Artificial Intelligence and Instructional Strategies in Science Education: Cultivating Industry-Relevant Innovative Competencies among Diverse Learners

Abstract

Higher education institutions are increasingly challenged to prepare graduates who can thrive in technology-driven industries characterized by rapid innovation, evolving workforce expectations, and continuous technological change. Responding to these demands requires science education to move beyond disciplinary knowledge toward cultivating the higher-order competencies necessary for innovation and professional adaptability. This study examined how science education in the Zamboanga Peninsula, an emerging region in the Philippines, contributes to the development of industry-relevant innovative competencies through the integration of artificial intelligence (AI) and instructional practices. Using a qualitative design, the study involved in-depth interviews with 20 college students enrolled in science-related programs. The interview data were interpreted through reflexive thematic analysis to examine how learners perceived the role of AI and classroom instruction in shaping their learning experiences and professional competencies. The findings indicate that innovative competencies emerge through the dynamic interaction among AI technologies, instructors, peers, and structured learning environments. Participants described meaningful improvements in creativity, analytical problem-solving, decision-making, and learner autonomy as they engaged with AI-generated guidance alongside teacher-facilitated instruction. Rather than serving merely as instructional aids, AI applications and educators jointly functioned as cognitive and procedural scaffolds by providing structured explanations, alternative perspectives, timely feedback, and opportunities for reflective learning. These interactions supported the gradual internalization of systematic reasoning and strengthened students' capacity for independent problem solving. Furthermore, the narratives suggest that AI-assisted learning and teacher-mediated instruction operate as complementary social learning processes in which observation, modeling, feedback, and sustained cognitive engagement collectively foster industry-relevant innovation competencies. These findings underscore the value of intentionally integrating AI within science education, not simply as a technological resource but as part of pedagogical practices that promote higher-order thinking, innovation, and workforce readiness among diverse learners.