AI-supported training for immigrant labour market integration: a four-phase model for adult immigrants in Finland
Farzanehkari, Shabnam (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025121235293
https://urn.fi/URN:NBN:fi:amk-2025121235293
Tiivistelmä
This thesis investigates the potential of artificial intelligence (AI) to improve the integration of adult immigrants into the Finnish labour market. It is motivated by Finland’s demographic challenges—an ageing workforce and shortages in many sectors—and by the increased responsibilities placed on municipalities under the KOTO24 reform. The development task was to design and validate an AI-supported training model that municipalities can apply in integration services under KOTO24. The knowledge base draws from literature and practice on language learning, digital literacy, cultural adaptation and identity, and employment readiness.
The research design was exploratory and descriptive. Data were collected through a personalised Google Forms survey completed by 20 adult immigrants living in Finland. Participation was voluntary and anonymous, with secure data handling throughout.
The analysis revealed four recurring themes. Language learning was consistently identified as the greatest barrier to integration, with a strong interest in flexible AI tools that provide instant feedback and real-life practice. Digital literacy varied considerably: some participants managed digital platforms with confidence while others struggled with basic tasks, underlining the need for structured digital onboarding. Cultural adaptation and identity were closely linked to motivation and confidence; training should not only build skills but also support belonging and well-being. Employment readiness needs differed widely, ranging from help with job applications to workplace communication.
To translate these insights into action, the proposed model offers a practical roadmap: begin with digital onboarding to ensure access, create personalised learning tracks aligned with employment goals, combine AI tools with human mentoring to balance efficiency and empathy, and use simple feedback loops to monitor progress and refine services. This allows municipalities to pilot AI elements gradually with low upfront risk.
The study concludes that AI can enrich integration training through personalisation and flexible access, but human mentoring remains indispensable. Ethical concerns—privacy, bias and digital inequality—require transparent safeguards and co-development with stakeholders. While limited to 20 respondents, the results provide actionable insights and a framework ready for pilot testing.
The research design was exploratory and descriptive. Data were collected through a personalised Google Forms survey completed by 20 adult immigrants living in Finland. Participation was voluntary and anonymous, with secure data handling throughout.
The analysis revealed four recurring themes. Language learning was consistently identified as the greatest barrier to integration, with a strong interest in flexible AI tools that provide instant feedback and real-life practice. Digital literacy varied considerably: some participants managed digital platforms with confidence while others struggled with basic tasks, underlining the need for structured digital onboarding. Cultural adaptation and identity were closely linked to motivation and confidence; training should not only build skills but also support belonging and well-being. Employment readiness needs differed widely, ranging from help with job applications to workplace communication.
To translate these insights into action, the proposed model offers a practical roadmap: begin with digital onboarding to ensure access, create personalised learning tracks aligned with employment goals, combine AI tools with human mentoring to balance efficiency and empathy, and use simple feedback loops to monitor progress and refine services. This allows municipalities to pilot AI elements gradually with low upfront risk.
The study concludes that AI can enrich integration training through personalisation and flexible access, but human mentoring remains indispensable. Ethical concerns—privacy, bias and digital inequality—require transparent safeguards and co-development with stakeholders. While limited to 20 respondents, the results provide actionable insights and a framework ready for pilot testing.