Education Sciences

Professionalism is central to veterinary practice, shaping not only the quality of care provided to animals but also the wellbeing of practitioners, the satisfaction of clients, and the sustainability of the profession. Prior research has catalogued various attributes of professionalism that are important for career success, but few studies have integrated these multiple perspectives into a cohesive framework. This study synthesizes insights from three key veterinary stakeholder groups—students, clinical practitioners, and clients—using a multi-methods approach including surveys, focus groups, critical incident interviews, and client complaint analyses. Across the datasets, ranking of Likert-scale responses and thematic analysis revealed four recurring themes that were identified as essential for career success: ‘Effective communication’; ‘Accountability, integrity, trustworthiness, and honesty’; ‘Personal wellbeing’; and ‘Quality of service’. These themes were organized into a unifying theoretical model of veterinary professionalism, conceptualized as a ‘navigational compass’, comprising three domains of care: patient-centered care, relationship-centered care, and self-care. By conceptualizing professionalism in terms of a compass, the model illustrates how veterinarians can draw on key professionalism attributes, coupled with consideration of the three domains of veterinary care, to navigate the challenges of practice and sustain long-term career success. The compass provides a reflective framework to guide veterinarians and educators, to support the integration of professionalism into curricula and to guide careers toward excellence in care and lasting personal fulfilment.

Artificial Intelligence (AI) holds transformative potential to revolutionize teaching and learning, yet its rapid integration poses significant challenges for teacher preparation. While AI competencies—encompassing knowledge, skills, and attitudes—are critical for effective integration, limited research has holistically addressed these three interconnected domains. To bridge this gap, this quasi-experimental study (N = 259) evaluated a triadic instructional design synergizing the intelligent technological, pedagogical, and content knowledge (Intelligent-TPACK) framework, Synthesis of Qualitative Data model, and curated AI tools. Pre-service English as a foreign language (EFL) teachers were assigned to an experimental group (n = 137) receiving the structured intervention or a control group (n = 122) engaging in self-directed AI exploration. Results reveal that the experimental group achieved greater gains across all Intelligent-TPACK dimensions and demonstrated higher-order AI applications in lesson planning. Furthermore, the experimental group experienced a significant reduction in perceived pressure and reported higher perceived usefulness regarding AI integration. Qualitative data revealed that hands-on AI tasks enhanced participants’ confidence, yet challenges with prompts and critical adaptation persisted. The findings demonstrate that systematic training is essential for transforming pre-service teachers’ passive awareness into competent AI integration. Finally, this paper proposes practical implications for integrating this triadic framework into teacher education curricula to facilitate sustainable AI adoption.

The increasing integration of artificial intelligence (AI) in education highlights the need for teacher preparation programs to support pre-service teachers in developing pedagogically grounded and ethically responsible AI competencies. This study designed and preliminarily examined an Experiential Design Learning model within a Digital Learning Ecosystem (EDL–DLE) to support the development of AI competencies and instructional innovation in pre-service science teacher education. A four-phase research and development framework was employed, including conceptual synthesis, model design and expert validation, implementation, and evaluation. Participants were 19 second-year pre-service science teachers from a university in Bangkok. Research instruments included a 40-item AI competency assessment and an instructional innovation evaluation rubric. Paired-sample t-test results indicated statistically significant pre–post difference across all AI competency dimensions, with large effect sizes (Cohen’s d = 0.82–1.59), reflecting notable within-group changes observed within the EDL–DLE learning context. The instructional innovation lesson plans were evaluated as generally strong across multiple dimensions, particularly in learner-centered pedagogy, creativity, and collaboration, while relatively lower performance was observed in appropriate AI technology selection and ethical use. Overall, the findings provide preliminary evidence supporting the feasibility of the EDL–DLE model as an exploratory instructional approach for fostering foundational AI-related pedagogical competencies in pre-service science teacher education.