Developing the Triarchic Responsibility Model (TRM-AI) for Human–AI Collaboration in Higher Education: A Comparative Analysis of Faculty and Students' Perspectives
Keywords:
Artificial Intelligence; Assistive Intelligent Tool; Higher Education; Human-AI Collaboration; Triarchic Responsibility Model for AI Integration (TRM-AI)Abstract
Increasing presence of AI in education opens new avenues for Human-AI collaboration. However, it raises significant questions regarding cognitive efficiency, productivity, and academic ethics. This study examines and compares the perceptions and experiences of Human-AI collaboration in academic contexts between faculty members and higher education students. The study employed a comparative mixed-methods design, surveying 100 faculty members and 533 students at public higher education institutions. Quantitative results show that both groups view AI as a helpful, collaborative tool for improving cognitive processes and enhancing efficiency and productivity by saving time and effort in information processing, content creation, task completion, and idea generation. However, students were more positive about AI's impact on efficiency (M = 2.98) and productivity (M = 2.93), whereas faculty expressed greater concern regarding ethical issues (M = 3.48), including proper citation, transparency, accountability, and responsible use in teaching and learning. Qualitatively, students perceived Human-AI collaboration primarily as cognitive support to boost academic achievement, while the faculty emphasized critical thinking, originality, and academic integrity. Both groups cautiously agreed that AI can improve educational experiences if implemented correctly, demonstrating that strong Human-AI cooperation boosts efficiency and productivity provided there are ethical precautions and guaranteed responsible use. These findings support the TRM-AI framework, which emphasizes joint responsibility among students, instructors, and institutions to promote sustainable AI integration. While focused on Philippine public higher education institutions, the study contributes to global discussions on responsible AI integration and offers evidence-based recommendations for fair, ethical, and sustainable AI policy and practice.
https://doi.org/10.26803/ijlter.25.9.14
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