Keyword: artificial intelligence education

ABSTRACT: Increasingly, teachers are considering using Generative AI (GenAI) and large language models (LLMs) as new tools for developing lessons and creating resources. Interdisciplinary STEAM project design specifically continues to be problematic because it requires many professionals to work together in integrative ways when developing successful STEAM project designs. Collaborating closely to develop authentic problems, establish a high level of pedagogical quality, use principles of inclusive design, and create valid and reliable assessment instruments often exceeds the individual teacher’s professional expertise and available time. The purpose of this conceptual paper is to lay the foundation of what we call “the Interactive Design Process Framework (IDPF)” and propose its use in instruction. IDPF has four steps: Co-design, Analysis, Expert Review, and Synthesis. Using these four iterative (cyclical) steps, the IDPF framework positions GenAI as a collaborative designer to work with both classroom teachers and subject area expert reviewers. As a conceptual contribution, this paper does not present empirical data; rather, it articulates the theoretical rationale, structural logic, and design principles of the framework as a foundation for future empirical investigation. In addition to a primary education example of illustration, implications for practice, limitations of the study, and recommendations for future research are discussed.

ABSTRACT: This research aims in shedding light on international high school students’ perceptions, awareness, and prior knowledge about Artificial Intelligence and Machine Learning, as well as to investigate the effect of taking a relevant learning unit within a STEAM course on students’ dispositions toward and understanding about Artificial intelligence. The analysis of performance and reflection data from 62 individuals revealed low prior student engagement with Artificial Intelligence and Machine Learning content, a positive shift in the anticipated societal impact of Artificial Intelligence and an active engagement with online Artificial Intelligence applications during the unit, as well as no correlation between student performance and gender. It is suggested that the development and implementation of learning designs that focus on conceptual understanding of Artificial Intelligence and Machine Learning could benefit all students.