Articles

Knowledge of Content and Students Rules in Planning Mathematical Literacy Lesson: A Case Study of Preservice Teacher from Bachelor of Mathematics

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Knowledge of Content and Student (KCS) is a crucial component of teachers’ pedagogical content knowledge because it enables teachers to anticipate students’ characteristics and use this knowledge to make appropriate instructional decisions. In the context of mathematics literacy, teachers are expected not only to understand mathematical content but also to anticipate how students engage with contextual mathematical problems. This study aims to explore the KCS of a pre-service mathematics teacher from bachelor of mathematics in planning integrated mathematics literacy instruction. A qualitative case study design was used. Data were collected through observations of lesson plans, questionnaires, and semi-structured interviews and analyzed using methodological triangulation. The findings indicate that the participant developed knowledge about students by identifying their prior knowledge, anticipating learning difficulties, and considering students’ interests and motivations before making instructional decisions. These forms of pedagogical anticipation informed the selection of contextual learning tasks, instructional approaches, grouping strategies, and various activities. However, the participants tended to interpret students’ readiness for mathematics literacy learning through the lens of prerequisite mathematical knowledge, while explicit anticipation of students’ mathematics literacy processes received relatively less attention. This study demonstrates that planning mathematics literacy lessons requires teachers not only to identify prerequisite mathematical knowledge but also to anticipate students’ readiness throughout the entire mathematics literacy process. These findings contribute to an understanding of how KCS are applied in mathematics literacy lesson planning and provide implications for strengthening pre-service mathematics teacher education.

Artificial Intelligence and Mechanical Engineering in Greek Vocational High Schools: Educational Perspectives and Institutional Barriers

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This scoping review investigates the potential and challenges of integrating Generative Artificial Intelligence (GenAI) into Vocational Education and Training, with a specific focus on the Mechanical Engineering sector of Greek Vocational High Schools (EPA.L.). The purpose of this study is to analyze how AI can transform the teaching of mechanical engineering courses, bridging the gap between theoretical knowledge and laboratory practice in the context of Industry 4.0. The methodology involved a literature review of 26 scientific sources (2016-2026), utilizing thematic analysis guided by theoretical frameworks such as the Technology Acceptance Model (TAM) and teacher self-efficacy. Key findings indicate that the use of specialized, knowledge-enhanced Large Language Models (LLMs) significantly improves the structural integrity and technical accuracy of instructional materials, effectively reducing teacher preparation time. However, despite a high perceived usefulness, the adoption of AI by educators in public schools is severely hindered by the lack of adequate laboratory infrastructure, the absence of discipline-specific training, and heightened AI anxiety regarding ethical risks. In conclusion, the transition to an AI-enhanced school laboratory necessitates immediate upgrades to equipment and a shift from general ICT training to targeted, specialty-specific professional development programs, ultimately empowering teachers to become designers of digital learning experiences.