Sarah Katherine Howard

Artificial Intelligence (AI) literacy is becoming an essential competency across disciplines and stakeholders. However, AI-related education poses important difficulties, as it combines many concerns such as technical skills, ethical/legal issues, critical thinking, etc. These difficulties, combined with the variety of learners background, push towards customizable learning support. Such needs could be better addressed through machine processable representations, as those enable distribution, reuse, and automation mechanisms both from Model-Driven Engineering (MDE) and Generative AI techniques. This paper proposes a framework powered by MDE methodologies to support the conceptualization of the AI literacy domain in terms of competencies and learning scenarios. The conceptualization is realized by means of corresponding metamodels in close collaboration with pedagogy domain experts. The expressiveness of the metamodels is demonstrated in a proof-of-concept developed for AI literacy teaching.

There is significant discussion about the opportunities and issues associated with the use of artificial intelligence in higher education. However, issues, such as academic integrity and privacy, continue to dominate the conversation. This has limited how well instructors and higher education institutions can identify the conditions necessary to support AI use, to benefit all students. The present synthesis of qualitative evidence has explored the available evidence from 2019-2024, to consider the opportunities and conditions of AI use in higher education learning. The result is five synthesized findings addressing this issue at both institutional and learning design levels and considering subcategories ranging from access, to interactions to future learning. Based on the synthesized findings as the proposed AIMED model, addressing the conditions necessary to create opportunities of AI-tool use in higher education for all students. Implications and future research are explored.

Despite the widely recognised impact of both digital and physical spaces as active contributors to teaching and learning processes, relatively little is known about the learning environment. Furthermore, it is time to explore the interplay between these two spaces. Therefore, we proposed employing a holistic approach to develop a new conceptual model for Designing Teaching and Learning Environments (DTALE). To do this, the paper presents first a critical review of the research literature underpinning learning environments, with a particular interest in the integration of the physical and digital spaces, to develop the DTALE model. In the second step, the DTALE model has been developed and validated by applying the model to existing cases from different contexts. Based on the study’s findings, we outline the implications for theory and practice. Limitations and suggestions for future research are also included.