AI–Human Intelligence Synergy and Student Voice in Health Sciences Education: A Conceptual Framework
Keywords:
AI in education; TPACK; cognitive load theory; student voice; pedagogical intelligence; health sciences educationAbstract
The rapid adoption of artificial intelligence (AI) in health sciences education is often driven by technological capability rather than pedagogical purpose, raising concerns about automation, cognitive offloading, and the erosion of professional judgement. This conceptual paper argues that meaningful AI integration requires a shift from tool-oriented implementation to a pedagogically grounded model of artificial–human intelligence synergy. The author adopted a conceptual qualitative methodology grounded in interpretivist and critical pedagogical traditions. Drawing on cognitive load theory, experiential learning theory, and the Technological Pedagogical Content Knowledge framework, the paper conceptualises AI use as pedagogical alignment, ethical responsibility, and the distribution of cognitive agency. The paper introduces Student Voice as Pedagogical Intelligence as a novel theoretical construct that positions students’ lived learning experiences as an epistemic resource for guiding curriculum design, ethical boundaries, and decisions about appropriate AI use. Building on this conceptualisation, an AI–HI synergy framework is proposed that clarifies the complementary roles of students, educators, and AI systems in supporting professional learning outcomes, including critical judgement, reflective practice, identity formation, and agency. By highlighting pedagogy over technological optimisation, the paper contributes a theoretically grounded framework for understanding AI integration in health sciences education. While the paper does not present empirical data, it offers a structured theoretical model to guide future research and practice. The framework offers conceptual and practical guidance for educators and institutions seeking to integrate AI in ways that protect human judgement, support deep learning, and align technological innovation with the normative purposes of professional education.
https://doi.org/10.26803/ijlter.25.4.26
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