Designing Human-Centred AI Education for Future Professionals
Authors
Wentworth Institute of Higher Education (Australia)
Article Information
DOI: 10.47772/IJRISS.2026.1026EDU0428
Subject Category: Education
Volume/Issue: 10/26 | Page No: 5826-5842
Publication Timeline
Submitted: 2026-06-25
Accepted: 2026-06-30
Published: 2026-07-12
Abstract
Artificial Intelligence (AI) is transforming higher education and reshaping the capabilities graduates require for professional practice. As AI becomes increasingly embedded in workplaces, universities must move beyond teaching AI tools and instead develop curricula that foster AI literacy, critical thinking, ethical reasoning and effective human–AI collaboration. However, there is limited guidance on how these capabilities can be systematically embedded across higher education programs.
This paper proposes the Human-Centred AI Education Framework, comprising five developmental stages: AI Awareness, AI-Assisted Learning, Critical Evaluation, Human–AI Collaboration, and Professional Practice. The framework provides a structured approach for integrating AI into teaching, learning and assessment while maintaining the central role of human judgement, accountability and responsible AI use.
The framework is demonstrated through a postgraduate project management subject in which students used Generative AI to support authentic learning activities. Classroom observations indicate that structured AI integration promotes critical evaluation, reflective practice and professional judgement by encouraging students to evaluate rather than simply accept AI-generated outputs.
The paper contributes a practical curriculum framework that can be adapted across disciplines to prepare graduates for responsible participation in AI-enabled professional environments while preserving the human-centred capabilities essential for lifelong learning and professional practice.
Keywords
Artificial Intelligence, Higher Education, Curriculum Design, Human–AI Collaboration
Downloads
References
1. Australian Qualifications Framework Council. (2013). Australian Qualifications Framework (2nd ed.). Adelaide, SA, Australia: AQF Council. [Google Scholar] [Crossref]
2. Biggs, J., Tang, C., & Kennedy, G. (2022). Teaching for Quality Learning at University (5th ed.). Maidenhead, U.K.: Open University Press. [Google Scholar] [Crossref]
3. Boud, D., Keogh, R., & Walker, D. (Eds.). (1985). Reflection: Turning Experience into Learning. London, U.K.: Kogan Page. [Google Scholar] [Crossref]
4. Boud, D., & Falchikov, N. (Eds.). (2007). Rethinking Assessment in Higher Education: Learning for the Longer Term. London, U.K.: Routledge. [Google Scholar] [Crossref]
5. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating? Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148 [Google Scholar] [Crossref]
6. Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, Article 22. https://doi.org/10.1186/s41239-023-00392-8 [Google Scholar] [Crossref]
7. European Commission. (2017). European Framework for the Digital Competence of Educators (DigCompEdu). Luxembourg: Publications Office of the European Union. [Google Scholar] [Crossref]
8. International Organization for Standardization. (2023). ISO/IEC 42001:2023 Information Technology—Artificial Intelligence—Management System. Geneva, Switzerland: ISO. [Google Scholar] [Crossref]
9. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274 [Google Scholar] [Crossref]
10. Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, NJ, USA: Prentice-Hall. [Google Scholar] [Crossref]
11. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x [Google Scholar] [Crossref]
12. Mollick, E., & Mollick, L. (2024). Instructors as Innovators: A Future-Focused Approach to New AI Learning Opportunities, with Prompts. arXiv preprint arXiv:2407.05181. https://doi.org/10.48550/arXiv.2407.05181 [Google Scholar] [Crossref]
13. National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). Gaithersburg, MD, USA: NIST. [Google Scholar] [Crossref]
14. Organisation for Economic Co-operation and Development. (2019). OECD Learning Compass 2030: A Series of Concept Notes. Paris, France: OECD Publishing. [Google Scholar] [Crossref]
15. Organisation for Economic Co-operation and Development. (2026). Empowering Learners for the Age of AI. Paris, France: OECD Publishing. [Google Scholar] [Crossref]
16. Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic era. Journal of University Teaching and Learning Practice, 20(2), 1–12. https://doi.org/10.53761/1.20.02.07 [Google Scholar] [Crossref]
17. Project Management Institute. (2024). Pulse of the Profession® 2024: AI and Project Management. Newtown Square, PA, USA: Project Management Institute. [Google Scholar] [Crossref]
18. Schön, D. A. (1983). The Reflective Practitioner: How Professionals Think in Action. New York, NY, USA: Basic Books. [Google Scholar] [Crossref]
19. UNESCO. (2023). Guidance for Generative AI in Education and Research. Paris, France: United Nations Educational, Scientific and Cultural Organization. [Google Scholar] [Crossref]
20. Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, Article 124167. https://doi.org/10.1016/j.eswa.2024.124167 [Google Scholar] [Crossref]
21. Wiggins, G. (1998). Educative Assessment: Designing Assessments to Inform and Improve Student Performance. San Francisco, CA, USA: Jossey-Bass. [Google Scholar] [Crossref]
22. World Economic Forum. (2025). The Future of Jobs Report 2025. Geneva, Switzerland: World Economic Forum. [Google Scholar] [Crossref]
23. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0 [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
- Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
- “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
- Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
- Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study