helping students’ additional experiences and comprehension while embracing AI systems in education to ensure the quality, relevance, and fairness of their educational experience overall.

The post-digital era is characterised by the vast presence of platforms that impose their digital affordances and algorithmic control on our behaviour. This environment is challenging education and training, with implications for digital and transmedial literacy. Investigating instructional methodologies is crucial to fostering critical comprehension of such novel informational environments. Argument maps (AMs), which were first created and evaluated in static information contexts (analogical/old web), could be useful in the emergence of dynamic (post-digital) textual forms. The current paper describes a comprehensive literature review based on the above assumptions. We looked into state-of-the-art research on using AMs to handle dynamic information. We found 150 papers using a PRISMA procedure and then examined 19 of them. Our review produced pertinent data about the current state of AMs, including the types of texts on which they are used and the tools (especially digital and artificial intelligence [AI]-based) that have been employed. Our research lays the groundwork for teaching the literacies needed in new informational settings, such as multimodal, dynamic, algorithmic and data-driven contexts, with a specific focus on AMs as effective mediational tools.