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Human-centred artificial intelligence in progressive education: unravelling the benefits and challenges in Qatar’s HEIs

Bernard J. Jansen*, Soon-gyo Jung, Ali Farooq, Joni Salminen, Kholoud Aldous, Pilira Stella Msefula, Amani Alabed, Salar M. Khan, Richard O'Kennedy

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

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Abstract

Algorithmically generated personas (AGPs) can provide actionable insights into the needs, preferences, and behaviours of stakeholders, including Applicants, Students, Alumni, Faculty, and Staff (ASAFS) for progressive education at higher education institutes (HEIs) in Qatar. AGPs empower data-based decision-making, and contribute to progressive education by representing stakeholders in an empathic format. This chapter sheds light on the pivotal role of alumni, showing how the innovative approach of using personas enables the “beyond education” concept, while providing strategic foresight for HEIs in Qatar. Machine learning models can generate AGPs as humanised representations of these key stakeholders. Building on our multi-year experience in studying AGPs, we argue that these personas can significantly aid HEI decision-makers in designing more learner-centred and inclusive curricula, pedagogy, and assessment via empathic understanding. While AGPs have been prevalent in marketing or design, their potential remains largely unexplored in HEIs.
Original languageEnglish
Title of host publicationThe Future of Educaiton Policy in the State of Qatar
EditorsEsmat Zaidan, Evren Tok, Asmaa Al-Fadala, Logan Cochrane
Place of PublicationSingapore
PublisherSpringer
Chapter6
Pages105-126
Number of pages22
ISBN (Electronic)9789819796670
ISBN (Print)9789819796663, 9789819796694
DOIs
Publication statusPublished - 12 Apr 2025

Publication series

NameGulf Studies
Volume21
ISSN (Print)2662-4494
ISSN (Electronic)2662-4508

Funding

Qatar Foundation

Keywords

  • algorithmically generated personas
  • progressive education
  • Qatar
  • alumni
  • HEIs
  • machine learning models
  • higher education

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