 What is Ethical: AIHED Driving Humans or Human-Driven AIHED? A Conceptual Framework enabling the 'Ethos' of AI-driven Higher EducationPrashant Mahajan ()
Working Papers from HAL Abstract: Artificial Intelligence (AI) is transforming higher education (HE) by enabling personalized learning, automating administrative processes, and enhancing decision-making. However, AI adoption presents significant ethical and institutional challenges, including algorithmic bias, data privacy concerns, and governance inconsistencies. This study introduces the Human-Driven AI in Higher Education (HD-AIHED) Framework, an adaptive and structured model designed to integrate human intelligence (HI) into every phase of the AI lifecycle-adoption, design, deployment, evaluation, and exploration. Unlike conventional AI models that prioritize automation, HD-AIHED emphasizes human-centered governance, ethical compliance, and participatory decision-making to ensure that AI enhances rather than replaces human agency in HE. The framework aligns AI applications with both institutional and student needs, fostering trust, adaptability, and transparency. Its dual-layered approach integrates AI across both the AI lifecycle and the student lifecycle, ensuring context-sensitive, equitable, and goal-oriented AI implementation. A key contribution of this study is its regionally adaptable approach, recognizing variations in technological infrastructure and policy landscapes. Additionally, the integration of SWOC (Strengths, Weaknesses, Opportunities, and Challenges) analysis during the adoption phase allows HE institutions to evaluate AI readiness, mitigate risks, and refine governance structures, while the exploration phase ensures long-term adaptability, scalability, sustainability, and AI promotion through continuous research, innovation, and interdisciplinary collaboration. To ensure AI remains a responsible enabler in HE, this study advocates for the establishment of University/HE Institutional AI Ethical Review Boards, alignment with global regulatory frameworks (e.g., UNESCO AI Ethics Guidelines, GDPR, Sustainable Development Goal 4), and the promotion of inclusive and transparent AI adoption policies. Key insights from the HD-AIHED model highlight its role in bridging AI research gaps and overcoming real-time challenges in global HE institutions. The framework offers tailored strategies for diverse educational contexts, including developed and emerging countries, ensuring that AI implementation is contextually relevant and ethically sound. By emphasizing interdisciplinary collaboration among policymakers, educators, industry leaders, and students, this study envisions AI as an ethical and equitable force for innovation in HE. Ultimately, the HD-AIHED model serves as a catalyst for AI inclusivity rather than exclusion and a driver of educational equity rather than disparity by embedding the ethos of AIHED into higher education systems. Future research should focus on the empirical validation of HD-AIHED through institutional case studies, AI bias audits, and longitudinal assessments to ensure ethical integrity, transparency, and sustainability in AI deployment. Keywords: Artificial Intelligence; Higher Education; AI Lifecycle; Educational Ecosystem; Ethics in Education; AI in higher Education; Human Intelligence; Educational Technology Implementation; Technology Adoption (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-04982990 Access Statistics for this paper More papers in Working Papers from HAL |
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