Research Proposal

Development of a Proposed Ethical Framework for Practical Use and Misuse of Generative Artificial Intelligence (GenAI) in Education and Social Sciences Grounded in the Human-Centered AI (HCAI) Framework

by Almighty Cortezo Tabuena

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Research Overview

This dissertation, titled “Development of a Proposed Ethical Framework for Practical Use and Misuse of Generative Artificial Intelligence (GenAI) in Education and Social Sciences Grounded in the Human-Centered AI (HCAI) Framework,” examines the rapid integration of generative AI into academic environments and the urgent need for ethical governance. It highlights that while AI technologies such as large language models offer transformative benefits—like personalized learning and advanced data analysis—they also introduce serious risks, including data privacy violations, algorithmic bias, and the erosion of academic integrity. The study positions these challenges within the context of human-centered disciplines such as education, psychology, social work, and communication, where ethical considerations are particularly critical.

The research adopts a structured, multi-stage methodology that includes a systematic literature review, gap analysis, framework development, and case study validation. It identifies a key limitation in existing global AI ethics guidelines: they are often too general and fail to address the specific needs of social science disciplines. In response, the study proposes a Human-Centered AI (HCAI)-based ethical framework that emphasizes maintaining human control over AI systems. This framework treats AI as a “super-tool” that supports, rather than replaces, human judgment and decision-making.

A major contribution of the dissertation is the development of a practical ethical framework built on four core pillars: data governance and privacy protection, algorithmic bias mitigation, authorship transparency, and continuous professional adaptation. It introduces concrete mechanisms such as human-in-the-loop verification, cultural context audits, and AI disclosure requirements to ensure responsible use. The framework also distinguishes between acceptable AI assistance (e.g., technical support) and misuse (e.g., unverified content generation), thereby preserving cognitive agency and academic rigor.

The study further demonstrates the framework’s applicability through case-based scenarios across different disciplines, showing how AI can enhance teaching, research, and decision-making while maintaining ethical safeguards. It emphasizes that without structured governance, AI adoption may lead to deskilling, biased decision-making, and misuse of sensitive data. Ultimately, the dissertation concludes that a human-centered, participatory, and discipline-specific ethical framework is essential for ensuring that AI serves as a tool for empowerment rather than a source of harm in education and social sciences.