Ethics of AI Tutors: Balancing Automation and Human Interaction in Education - Ali Mufron; Ethan Tan; Ava Lee (Journal Emerging Technologies in Education, 2025)
Background. Artificial intelligence tutors are increasingly adopted in education to enhance efficiency, personalization, and accessibility. Despite their widespread use, ethical concerns related to reduced human interaction, moral responsibility, and learner well-being remain insufficiently addressed. Purpose. This study aims to examine the ethical implications of AI tutors by analyzing how automation can be balanced with meaningful human interaction in educational settings. Method. The study employs a qualitative descriptive approach using secondary statistical data, document analysis, and case study examination. Data are analyzed thematically to identify ethical patterns related to automation, human roles, and institutional governance. Results. The findings indicate that AI tutors effectively support structured learning activities but are limited in relational and emotional engagement. Educators perceive AI tutors as supplementary tools, while ethical responsibility is often managed informally due to the absence of explicit institutional guidelines. Conclusion. The study concludes that ethical integration of AI tutors requires intentional balance, where automation enhances efficiency and human interaction preserves educational values. The novelty of this research lies in its integration of ethical theory and educational practice to conceptualize AI tutors as complementary rather than substitutive agents within human-centered learning environments.