About the Project
An interdisciplinary foresight stream is seeking doctoral researchers to investigate avenues of human-AI interaction (HAII). The stream are part of a larger UKRI Metascience AI Fellowship that is investigating how cognition is affected when using AI technologies as decision-support systems. The project is hosted by the Futures & Analytics Research (FAR) Hub and Centre for Business Innovations and Sustainable Solutions (CBISS) within Edinburgh Napier Business School.
PhD – The future of AI organisational beneficence
Organisations integrating AI systems over time is a certain, the extent to which these integrations could benefit the business ecosystem is uncertain. “Benefits” are framed as capabilities that expand human skillsets, rather than replace human workforces. In this programme, you will use foresight methods (e.g. scenario planning, OR, causal layered analysis, speculative design) to investigate how AI technologies (e.g. generative, predictive, agentic, causal) could transform roles, sectors, and relationships within a business ecosystem, i.e. “The Intelligent Organisation”. A guiding question in this project is, How can HAII be beneficent, thereby avoiding techno-dominance to maximize techno-relevance? You will explore methods for evidencing HAII impact and develop targeted industry-ready models for integrations that identify crucial issues and vulnerabilities into the future, and challenge assumptions, e.g. socio-cultural expectations.
What to expect
This programme is ideal for someone who wants to explore innovative trend analyses, predictive analytics, progression modelling, foresight mixed-methods, bibliometric analysis, qualitative comparative analysis, ML and more. There is scope to develop your own focus and to link into targeted and relevant UN Sustainable Development Goals.
We welcome targeted, focused, and well-developed research proposals that address this evolving area. Applicants should demonstrate a clear understanding of the role of AI in shaping our plausible futures and should set out a coherent and feasible plan for investigating this topic within the UK and/or Scottish context. Proposals should be detailed enough to show originality, methodological rigour, and clear alignment with the project’s themes.
Willing to accept full-time and part-time applicants.
Publications
This programme emphasizes sharing your discoveries through publications (e.g. academic journals, books and conferences) and with non-academic audiences (e.g. industry events, podcasts, and media). Science communication is a core value of the University, and your supervisors and department will support you through these outputs.
Academic qualifications
A first degree (at least a 2.1) ideally in behavioural sciences, supply management, or related, with a good fundamental knowledge of experimental methods, statistical analyses and behavioural sciences.
English language requirement
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.
Essential attributes:
- Experience of fundamental quantitative research methods, human-based testing, secondary data methods, organisational behaviour theory, LLM, NLP, or related machine learning context.
- Competent in mixed-methods approaches and online recruitment platforms.
- Knowledge of decision theory, multiple intelligences, scenario planning or foresight theories.
- Good written and oral communication skills
- Strong motivation, with evidence of independent research skills relevant to the project
- Good time management
Desirable attributes:
Applications are especially welcomed from those with experience in decolonizing scientific knowledge and methods, business ethics, building dashboards, social media applications, philosophy of artificial intelligence, and machine learning ethics.
Funding Notes
This is an unfunded position
References
Indicative Bibliography
- Bourgeois, R., Karuri-Sebina, G. and Feukeu, K.E. (2024). The future as a public good: decolonising the future through anticipatory participatory action research, Foresight, 26(4), pp. 533-549. https://doi.org/10.1108/FS-11-2021-0225
- Crawford, M. M. (2019). A comprehensive scenario intervention typology. Technological Forecasting And Social Change, 149, 119748. https://doi.org/10.1016/j.techfore.2019.119748
- Crawford, M. M. & Wright, G. (Eds.). (2025). Improving and Enhancing Scenario Planning: Futures Thinking. Edward Elgar. https://www.e-elgar.com/shop/gbp/improving-and-enhancing-scenario-planning-9781035310579.html
- Costanzo, L. A., & MacKay, R. B. (Eds.). (2009). Handbook Of Research On Strategy And Foresight. Edward Elgar Publishing. https://www.e-elgar.com/shop/gbp/handbook-of-research-on-strategy-and-foresight-9781845429638.html
- Bradfield. (20205). Understanding the Future: An Introduction to Scenario Planning. De Gruyter. https://www.degruyterbrill.com/document/doi/10.1515/9783111617442/html
- Goodwin, P., & Wright, G. (2014). Decision Analysis for Management Judgment. John Wiley & Sons. https://pureportal.strath.ac.uk/en/publications/decision-analysis-for-management-judgment-5th-ed
- Juárez Ramos, V. (Ed.). (2018). Analyzing the role of cognitive biases in the decision-making process. IGI Global. https://www.igi-global.com/book/analyzing-role-cognitive-biases-decision/179223
- Scottish Government. (2026, March 20). Scotland’s artificial intelligence strategy 2026–2031. https://www.gov.scot/publications/scotlands-ai-strategy-2026-2031
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