Infographics and data visualisations have become the primary mode for organising, teaching, and assessing complex information, and a substantial body of work establishes why they support learning. Generative AI now allows learners to produce sophisticated visualisations from natural-language prompts, removing technical barriers but introducing a pedagogical tension: when an AI system performs the analytic and encoding work, learners can obtain competent outputs without engaging the reasoning that makes visual artefacts educational. We argue that this is not only a question of instructional efficiency but of learner agency, of whether the human remains the author of meaning, and therefore a question of dignity in the teaching-learning process. Synthesising two systematic reviews, one spanning information design and the learning sciences and one covering Generative AI in visualisation education, we develop a unified conceptual framework. The second review yields a classification of AI visualisation systems by pedagogical orientation, in which systems that engage learners in reasoning are supported by stronger evidence of learning outcomes than those that deliver finished artefacts. The framework reconceptualises infographics and data visualisations as cognitive–instructional systems whose educational value resides in their construction, across an interlinked process of thinking, learning, and communication, and crosses this process with a set of learner-centred design layers. An agency principle organises the framework, distinguishing tools that scaffold the learner's cognitive work from those that substitute for it, operationalised through transparency, invitation, and the progressive fading of support. We close with design implications and a research agenda foregrounding the needs of linguistically diverse and under-resourced learners, and the measurement of transferable visualisation literacy.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.