INTEGRATION OF GENERATIVE AI IN HIGHER EDUCATION PEDAGOGY: A MIXED-METHODS STUDY OF INSTRUCTIONAL DESIGN ADAPTATION, STUDENT LEARNING OUTCOMES, AND ETHICAL GOVERNANCE IN INDONESIAN HIGHER EDUCATION
Keywords:
ChatGPT, Indonesian Higher Education, Ethical Governance, Learning Outcomes, Generative AIAbstract
The rapid spread of generative artificial intelligence (GenAI) tools, especially large language models such as ChatGPT, has disrupted the conventional pedagogical paradigm in higher education. Universities in Indonesia face a dual challenge: The aim of this study is to integrate technology into instructional design while addressing acute concerns related to academic integrity, digital equity, and institutional governance. This study uses a sequential explanatory mixed method design. Quantitative data was collected from 385 undergraduate students at five public universities in Indonesia using validated survey instruments and pre-test/post-test assessments. Qualitative data were then collected through semi-structured interviews with 24 lecturers, which were analyzed using reflective thematic analysis. Quantitative findings showed a statistically significant improvement in student learning outcomes (t(384) = 18.47, p < 0.001, Cohen's d = 1.47), with a concentrated improvement in high-level cognitive tasks. Lecturer interviews resulted in four main themes: pedagogical reconfiguration, strengthening student engagement, ethical uncertainty, and governance deficits. Seventy-one percent of institutions do not have a formal GenAI policy in place at the time of data collection. GenAI integration has been shown to improve learning outcomes when embedded in a structured pedagogical framework; However, its transformative potential is limited by inconsistent institutional governance and ongoing equality issues. This study proposes an evidence-based Adaptive GenAI Governance (AGG) Framework to guide policy development in Indonesia and comparable higher education contexts in developing countries.
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