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This study examines the returns from education for three distinct groups: always employees, dabblers in self-employment and sustained self-employed individuals. We aim to understand how educational attainment translates into earnings across these employment types in the UK.

Using data from the British Household Panel Survey (BHPS) and Understanding Society: The UK Household Longitudinal Study (UKHLS), we employ a random effects (RE) model to account for unobserved individual characteristics and the Heckman selection model to address self-selection bias, ensuring accurate estimates of educational returns.

Our findings indicate that sustained self-employed individuals benefit more from education compared to dabblers and, in certain cases, traditional employees. Dabblers with postgraduate education report higher returns than always employees, but those with lower educational levels experience disadvantages due to their intermittent labour market engagement.

This study introduces new evidence on the heterogeneity of educational returns for self-employed individuals in the UK, providing a novel comparative analysis of different employment types and highlighting the unique challenges and outcomes related to educational attainment and earnings.

This study aims to examine the transitions of workers between paid employment and self-employment before and after the 2008 financial crisis, focusing on the drivers behind increased self-employment in the UK and the role of trade union membership in these transitions.

Using a long-running panel data set, the labour market is divided into three groups: sustained self-employed, always employed and those who transition between the two. The framework assesses the influence of union membership on these transitions.

Long-term paid employees, rather than those transitioning between employment types, are driving increased self-employment. Union membership appears more attractive post-crisis to paid employees, but disengagement from unions may be linked to transitions towards self-employment.

This research highlights the nuanced role of trade unions in employment transitions and contributes to understanding labour market dynamics post-financial crisis in the UK.

This study aims to explore how artificial intelligence (AI) tools influence the academic success and adaptation of international students in higher education. It examines the benefits, challenges and ethical considerations including academic integrity of integrating AI in learning environments.

An exploratory qualitative research approach was employed, utilising semi-structured interviews with postgraduate international students from diverse backgrounds.

The findings suggest that AI tools enhance academic performance by offering personalised learning, immediate feedback and efficient assessment. However, concerns about ethical use, over-reliance and the potential impact on critical thinking and academic integrity were prominent in the contexts of assessments and learning experiences.

The research offers unique insights by focusing on postgraduate international students, an often-underrepresented group in AI education studies. Their distinctive challenges, including adapting to new academic environments and overcoming language barriers, make them a particularly valuable sample for understanding the role of AI in higher education. This focus allows the study to contribute new perspectives on how generative AI (GenAI) tools like Grammarly and ChatGPT facilitate academic performance improvement, especially in enhancing writing proficiency and managing academic expectations. These findings extend the discussion by specifically addressing the experiences of international students in postgraduate studies, a demographic where AI’s impact has been less explored.