Designing an AI governance framework for generative AI in higher education: Integrating large language models, academic integrity, employability, and national AI policies

dc.collaborationInternational Collaboration
dc.contributor.authorShahid, Choudhry
dc.contributor.authorTasavvar, Nishwa
dc.contributor.authorKhan, Atif
dc.contributor.authorAhmad, Sajjad
dc.contributor.authorBayram, Mustafa
dc.contributor.authorAlmomani, Omar
dc.contributor.authorAhmad, Sohail
dc.date.accessioned2026-09-23T10:14:14Z
dc.date.issued2026
dc.departmentİHÜ, Lisansüstü Eğitim Enstitüsü, Yeni Medya ve İletişim Ana Bilim Dalı
dc.description.abstractGenerative artificial intelligence (GenAI), particularly large language models (LLMs), is transforming higher education, impacting the way teachers teach, students learn, are evaluated, research is carried out and graduates are prepared. As it gains traction, however, it has raised complex challenges in terms of academic integrity, responsible use of AI, privacy, institutional accountability, employability and national priorities. This study proposed an AI governance framework for the use of generative AI in Higher Education Institutions (HEIs) in Pakistan and validated it. A design of a mixed methods framework-development and expert-validation was used. The study included a systematic review of literature, a comparative analysis of national and international AI policy, content-validity assessment, and a two-round Delphi consultation with 30 experts from the field of higher education administration, AI policy, educational technology, academic integrity, and industry. Content Validity Ratio, Item-Level ContentValidity Index, Scale-Level Content Validity Index, Delphi median and interquartile range, Kendall's coefficient of concordance, Friedman test and reliability analysis were used to validate the proposed governance dimensions. Qualitative interviews and Delphi responses were thematically analyzedto give insights into the quantitative results. The research results revealed five interconnected dimensions: Responsible integration of LLM, Academic integrity and assessment governance, AI literacy and graduate employability, Ethical governance and institutional governance, and Alignment with national AI policy. The study suggests a multi-layered governance system that places a strong focus on responsible innovation, human supervision, academic accountability, graduate readiness, institutional accountability, and ongoing policy adaptation in the Pakistani higher education system.
dc.identifier.citationShahid, C., Tasavvar, N., Khan, A., Ahmad, S., Bayram, M., Almomani, O., & Ahmad, S. (2026). Designing an AI governance framework for generative AI in higher education: Integrating large language models, academic integrity, employability, and national AI policies. Journal of Intelligent Decision Making and Information Science, 3(12s), 2203-2223.
dc.identifier.endpage2223
dc.identifier.issn3079-0875
dc.identifier.issue12s
dc.identifier.orcid0009-0006-9691-1088
dc.identifier.startpage2203
dc.identifier.urihttps://hdl.handle.net/20.500.12154/4132
dc.identifier.volume3
dc.institutionauthorTasavvar, Nishwa
dc.institutionauthorid0009-0006-9691-1088
dc.language.isoen
dc.publisherNexora Academic Press
dc.relation.ispartofJournal of Intelligent Decision Making and Information Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrenci
dc.relation.publicationcategoryÖğrenci
dc.relation.sdgGoal-04: Quality Education
dc.relation.sdgGoal-16: Peace, Justice and Strong Institutions
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectGenerative Artificial Intelligence (GenAI)
dc.subjectLarge Language Models (LLMs)
dc.subjectHigher Education
dc.subjectAcademic Integrity
dc.subjectuse of AI
dc.subjectInstitutional Accountability
dc.subjectAI Governance Framework
dc.titleDesigning an AI governance framework for generative AI in higher education: Integrating large language models, academic integrity, employability, and national AI policies
dc.typeArticle
dspace.entity.typePublication

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