Generative AI in the Classroom: Ethical Frameworks for Student-AI Collaboration - Amir Zaki; Nong Chai; Vann Sok (Journal of Paddisengeng Technology, 2026)
Background. The rapid integration of generative artificial intelligence (AI) in educational settings has transformed student learning practices and introduced new forms of human–AI collaboration. Despite its potential to enhance engagement and expand access to knowledge, the use of AI raises critical ethical concerns related to academic integrity, authorship, and responsible use in academic contexts. Purpose. This study aims to develop an ethical framework to guide student–AI collaboration in classroom settings and to examine the relationship between AI usage, ethical awareness, and student learning outcomes. Method. A mixed-methods research design was employed, integrating quantitative and qualitative approaches. Survey data were collected from 212 participants to assess patterns of AI usage, ethical awareness, and perceived learning outcomes. In addition, interviews and focus group discussions were conducted to capture in-depth perspectives. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative data were examined through thematic coding techniques. Results. The findings reveal that AI usage significantly enhances student engagement; however, it does not automatically improve ethical awareness. Institutional guidance and clear policy frameworks emerge as critical factors influencing responsible AI use and the maintenance of academic integrity. The analysis further identifies key ethical dimensions underlying student–AI collaboration, including transparency, accountability, fairness, and critical engagement. Conclusion. The effective integration of generative AI in education requires the development of structured ethical frameworks that align technological practices with pedagogical objectives. The proposed framework offers practical guidance for educators and institutions in fostering responsible, ethical, and meaningful student–AI collaboration.