Ensuring Well-Being in the Age of Generative AI

About the Project

Generative AI is rapidly transforming diverse sectors, yet its implications for well-being remain underexplored. Beyond productivity gains, concerns are emerging around heightened performance expectations, reduced autonomy, and career uncertainty in AI-augmented environments. For example, in education, generative tools may diminish teachers’ professional judgment and empathy; in IT, coding assistants can reduce creative ownership and increase deadline pressure; and in healthcare, AI diagnostics may undermine clinicians’ trust in their expertise and accountability. Moreover, the unattributed reuse of open-source content by LLMs blurs authorship boundaries and devalues creative work, raising new ethical and emotional challenges for professionals.

Against this backdrop of emerging well-being risks, the broader organisational landscape reveals an accelerating race for adoption. For example, Amazon openly declare that “today, in virtually every corner of the company, we’re using Generative AI” (Andy Jassy, CEO, June 2025), setting aggressive benchmarks that ripple across industries. In contrast, smaller organisations often lack the resources, technical skills, and governance structures to implement AI responsibly. This imbalance risks widening capability gaps, with smaller players overextending limited capacity or relying on opaque third-party systems without adequate safeguards—further amplifying workplace stress and inequity.

This PhD project proposes the development of a human-centred, evidence-informed framework that identifies and mitigates risks to well-being arising from Generative-AI adoption. The aim is to map risk pathways, conceptualise well-being indicators, and articulate governance patterns that balance innovation with the protection of dignity, autonomy, and fairness. By moving beyond purely technical or efficiency-driven narratives, the project seeks to establish well-being assurance as a core principle in the future of AI-enabled work and society.

Academic qualifications

First degree (minimum 2:1 classification) in Computer Science

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 in fundamental software engineering
• Competent in one (or some) programming languages
• Knowledge of Machine learning,
• Good writing and communication skills

Desirable attributes:

• Knowledge of statistics

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about
  1. Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. List of references.
• The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.
• Statement no longer than 1 page describing your motivations and fit with the project.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use

• the advertised title as project title

For informal enquiries about this PhD project, please contact s.rafi@napier.ac.uk

Application Enquiries: https://www.napier.ac.uk/research-and-innovation/doctoral-college/application-guidance

Application link: https://evision.napier.ac.uk/si/sits.urd/run/siw_sso.go?ElOlarlItFiG37xnH5PRRBvv3d563wLdwX4JfhYskMa3bJWTuc