Aug 2026· Lecture Notes in Education Psychology and Public Media· Vol 147, pp. 116-123· 0 citations
TL;DR
A Pedagogical Primacy Framework is proposed to guide educational decisions through four connected considerations: educational purpose, cognitive necessity, human agency, and accountability and supports teacher-governed use in basic education and transparent, reviewable use in higher education.
Abstract
Artificial intelligence is becoming part of everyday teaching, assessment, and knowledge production in schools and universities. Debate has concentrated on the functions of these systems, but the educational consequences always depend on the logic through which they operate. AI applications are commonly organized around optimization, automation, prediction, datafication, and scale. Education follows a different set of commitments, including developmental appropriateness, sustained cognitive effort, professional judgement, human interaction, and responsibility for knowledge. Using critical conceptual analysis and directed content analysis of international policy documents and academic literature, this paper examines how these logics interact in basic and higher education. The analysis identifies goal displacement as the central problem. Indicators that are easy to measure and optimize can gradually redefine what institutions treat as learning. The consequences differ across educational stages. In basic education, premature cognitive outsourcing may interrupt the formation of foundational capabilities and reduce meaningful interaction. In higher education, AI-assisted knowledge production raises concerns about verification, authorship, disciplinary judgement, and academic responsibility. A Pedagogical Primacy Framework is proposed to guide educational decisions through four connected considerations: educational purpose, cognitive necessity, human agency, and accountability. The framework supports teacher-governed use in basic education and transparent, reviewable use in higher education.
Artificial intelligence may influence teaching and university administration, but the two areas do not create value in the same way. Treating deployment or processing speed as evidence of educational improvement risks overlooking weak learning outcomes, additional review work, bias, privacy concerns and blurred responsibility. Drawing on policy documents and a narrative reading of higher-education research, this article develops a governance framework for local institutions. The framework begins with a shared foundation of data, platforms, staff capability and institutional rules, then separates a teaching pathway from a management pathway. Teaching is considered in terms of learning goals, feedback, differentiated support, student engagement and teachers' professional judgement. Administration is considered in terms of turnaround time, error and rework, access to services, transparency and the cost of appeal. The analysis proposes an evidence cycle that moves from problem definition and risk classification to a limited pilot, human review, multidimensional evaluation and a decision to scale, revise or stop. For institutions in Shenyang, the practical priorities are shared technical standards with controlled data access, role-specific AI literacy, explicit evidence thresholds and an auditable division of responsibility between people and systems.
Hu Nannan, Nooreen Noordin, Dr. Wong Siew Ping· International journal of inf...· 0 citations
Abstract - Artificial intelligence (AI) is increasingly influencing how universities manage people, information, resources, and institutional decisions. While research on AI in higher education has largely focused on teaching and learning, less attention has been given to the changing relationship between AI systems and administrative workforces. This paper examines how Human-AI collaboration is reshaping higher education administration across human resource management, admissions, finance, procurement, student services, research administration, and strategic planning. The study develops a conceptual framework that includes technological capability, human expertise, organizational readiness and responsible governance. The framework is based on socio-technical systems theory, human-AI complementarity theory, dynamic capabilities theory and the Technology Acceptance Model. This shows that AI is more interpretable as a means to augment administrative capabilities rather than as a direct substitute for human skills. But it’s not only about technology investment to get effective execution. This transformation depends on aligning employee competencies and supportive leadership with organizational trust in AI, administrative process reengineering, and sustained human accountability The study also highlights a number of challenges to AI adoption, including employee resistance, skills gaps, algorithmic bias, privacy concerns, reliance on external vendors and lack of clear accountability. Organizations that harness the analytical power of AI with human judgement and responsible governance are better placed to drive administrative innovation, enhance organizational agility and build long-term institutional resilience.
Key Words: Human–AI collaboration, higher education administration, workforce transformation, institutional innovation, responsible AI governance, digital transformation.
Hamsa K, Dr. Shreevamshi Naveen· International Scientific Jou...· 0 citations
The study conceptualizes artificial intelligence in higher education as a socio-technical system in which human cognition, institutional norms, and algorithmic processes interact. It aims to develop a model of functional differentiation that preserves students’ cognitive responsibility while enabling the safe and effective use of artificial intelligence. The study employs a mixed-methods design combining conceptual analysis with empirical data from student surveys conducted in higher education institutions in Georgia. The findings indicate that artificial intelligence enhances operational efficiency and supports academic tasks; however, its use for content generation is associated with reduced cognitive engagement and risks to knowledge reliability and academic integrity. The study concludes that clear functional boundaries, process-oriented assessment, AI literacy, and institutional governance are essential for responsible integration.
I. Didmanidze, Ia Khasaia, Nato Sherozia et al.· Challenges to national defen...· 0 citations
Artificial Intelligence (AI) is reshaping higher education through adaptive learning, intelligent tutoring, generative tools, learning analytics, research assistance and administrative automation. This structured narrative review synthesizes recent academic and institutional literature on the influence of AI on teaching, learning, research and university management. The evidence indicates meaningful potential for personalization, timely feedback, accessibility and operational efficiency, but also identifies risks involving inaccurate output, academic integrity, algorithmic bias, data privacy, unequal access and insufficient faculty preparedness. The review argues that AI should augment rather than replace educational judgment. Sustainable adoption requires clear governance, transparent assessment rules, faculty and student AI literacy, data protection, human oversight and regular evaluation of accuracy and equity. The paper also identifies the need for longitudinal research on learning outcomes and discipline-specific implementation.
Husna Sultana, Irfan Ahmed, Jeevan G· International journal of res...· 0 citations
This study synthesizes fragmented research on artificial intelligence (AI) in higher education governance and identifies key gaps for future research and policy and provides a useful lens for interpreting institutional adaptation.
Xinyi Jiang, Zuraidah Abdullah· Frontiers in Education· 3 citations
Artificial intelligence (AI) is accelerating the digital transformation of higher education and is changing the competencies expected of Business Administration graduates. This conceptual article develops the Artificial Intelligence-Based Business Administration Learning (AI-BAL) Framework to explain how AI can be integrated into Business Administration curricula without reducing education to technology adoption. The study uses a concept-driven integrative review and theory-synthesis approach. Literature on digital transformation, artificial intelligence in education, AI literacy, management education, human-AI augmentation, and responsible AI was examined and organized through iterative concept identification, categorization, relational mapping, and framework construction. The resulting framework connects four core layers: AI technologies, digital pedagogical transformation, Business Administration competencies, and graduate outcomes. These layers are shaped by external drivers, enabled by institutional leadership, faculty capability, infrastructure, policy, and industry collaboration, and governed by cross-cutting principles of human agency, transparency, fairness, privacy, accountability, inclusion, and academic integrity. The framework positions pedagogy as the mechanism that converts technological affordances into disciplinary competence and proposes a continuous evaluation loop for curriculum improvement. Six propositions specify testable relationships among AI adoption, pedagogy, competency development, institutional readiness, and graduate outcomes. The article contributes a discipline-specific and empirically testable model for curriculum redesign, faculty development, governance, and future research in Business Administration education.
Eka Annisa Zulqaidah, Kadaruddin Kadaruddin· International Journal of Bus...· 0 citations
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